{"id":52873,"url":"https://github.com/dylanhogg/awesome-python","name":"awesome-python","description":"🐍 Hand-picked awesome Python libraries and frameworks, organised by category","projects_count":1009,"last_synced_at":"2026-04-07T02:00:22.905Z","repository":{"id":37250582,"uuid":"273678934","full_name":"dylanhogg/awesome-python","owner":"dylanhogg","description":"🐍 Hand-picked awesome Python libraries and frameworks, organised by category","archived":false,"fork":false,"pushed_at":"2026-02-11T01:37:59.000Z","size":98945,"stargazers_count":447,"open_issues_count":12,"forks_count":39,"subscribers_count":14,"default_branch":"main","last_synced_at":"2026-03-24T12:45:55.697Z","etag":null,"topics":["awesome","awesome-list","awesome-python","chatgpt","data","data-science","deep-learning","jupyter","machine-learning","natural-language-processing","nlp","open-source","pandas","python","python-library"],"latest_commit_sha":null,"homepage":"https://www.awesomepython.org","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dylanhogg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2020-06-20T09:36:39.000Z","updated_at":"2026-03-16T02:33:23.000Z","dependencies_parsed_at":"2023-10-04T09:32:32.936Z","dependency_job_id":"6da9f544-a1b2-42fb-9cf6-3d634f490324","html_url":"https://github.com/dylanhogg/awesome-python","commit_stats":null,"previous_names":["dylanhogg/crazy-awesome-python"],"tags_count":12,"template":false,"template_full_name":null,"purl":"pkg:github/dylanhogg/awesome-python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dylanhogg%2Fawesome-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dylanhogg%2Fawesome-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dylanhogg%2Fawesome-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dylanhogg%2Fawesome-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dylanhogg","download_url":"https://codeload.github.com/dylanhogg/awesome-python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dylanhogg%2Fawesome-python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31496769,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-06T17:22:55.647Z","status":"online","status_checked_at":"2026-04-07T02:00:07.164Z","response_time":105,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"readme":"# Awesome Python  \n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)  ![Last commit](https://img.shields.io/github/last-commit/dylanhogg/awesome-python)  [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)  \n\nHand-picked awesome Python libraries and frameworks, organised by category 🐍  \n\nInteractive version: [www.awesomepython.org](https://www.awesomepython.org/)  \n  \n\u003cimg src='https://www.awesomepython.org/img/media/github-repo-banner.jpg' /\u003e  \n\nUpdated 11 Feb 2026 with 1,553 repos\n\n## Categories\n\n- [Newly Created Repositories](#newly-created-repositories) - Awesome Python is regularly updated, and this category lists the most recently created GitHub repositories from all the other repositories here (10 repos)\n- [Agentic AI](#agentic-ai) - Agentic AI libraries, frameworks and tools: AI agents, workflows, autonomous decision-making, goal-oriented tasks, and API integrations (109 repos)\n- [Code Quality](#code-quality) - Code quality tooling: linters, formatters, pre-commit hooks, unused code removal (14 repos)\n- [Crypto and Blockchain](#crypto-and-blockchain) - Cryptocurrency and blockchain libraries: trading bots, API integration, Ethereum virtual machine, solidity (10 repos)\n- [Data](#data) - General data libraries: data processing, serialisation, formats, databases, SQL, connectors, web crawlers, data generation/augmentation/checks (81 repos)\n- [Debugging](#debugging) - Debugging and tracing tools (3 repos)\n- [Diffusion Text to Image](#diffusion-text-to-image) - Text-to-image diffusion model libraries, tools and apps for generating images from natural language (40 repos)\n- [Finance](#finance) - Financial and quantitative libraries: investment research tools, market data, algorithmic trading, backtesting, financial derivatives (27 repos)\n- [Game Development](#game-development) - Game development tools, engines and libraries (6 repos)\n- [GIS](#gis) - Geospatial libraries: raster and vector data formats, interactive mapping and visualisation, computing frameworks for processing images, projections (17 repos)\n- [Graph](#graph) - Graphs and network libraries: network analysis, graph machine learning, visualisation (4 repos)\n- [GUI](#gui) - Graphical user interface libraries and toolkits (6 repos)\n- [Jupyter](#jupyter) - Jupyter and JupyterLab and Notebook tools, libraries and plugins (16 repos)\n- [LLMs and ChatGPT](#llms-and-chatgpt) - Large language model and GPT libraries and frameworks: auto-gpt, agents, QnA, chain-of-thought workflows, API integations. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#natural-language-processing\"\u003eNatural Language Processing\u003c/a\u003e category for crossover (348 repos)\n- [Math and Science](#math-and-science) - Mathematical, numerical and scientific libraries (22 repos)\n- [Machine Learning - General](#machine-learning---general) - General and classical machine learning libraries. See below for other sections covering specialised ML areas (143 repos)\n- [Machine Learning - Deep Learning](#machine-learning---deep-learning) - Machine learning libraries that cross over with deep learning in some way (67 repos)\n- [Machine Learning - Interpretability](#machine-learning---interpretability) - Machine learning interpretability libraries. Covers explainability, prediction explainations, dashboards, understanding knowledge development in training (21 repos)\n- [Machine Learning - Ops](#machine-learning---ops) - MLOps tools, frameworks and libraries: intersection of machine learning, data engineering and DevOps; deployment, health, diagnostics and governance of ML models (48 repos)\n- [Machine Learning - Reinforcement](#machine-learning---reinforcement) - Machine learning libraries and toolkits that cross over with reinforcement learning in some way: agent reinforcement learning, agent environemnts, RLHF (22 repos)\n- [Machine Learning - Time Series](#machine-learning---time-series) - Machine learning and classical timeseries libraries: forecasting, seasonality, anomaly detection, econometrics (17 repos)\n- [Natural Language Processing](#natural-language-processing) - Natural language processing libraries and toolkits: text processing, topic modelling, tokenisers, chatbots. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#llms-and-chatgpt\"\u003eLLMs and ChatGPT\u003c/a\u003e category for crossover (72 repos)\n- [Packaging](#packaging) - Python packaging, dependency management and bundling (21 repos)\n- [Pandas](#pandas) - Pandas and dataframe libraries: data analysis, statistical reporting, pandas GUIs, pandas performance optimisations (18 repos)\n- [Performance](#performance) - Performance, parallelisation and low level libraries (19 repos)\n- [Profiling](#profiling) - Memory and CPU/GPU profiling tools and libraries (8 repos)\n- [Security](#security) - Security related libraries: vulnerability discovery, SQL injection, environment auditing (12 repos)\n- [Simulation](#simulation) - Simulation libraries: robotics, economic, agent-based, traffic, physics, astronomy, chemistry, quantum simulation. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#math-and-science\"\u003eMaths and Science\u003c/a\u003e category for crossover (35 repos)\n- [Study](#study) - Miscellaneous study resources: algorithms, general resources, system design, code repos for textbooks, best practices, tutorials (63 repos)\n- [Template](#template) - Template tools and libraries: cookiecutter repos, generators, quick-starts (9 repos)\n- [Terminal](#terminal) - Terminal and console tools and libraries: CLI tools, terminal based formatters, progress bars (18 repos)\n- [Testing](#testing) - Testing libraries: unit testing, load testing, acceptance testing, code coverage, browser automation, plugins (15 repos)\n- [Typing](#typing) - Typing libraries: static and run-time type checking, annotations (14 repos)\n- [Utility](#utility) - General utility libraries: miscellaneous tools, linters, code formatters, version management, package tools, documentation tools (140 repos)\n- [Vizualisation](#vizualisation) - Vizualisation tools and libraries. Application frameworks, 2D/3D plotting, dashboards, WebGL (30 repos)\n- [Web](#web) - Web related frameworks and libraries: webapp servers, WSGI, ASGI, asyncio, HTTP, REST, user management (48 repos)\n\n\n## Newly Created Repositories\n\nAwesome Python is regularly updated, and this category lists the most recently created GitHub repositories from all the other repositories here.  \n\n1. \u003ca href=\"https://github.com/affaan-m/everything-claude-code\"\u003eaffaan-m/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/affaan-m/everything-claude-code\"\u003eeverything-claude-code\u003c/a\u003e\u003c/b\u003e ⭐ 23,150    \n   Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner.  \n\n2. \u003ca href=\"https://github.com/karpathy/llm-council\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/llm-council\"\u003ellm-council\u003c/a\u003e\u003c/b\u003e ⭐ 13,742    \n   LLM Council works together to answer your hardest questions  \n\n3. \u003ca href=\"https://github.com/originalankur/maptoposter\"\u003eoriginalankur/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/originalankur/maptoposter\"\u003emaptoposter\u003c/a\u003e\u003c/b\u003e ⭐ 7,190    \n   Transform your favorite cities into beautiful, minimalist designs. MapToPoster lets you create and export visually striking map posters with code.  \n\n4. \u003ca href=\"https://github.com/anthropics/knowledge-work-plugins\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/knowledge-work-plugins\"\u003eknowledge-work-plugins\u003c/a\u003e\u003c/b\u003e ⭐ 6,910    \n   Knowledge Work Plugins that turn Claude into a specialist for your role, team, and company  \n\n5. \u003ca href=\"https://github.com/deepseek-ai/engram\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/engram\"\u003eEngram\u003c/a\u003e\u003c/b\u003e ⭐ 3,252    \n   Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models.  \n\n6. \u003ca href=\"https://github.com/aiming-lab/simplemem\"\u003eaiming-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aiming-lab/simplemem\"\u003eSimpleMem\u003c/a\u003e\u003c/b\u003e ⭐ 1,871    \n   SimpleMem addresses the fundamental challenge of efficient long-term memory for LLM agents through a three-stage pipeline grounded in Semantic Lossless Compression.  \n\n7. \u003ca href=\"https://github.com/1rgs/nanocode\"\u003e1rgs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/1rgs/nanocode\"\u003enanocode\u003c/a\u003e\u003c/b\u003e ⭐ 1,669    \n   Minimal Claude Code alternative. Single Python file, zero dependencies, ~250 lines.  \n\n8. \u003ca href=\"https://github.com/alexzhang13/rlm\"\u003ealexzhang13/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alexzhang13/rlm\"\u003erlm\u003c/a\u003e\u003c/b\u003e ⭐ 1,668    \n   Recursive Language Models (RLMs) are a task-agnostic inference paradigm for language models (LMs) to handle near-infinite length contexts  \n   🔗 [arxiv.org/abs/2512.24601v1](https://arxiv.org/abs/2512.24601v1)  \n\n9. \u003ca href=\"https://github.com/agno-agi/dash\"\u003eagno-agi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agno-agi/dash\"\u003edash\u003c/a\u003e\u003c/b\u003e ⭐ 1,598    \n   Self-learning data agent that grounds its answers in 6 layers of context. Inspired by OpenAI's in-house implementation.  \n\n10. \u003ca href=\"https://github.com/open-tinker/opentinker\"\u003eopen-tinker/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-tinker/opentinker\"\u003eOpenTinker\u003c/a\u003e\u003c/b\u003e ⭐ 598    \n   OpenTinker is an RL-as-a-Service infrastructure for foundation models, providing a flexible environment design framework that supports diverse training scenarios over data and interaction modes.  \n\n## Agentic AI\n\nAgentic AI libraries, frameworks and tools: AI agents, workflows, autonomous decision-making, goal-oriented tasks, and API integrations.  \n\n1. \u003ca href=\"https://github.com/logspace-ai/langflow\"\u003elogspace-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/logspace-ai/langflow\"\u003elangflow\u003c/a\u003e\u003c/b\u003e ⭐ 144,166    \n   Langflow is a powerful tool for building and deploying AI-powered agents and workflows.  \n   🔗 [www.langflow.org](http://www.langflow.org)  \n\n2. \u003ca href=\"https://github.com/langgenius/dify\"\u003elanggenius/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langgenius/dify\"\u003edify\u003c/a\u003e\u003c/b\u003e ⭐ 127,042    \n   Production-ready platform for agentic workflow development.  \n   🔗 [dify.ai](https://dify.ai)  \n\n3. \u003ca href=\"https://github.com/langchain-ai/langchain\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/langchain\"\u003elangchain\u003c/a\u003e\u003c/b\u003e ⭐ 124,984    \n   🦜🔗 The platform for reliable agents.  \n   🔗 [docs.langchain.com/oss/python/langchain](https://docs.langchain.com/oss/python/langchain/)  \n\n4. \u003ca href=\"https://github.com/browser-use/browser-use\"\u003ebrowser-use/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/browser-use/browser-use\"\u003ebrowser-use\u003c/a\u003e\u003c/b\u003e ⭐ 76,464    \n   Browser use is the easiest way to connect your AI agents with the browser.  \n   🔗 [browser-use.com](https://browser-use.com)  \n\n5. \u003ca href=\"https://github.com/github/spec-kit\"\u003egithub/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/github/spec-kit\"\u003espec-kit\u003c/a\u003e\u003c/b\u003e ⭐ 64,735    \n   Toolkit to help you get started with Spec-Driven Development: specifications become executable, directly generating working implementations  \n\n6. \u003ca href=\"https://github.com/geekan/metagpt\"\u003egeekan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/geekan/metagpt\"\u003eMetaGPT\u003c/a\u003e\u003c/b\u003e ⭐ 63,365    \n   🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming  \n   🔗 [mgx.dev](https://mgx.dev/)  \n\n7. \u003ca href=\"https://github.com/microsoft/autogen\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/autogen\"\u003eautogen\u003c/a\u003e\u003c/b\u003e ⭐ 53,832    \n   AutoGen is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.  \n   🔗 [microsoft.github.io/autogen](https://microsoft.github.io/autogen/)  \n\n8. \u003ca href=\"https://github.com/run-llama/llama_index\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/llama_index\"\u003ellama_index\u003c/a\u003e\u003c/b\u003e ⭐ 46,554    \n   LlamaIndex is the leading framework for building LLM-powered agents over your data.  \n   🔗 [developers.llamaindex.ai](https://developers.llamaindex.ai)  \n\n9. \u003ca href=\"https://github.com/mem0ai/mem0\"\u003emem0ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mem0ai/mem0\"\u003emem0\u003c/a\u003e\u003c/b\u003e ⭐ 45,890    \n   Enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions  \n   🔗 [mem0.ai](https://mem0.ai)  \n\n10. \u003ca href=\"https://github.com/crewaiinc/crewai\"\u003ecrewaiinc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/crewaiinc/crewai\"\u003ecrewAI\u003c/a\u003e\u003c/b\u003e ⭐ 43,083    \n   Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.  \n   🔗 [crewai.com](https://crewai.com)  \n\n11. \u003ca href=\"https://github.com/agno-agi/agno\"\u003eagno-agi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agno-agi/agno\"\u003eagno\u003c/a\u003e\u003c/b\u003e ⭐ 37,137    \n   Build, run, manage multi-agent systems.  \n   🔗 [docs.agno.com](https://docs.agno.com)  \n\n12. \u003ca href=\"https://github.com/openbmb/chatdev\"\u003eopenbmb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openbmb/chatdev\"\u003eChatDev\u003c/a\u003e\u003c/b\u003e ⭐ 28,989    \n   ChatDev stands as a virtual software company that operates through various intelligent agents holding different roles, including Chief Executive Officer, Chief Product Officer etc  \n   🔗 [arxiv.org/abs/2307.07924](https://arxiv.org/abs/2307.07924)  \n\n13. \u003ca href=\"https://github.com/stanford-oval/storm\"\u003estanford-oval/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stanford-oval/storm\"\u003estorm\u003c/a\u003e\u003c/b\u003e ⭐ 27,814    \n   An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.  \n   🔗 [storm.genie.stanford.edu](http://storm.genie.stanford.edu)  \n\n14. \u003ca href=\"https://github.com/composiohq/composio\"\u003ecomposiohq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/composiohq/composio\"\u003ecomposio\u003c/a\u003e\u003c/b\u003e ⭐ 26,428    \n   Composio equips your AI agents \u0026 LLMs with 100+ high-quality integrations via function calling  \n   🔗 [docs.composio.dev](https://docs.composio.dev)  \n\n15. \u003ca href=\"https://github.com/huggingface/smolagents\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/smolagents\"\u003esmolagents\u003c/a\u003e\u003c/b\u003e ⭐ 25,075    \n   🤗 smolagents: a barebones library for agents that think in code.  \n   🔗 [huggingface.co/docs/smolagents](https://huggingface.co/docs/smolagents)  \n\n16. \u003ca href=\"https://github.com/assafelovic/gpt-researcher\"\u003eassafelovic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/assafelovic/gpt-researcher\"\u003egpt-researcher\u003c/a\u003e\u003c/b\u003e ⭐ 24,993    \n   An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations.  \n   🔗 [gptr.dev](https://gptr.dev)  \n\n17. \u003ca href=\"https://github.com/fosowl/agenticseek\"\u003efosowl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fosowl/agenticseek\"\u003eagenticSeek\u003c/a\u003e\u003c/b\u003e ⭐ 24,539    \n   A 100% local alternative to Manus AI, this voice-enabled AI assistant autonomously browses the web, writes code, and plans tasks while keeping all data on your device.  \n   🔗 [agenticseek.tech](http://agenticseek.tech)  \n\n18. \u003ca href=\"https://github.com/microsoft/omniparser\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/omniparser\"\u003eOmniParser\u003c/a\u003e\u003c/b\u003e ⭐ 24,265    \n   OmniParser is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements  \n\n19. \u003ca href=\"https://github.com/langchain-ai/langgraph\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/langgraph\"\u003elanggraph\u003c/a\u003e\u003c/b\u003e ⭐ 23,696    \n   LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain.  \n   🔗 [docs.langchain.com/oss/python/langgraph](https://docs.langchain.com/oss/python/langgraph/)  \n\n20. \u003ca href=\"https://github.com/yoheinakajima/babyagi\"\u003eyoheinakajima/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yoheinakajima/babyagi\"\u003ebabyagi\u003c/a\u003e\u003c/b\u003e ⭐ 22,094    \n   GPT-4 powered task-driven autonomous agent  \n   🔗 [babyagi.org](https://babyagi.org/)  \n\n21. \u003ca href=\"https://github.com/a2aproject/a2a\"\u003ea2aproject/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/a2aproject/a2a\"\u003eA2A\u003c/a\u003e\u003c/b\u003e ⭐ 21,577    \n   An open protocol enabling communication and interoperability between opaque agentic applications.  \n   🔗 [a2a-protocol.org](https://a2a-protocol.org/)  \n\n22. \u003ca href=\"https://github.com/openai/swarm\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/swarm\"\u003eswarm\u003c/a\u003e\u003c/b\u003e ⭐ 20,819    \n   A framework exploring ergonomic, lightweight multi-agent orchestration.  \n\n23. \u003ca href=\"https://github.com/letta-ai/letta\"\u003eletta-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/letta-ai/letta\"\u003eletta\u003c/a\u003e\u003c/b\u003e ⭐ 20,805    \n   Letta (formerly MemGPT) is a framework for creating LLM services with memory.  \n   🔗 [docs.letta.com](https://docs.letta.com/)  \n\n24. \u003ca href=\"https://github.com/nirdiamant/genai_agents\"\u003enirdiamant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nirdiamant/genai_agents\"\u003eGenAI_Agents\u003c/a\u003e\u003c/b\u003e ⭐ 19,499    \n   Tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.  \n\n25. \u003ca href=\"https://github.com/bytedance/deer-flow\"\u003ebytedance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedance/deer-flow\"\u003edeer-flow\u003c/a\u003e\u003c/b\u003e ⭐ 19,381    \n   DeerFlow is a community-driven Deep Research framework, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source community.  \n   🔗 [deerflow.tech](https://deerflow.tech)  \n\n26. \u003ca href=\"https://github.com/unity-technologies/ml-agents\"\u003eunity-technologies/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/unity-technologies/ml-agents\"\u003eml-agents\u003c/a\u003e\u003c/b\u003e ⭐ 19,065    \n   The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.  \n   🔗 [unity.com/products/machine-learning-agents](https://unity.com/products/machine-learning-agents)  \n\n27. \u003ca href=\"https://github.com/camel-ai/owl\"\u003ecamel-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/camel-ai/owl\"\u003eowl\u003c/a\u003e\u003c/b\u003e ⭐ 18,929    \n   🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation  \n\n28. \u003ca href=\"https://github.com/openai/openai-agents-python\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/openai-agents-python\"\u003eopenai-agents-python\u003c/a\u003e\u003c/b\u003e ⭐ 18,502    \n   A lightweight yet powerful framework for building multi-agent workflows. It is provider-agnostic, supporting the OpenAI Responses and Chat Completions APIs, as well as 100+ other LLMs.  \n   🔗 [openai.github.io/openai-agents-python](https://openai.github.io/openai-agents-python/)  \n\n29. \u003ca href=\"https://github.com/dzhng/deep-research\"\u003edzhng/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dzhng/deep-research\"\u003edeep-research\u003c/a\u003e\u003c/b\u003e ⭐ 18,372    \n   An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models.  \n\n30. \u003ca href=\"https://github.com/alibaba-nlp/deepresearch\"\u003ealibaba-nlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alibaba-nlp/deepresearch\"\u003eDeepResearch\u003c/a\u003e\u003c/b\u003e ⭐ 18,035    \n   Tongyi Deep Research, the Leading Open-source Deep Research Agent  \n   🔗 [tongyi-agent.github.io/blog/introducing-tongyi-deep-research](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)  \n\n31. \u003ca href=\"https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart\"\u003egoogle-gemini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-gemini/gemini-fullstack-langgraph-quickstart\"\u003egemini-fullstack-langgraph-quickstart\u003c/a\u003e\u003c/b\u003e ⭐ 17,758    \n   Demonstrates a fullstack application using a React and LangGraph-powered backend agent. The agent is designed to perform comprehensive research on a user's query.  \n   🔗 [ai.google.dev/gemini-api/docs/google-search](https://ai.google.dev/gemini-api/docs/google-search)  \n\n32. \u003ca href=\"https://github.com/emcie-co/parlant\"\u003eemcie-co/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/emcie-co/parlant\"\u003eparlant\u003c/a\u003e\u003c/b\u003e ⭐ 17,579    \n   LLM agents built for control. Designed for real-world use. Deployed in minutes.  \n   🔗 [www.parlant.io](https://www.parlant.io)  \n\n33. \u003ca href=\"https://github.com/google/adk-python\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/adk-python\"\u003eadk-python\u003c/a\u003e\u003c/b\u003e ⭐ 17,293    \n   An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.  \n   🔗 [google.github.io/adk-docs](https://google.github.io/adk-docs/)  \n\n34. \u003ca href=\"https://github.com/agentscope-ai/agentscope\"\u003eagentscope-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agentscope-ai/agentscope\"\u003eagentscope\u003c/a\u003e\u003c/b\u003e ⭐ 15,850    \n   AgentScope: Agent-Oriented Programming for Building LLM Applications  \n   🔗 [doc.agentscope.io](https://doc.agentscope.io/)  \n\n35. \u003ca href=\"https://github.com/camel-ai/camel\"\u003ecamel-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/camel-ai/camel\"\u003ecamel\u003c/a\u003e\u003c/b\u003e ⭐ 15,751    \n   🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org  \n   🔗 [docs.camel-ai.org](https://docs.camel-ai.org/)  \n\n36. \u003ca href=\"https://github.com/pydantic/pydantic-ai\"\u003epydantic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydantic/pydantic-ai\"\u003epydantic-ai\u003c/a\u003e\u003c/b\u003e ⭐ 14,427    \n   PydanticAI is a Python Agent Framework designed to make it less painful to build production grade applications with Generative AI.  \n   🔗 [ai.pydantic.dev](https://ai.pydantic.dev)  \n\n37. \u003ca href=\"https://github.com/asyncfuncai/deepwiki-open\"\u003easyncfuncai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/asyncfuncai/deepwiki-open\"\u003edeepwiki-open\u003c/a\u003e\u003c/b\u003e ⭐ 13,812    \n   Custom implementation of DeepWiki, automatically creates beautiful, interactive wikis for any GitHub, GitLab, or BitBucket repository  \n   🔗 [asyncfunc.mintlify.app](https://asyncfunc.mintlify.app/)  \n\n38. \u003ca href=\"https://github.com/smol-ai/developer\"\u003esmol-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/smol-ai/developer\"\u003edeveloper\u003c/a\u003e\u003c/b\u003e ⭐ 12,205    \n   the first library to let you embed a developer agent in your own app!  \n   🔗 [twitter.com/smolmodels](https://twitter.com/SmolModels)  \n\n39. \u003ca href=\"https://github.com/sakanaai/ai-scientist\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/ai-scientist\"\u003eAI-Scientist\u003c/a\u003e\u003c/b\u003e ⭐ 11,987    \n   The AI Scientist, the first comprehensive system for fully automatic scientific discovery, enabling Foundation Models such as Large Language Models (LLMs) to perform research independently.  \n\n40. \u003ca href=\"https://github.com/microsoft/agent-lightning\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/agent-lightning\"\u003eagent-lightning\u003c/a\u003e\u003c/b\u003e ⭐ 11,622    \n   A structured way to train your agents with Automatic Prompt Optimization (APO). Just like you train a machine learning model on data, you can train an agent on a task dataset.  \n   🔗 [microsoft.github.io/agent-lightning](https://microsoft.github.io/agent-lightning/)  \n\n41. \u003ca href=\"https://github.com/ag-ui-protocol/ag-ui\"\u003eag-ui-protocol/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ag-ui-protocol/ag-ui\"\u003eag-ui\u003c/a\u003e\u003c/b\u003e ⭐ 11,565    \n   AG-UI: the Agent-User Interaction Protocol. Bring Agents into Frontend Applications.  \n   🔗 [ag-ui.com](https://ag-ui.com)  \n\n42. \u003ca href=\"https://github.com/langchain-ai/open_deep_research\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/open_deep_research\"\u003eopen_deep_research\u003c/a\u003e\u003c/b\u003e ⭐ 10,301    \n   Open Deep Research is an open source assistant that automates research and produces customizable reports on any topic  \n\n43. \u003ca href=\"https://github.com/microsoft/magentic-ui\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/magentic-ui\"\u003emagentic-ui\u003c/a\u003e\u003c/b\u003e ⭐ 9,609    \n   A prototype of a human-centered interface powered by a multi-agent system that can browse and perform actions on the web, generate and execute code  \n   🔗 [www.microsoft.com/en-us/research/blog/magentic-ui-an-experimental-human-centered-web-agent](https://www.microsoft.com/en-us/research/blog/magentic-ui-an-experimental-human-centered-web-agent/)  \n\n44. \u003ca href=\"https://github.com/humanlayer/humanlayer\"\u003ehumanlayer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/humanlayer/humanlayer\"\u003ehumanlayer\u003c/a\u003e\u003c/b\u003e ⭐ 8,911    \n   HumanLayer is an API and SDK that enables AI Agents to contact humans for help, feedback, and approvals.  \n   🔗 [humanlayer.dev/code](https://humanlayer.dev/code)  \n\n45. \u003ca href=\"https://github.com/meta-llama/llama-stack\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/llama-stack\"\u003ellama-stack\u003c/a\u003e\u003c/b\u003e ⭐ 8,246    \n   Llama Stack standardizes the building blocks needed to bring genai applications to market. These blocks cover model training and fine-tuning, evaluation, and running AI agents in production  \n   🔗 [llamastack.github.io](https://llamastack.github.io)  \n\n46. \u003ca href=\"https://github.com/upsonic/upsonic\"\u003eupsonic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/upsonic/upsonic\"\u003eUpsonic\u003c/a\u003e\u003c/b\u003e ⭐ 7,750    \n   Upsonic is a reliability-focused framework designed for real-world applications. It enables trusted agent workflows in your organization through advanced reliability features, including verification layers, triangular architecture, validator agents, and output evaluation systems.  \n   🔗 [docs.upsonic.ai](https://docs.upsonic.ai)  \n\n47. \u003ca href=\"https://github.com/zilliztech/deep-searcher\"\u003ezilliztech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zilliztech/deep-searcher\"\u003edeep-searcher\u003c/a\u003e\u003c/b\u003e ⭐ 7,506    \n   DeepSearcher combines reasoning LLMs and VectorDBs o perform search, evaluation, and reasoning based on private data, providing highly accurate answer and comprehensive report  \n   🔗 [zilliztech.github.io/deep-searcher](https://zilliztech.github.io/deep-searcher/)  \n\n48. \u003ca href=\"https://github.com/awslabs/agent-squad\"\u003eawslabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/awslabs/agent-squad\"\u003eagent-squad\u003c/a\u003e\u003c/b\u003e ⭐ 7,280    \n   Flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations  \n   🔗 [awslabs.github.io/agent-squad](https://awslabs.github.io/agent-squad/)  \n\n49. \u003ca href=\"https://github.com/x-plug/mobileagent\"\u003ex-plug/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/x-plug/mobileagent\"\u003eMobileAgent\u003c/a\u003e\u003c/b\u003e ⭐ 7,035    \n    Mobile-Agent: The Powerful GUI Agent Family  \n\n50. \u003ca href=\"https://github.com/mnotgod96/appagent\"\u003emnotgod96/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mnotgod96/appagent\"\u003eAppAgent\u003c/a\u003e\u003c/b\u003e ⭐ 6,478    \n   AppAgent: Multimodal Agents as Smartphone Users, an LLM-based multimodal agent framework designed to operate smartphone apps.  \n   🔗 [appagent-official.github.io](https://appagent-official.github.io/)  \n\n51. \u003ca href=\"https://github.com/samsungsailmontreal/tinyrecursivemodels\"\u003esamsungsailmontreal/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/samsungsailmontreal/tinyrecursivemodels\"\u003eTinyRecursiveModels\u003c/a\u003e\u003c/b\u003e ⭐ 6,275    \n   A recursive reasoning model that achieves amazing scores ARC-AGI-1 and ARC-AGI-2 with a tiny 7M parameters neural network  \n\n52. \u003ca href=\"https://github.com/prefecthq/marvin\"\u003eprefecthq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prefecthq/marvin\"\u003emarvin\u003c/a\u003e\u003c/b\u003e ⭐ 6,055    \n   an ambient intelligence library  \n   🔗 [marvin.mintlify.app](https://marvin.mintlify.app)  \n\n53. \u003ca href=\"https://github.com/openai/openai-cs-agents-demo\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/openai-cs-agents-demo\"\u003eopenai-cs-agents-demo\u003c/a\u003e\u003c/b\u003e ⭐ 5,903    \n   Demo of a Customer Service Agent interface built on top of the OpenAI Agents SDK  \n\n54. \u003ca href=\"https://github.com/pyspur-dev/pyspur\"\u003epyspur-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyspur-dev/pyspur\"\u003epyspur\u003c/a\u003e\u003c/b\u003e ⭐ 5,660    \n   A visual playground for agentic workflows: Iterate over your agents 10x faster  \n   🔗 [pyspur.dev](https://pyspur.dev)  \n\n55. \u003ca href=\"https://github.com/kyegomez/swarms\"\u003ekyegomez/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kyegomez/swarms\"\u003eswarms\u003c/a\u003e\u003c/b\u003e ⭐ 5,638    \n   The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework. Website: https://swarms.ai  \n   🔗 [docs.swarms.world](https://docs.swarms.world)  \n\n56. \u003ca href=\"https://github.com/brainblend-ai/atomic-agents\"\u003ebrainblend-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/brainblend-ai/atomic-agents\"\u003eatomic-agents\u003c/a\u003e\u003c/b\u003e ⭐ 5,516    \n   Atomic Agents provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization.  \n\n57. \u003ca href=\"https://github.com/crewaiinc/crewai-examples\"\u003ecrewaiinc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/crewaiinc/crewai-examples\"\u003ecrewAI-examples\u003c/a\u003e\u003c/b\u003e ⭐ 5,436    \n   A collection of examples that show how to use CrewAI framework to automate workflows.  \n\n58. \u003ca href=\"https://github.com/landing-ai/vision-agent\"\u003elanding-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/landing-ai/vision-agent\"\u003evision-agent\u003c/a\u003e\u003c/b\u003e ⭐ 5,206    \n   VisionAgent is a library that helps you utilize agent frameworks to generate code to solve your vision task  \n\n59. \u003ca href=\"https://github.com/codelion/openevolve\"\u003ecodelion/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/codelion/openevolve\"\u003eopenevolve\u003c/a\u003e\u003c/b\u003e ⭐ 5,203    \n   Evolutionary coding agent (like AlphaEvolve) enabling automated scientific and algorithmic discovery  \n\n60. \u003ca href=\"https://github.com/strands-agents/sdk-python\"\u003estrands-agents/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/strands-agents/sdk-python\"\u003esdk-python\u003c/a\u003e\u003c/b\u003e ⭐ 4,940    \n   A model-driven approach to building AI agents in just a few lines of code.  \n   🔗 [strandsagents.com](https://strandsagents.com)  \n\n61. \u003ca href=\"https://github.com/rowboatlabs/rowboat\"\u003erowboatlabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rowboatlabs/rowboat\"\u003erowboat\u003c/a\u003e\u003c/b\u003e ⭐ 4,321    \n   Local-first AI coworker, with memory  \n   🔗 [www.rowboatlabs.com](https://www.rowboatlabs.com)  \n\n62. \u003ca href=\"https://github.com/meta-llama/llama-agentic-system\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/llama-agentic-system\"\u003ellama-stack-apps\u003c/a\u003e\u003c/b\u003e ⭐ 4,289    \n   Agentic components of the Llama Stack APIs  \n\n63. \u003ca href=\"https://github.com/tencentcloudadp/youtu-agent\"\u003etencentcloudadp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tencentcloudadp/youtu-agent\"\u003eyoutu-agent\u003c/a\u003e\u003c/b\u003e ⭐ 4,279    \n   A flexible, high-performance framework for building, running, and evaluating autonomous agents  \n   🔗 [tencentcloudadp.github.io/youtu-agent](https://tencentcloudadp.github.io/youtu-agent/)  \n\n64. \u003ca href=\"https://github.com/ag2ai/ag2\"\u003eag2ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ag2ai/ag2\"\u003eag2\u003c/a\u003e\u003c/b\u003e ⭐ 4,082    \n   AG2 (formerly AutoGen) is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks.  \n   🔗 [ag2.ai](https://ag2.ai)  \n\n65. \u003ca href=\"https://github.com/joshuac215/agent-service-toolkit\"\u003ejoshuac215/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/joshuac215/agent-service-toolkit\"\u003eagent-service-toolkit\u003c/a\u003e\u003c/b\u003e ⭐ 4,034    \n   A full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit.  \n   🔗 [agent-service-toolkit.streamlit.app](https://agent-service-toolkit.streamlit.app)  \n\n66. \u003ca href=\"https://github.com/going-doer/paper2code\"\u003egoing-doer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/going-doer/paper2code\"\u003ePaper2Code\u003c/a\u003e\u003c/b\u003e ⭐ 4,000    \n   A multi-agent LLM system that transforms paper into a code repository. It follows a three-stage pipeline: planning, analysis, and code generation, each handled by specialized agents.  \n\n67. \u003ca href=\"https://github.com/getzep/zep\"\u003egetzep/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/getzep/zep\"\u003ezep\u003c/a\u003e\u003c/b\u003e ⭐ 3,999    \n   Zep is a memory platform for AI agents that learns from user interactions and business data  \n   🔗 [help.getzep.com](https://help.getzep.com)  \n\n68. \u003ca href=\"https://github.com/langroid/langroid\"\u003elangroid/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langroid/langroid\"\u003elangroid\u003c/a\u003e\u003c/b\u003e ⭐ 3,849    \n   Harness LLMs with Multi-Agent Programming  \n   🔗 [langroid.github.io/langroid](https://langroid.github.io/langroid/)  \n\n69. \u003ca href=\"https://github.com/openmanus/openmanus-rl\"\u003eopenmanus/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openmanus/openmanus-rl\"\u003eOpenManus-RL\u003c/a\u003e\u003c/b\u003e ⭐ 3,836    \n   OpenManus-RL is an open-source initiative collaboratively led by Ulab-UIUC and MetaGPT. This project is an extended version of the original OpenManus initiative.  \n\n70. \u003ca href=\"https://github.com/i-am-bee/beeai-framework\"\u003ei-am-bee/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/i-am-bee/beeai-framework\"\u003ebeeai-framework\u003c/a\u003e\u003c/b\u003e ⭐ 3,069    \n   Build production-ready AI agents in both Python and Typescript.  \n   🔗 [framework.beeai.dev](http://framework.beeai.dev)  \n\n71. \u003ca href=\"https://github.com/facebookresearch/pearl\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/pearl\"\u003ePearl\u003c/a\u003e\u003c/b\u003e ⭐ 2,971    \n   A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.   \n\n72. \u003ca href=\"https://github.com/cheshire-cat-ai/core\"\u003echeshire-cat-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cheshire-cat-ai/core\"\u003ecore\u003c/a\u003e\u003c/b\u003e ⭐ 2,951    \n   AI agent microservice  \n   🔗 [cheshirecat.ai](https://cheshirecat.ai)  \n\n73. \u003ca href=\"https://github.com/vllm-project/semantic-router\"\u003evllm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vllm-project/semantic-router\"\u003esemantic-router\u003c/a\u003e\u003c/b\u003e ⭐ 2,907    \n   An Mixture-of-Models router that directs OpenAI API requests to the most suitable models from a defined pool based on Semantic Understanding  \n   🔗 [vllm-semantic-router.com](https://vllm-semantic-router.com)  \n\n74. \u003ca href=\"https://github.com/om-ai-lab/omagent\"\u003eom-ai-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/om-ai-lab/omagent\"\u003eOmAgent\u003c/a\u003e\u003c/b\u003e ⭐ 2,624    \n   OmAgent is python library for building multimodal language agents with ease. We try to keep the library simple without too much overhead like other agent framework.  \n   🔗 [om-agent.com](https://om-agent.com)  \n\n75. \u003ca href=\"https://github.com/swe-agent/mini-swe-agent\"\u003eswe-agent/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/swe-agent/mini-swe-agent\"\u003emini-swe-agent\u003c/a\u003e\u003c/b\u003e ⭐ 2,606    \n   The 100 line AI agent that solves GitHub issues or helps you in your command line  \n   🔗 [mini-swe-agent.com](https://mini-swe-agent.com)  \n\n76. \u003ca href=\"https://github.com/griptape-ai/griptape\"\u003egriptape-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/griptape-ai/griptape\"\u003egriptape\u003c/a\u003e\u003c/b\u003e ⭐ 2,458    \n   Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.   \n   🔗 [www.griptape.ai](https://www.griptape.ai)  \n\n77. \u003ca href=\"https://github.com/langchain-ai/executive-ai-assistant\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/executive-ai-assistant\"\u003eexecutive-ai-assistant\u003c/a\u003e\u003c/b\u003e ⭐ 2,161    \n   Executive AI Assistant (EAIA) is an AI agent that attempts to do the job of an Executive Assistant (EA).  \n\n78. \u003ca href=\"https://github.com/btahir/open-deep-research\"\u003ebtahir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/btahir/open-deep-research\"\u003eopen-deep-research\u003c/a\u003e\u003c/b\u003e ⭐ 2,119    \n   Open source alternative to Gemini Deep Research. Generate reports with AI based on search results.  \n   🔗 [opendeepresearch.vercel.app](https://opendeepresearch.vercel.app)  \n\n79. \u003ca href=\"https://github.com/agentops-ai/agentstack\"\u003eagentops-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agentops-ai/agentstack\"\u003eAgentStack\u003c/a\u003e\u003c/b\u003e ⭐ 2,086    \n   AgentStack scaffolds your agent stack - The tech stack that collectively is your agent  \n\n80. \u003ca href=\"https://github.com/run-llama/llama_deploy\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/llama_deploy\"\u003ellama_deploy\u003c/a\u003e\u003c/b\u003e ⭐ 2,071    \n   Async-first framework for deploying, scaling, and productionizing agentic multi-service systems based on workflows from llama_index.  \n   🔗 [docs.llamaindex.ai/en/stable/module_guides/llama_deploy](https://docs.llamaindex.ai/en/stable/module_guides/llama_deploy/)  \n\n81. \u003ca href=\"https://github.com/sakanaai/ai-scientist-v2\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/ai-scientist-v2\"\u003eAI-Scientist-v2\u003c/a\u003e\u003c/b\u003e ⭐ 2,034    \n   The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search  \n\n82. \u003ca href=\"https://github.com/openautocoder/agentless\"\u003eopenautocoder/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openautocoder/agentless\"\u003eAgentless\u003c/a\u003e\u003c/b\u003e ⭐ 2,002    \n   Agentless🐱:  an agentless approach to automatically solve software development problems  \n\n83. \u003ca href=\"https://github.com/weaviate/elysia\"\u003eweaviate/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/weaviate/elysia\"\u003eelysia\u003c/a\u003e\u003c/b\u003e ⭐ 1,867    \n   Elysia is an agentic platform designed to use tools in a decision tree. A decision agent decides which tools to use dynamically based on its environment and context.  \n\n84. \u003ca href=\"https://github.com/jd-opensource/oxygent\"\u003ejd-opensource/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jd-opensource/oxygent\"\u003eOxyGent\u003c/a\u003e\u003c/b\u003e ⭐ 1,831    \n   OxyGent is a modular multi-agent framework that lets you build, deploy, and evolve AI teams  \n   🔗 [oxygent.jd.com](https://oxygent.jd.com)  \n\n85. \u003ca href=\"https://github.com/msoedov/agentic_security\"\u003emsoedov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/msoedov/agentic_security\"\u003eagentic_security\u003c/a\u003e\u003c/b\u003e ⭐ 1,749    \n   An open-source vulnerability scanner for Agent Workflows and LLMs. Protecting AI systems from jailbreaks, fuzzing, and multimodal attacks.  \n   🔗 [agentic-security.vercel.app](https://agentic-security.vercel.app)  \n\n86. \u003ca href=\"https://github.com/agno-agi/dash\"\u003eagno-agi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agno-agi/dash\"\u003edash\u003c/a\u003e\u003c/b\u003e ⭐ 1,598    \n   Self-learning data agent that grounds its answers in 6 layers of context. Inspired by OpenAI's in-house implementation.  \n\n87. \u003ca href=\"https://github.com/szczyglis-dev/py-gpt\"\u003eszczyglis-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/szczyglis-dev/py-gpt\"\u003epy-gpt\u003c/a\u003e\u003c/b\u003e ⭐ 1,559    \n   Desktop AI Assistant powered by GPT-5, GPT-4, o1, o3, Gemini, Claude, Ollama, DeepSeek, Perplexity, Grok, Bielik, chat, vision, voice, RAG, image and video generation, agents, tools, MCP, plugins, speech synthesis and recognition, web search, memory, presets, assistants,and more. Linux, Windows, Mac  \n   🔗 [pygpt.net](https://pygpt.net)  \n\n88. \u003ca href=\"https://github.com/agentera/agently\"\u003eagentera/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agentera/agently\"\u003eAgently\u003c/a\u003e\u003c/b\u003e ⭐ 1,530    \n   Agently is a development framework that helps developers build AI agent native application really fast.  \n   🔗 [agently.tech](http://agently.tech)  \n\n89. \u003ca href=\"https://github.com/shengranhu/adas\"\u003eshengranhu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shengranhu/adas\"\u003eADAS\u003c/a\u003e\u003c/b\u003e ⭐ 1,498    \n   Automated Design of Agentic Systems using Meta Agent Search to show agents can invent novel and powerful agent designs  \n   🔗 [www.shengranhu.com/adas](https://www.shengranhu.com/ADAS/)  \n\n90. \u003ca href=\"https://github.com/link-agi/autoagents\"\u003elink-agi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/link-agi/autoagents\"\u003eAutoAgents\u003c/a\u003e\u003c/b\u003e ⭐ 1,456    \n   [IJCAI 2024] Generate different roles for GPTs to form a collaborative entity for complex tasks.  \n   🔗 [huggingface.co/spaces/linksoul/autoagents](https://huggingface.co/spaces/LinkSoul/AutoAgents)  \n\n91. \u003ca href=\"https://github.com/prefecthq/controlflow\"\u003eprefecthq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prefecthq/controlflow\"\u003eControlFlow\u003c/a\u003e\u003c/b\u003e ⭐ 1,388    \n   ControlFlow provides a structured, developer-focused framework for defining workflows and delegating work to LLMs, without sacrificing control or transparency  \n   🔗 [controlflow.ai](https://controlflow.ai)  \n\n92. \u003ca href=\"https://github.com/langchain-ai/langgraph-swarm-py\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/langgraph-swarm-py\"\u003elanggraph-swarm-py\u003c/a\u003e\u003c/b\u003e ⭐ 1,351    \n   A library for creating swarm-style multi-agent systems using LangGraph. A swarm is a type of multi-agent architecture where agents dynamically hand off control to one another based on their specializations  \n   🔗 [langchain-ai.github.io/langgraph/concepts/multi_agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/)  \n\n93. \u003ca href=\"https://github.com/bytedance-seed/m3-agent\"\u003ebytedance-seed/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedance-seed/m3-agent\"\u003em3-agent\u003c/a\u003e\u003c/b\u003e ⭐ 1,214    \n   Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory  \n\n94. \u003ca href=\"https://github.com/k-dense-ai/karpathy\"\u003ek-dense-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/k-dense-ai/karpathy\"\u003ekarpathy\u003c/a\u003e\u003c/b\u003e ⭐ 1,186    \n   An agentic Machine Learning Engineer that trains state-of-the-art ML models using Claude Code SDK and Google ADK  \n   🔗 [k-dense.ai](https://k-dense.ai)  \n\n95. \u003ca href=\"https://github.com/plurai-ai/intellagent\"\u003eplurai-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plurai-ai/intellagent\"\u003eintellagent\u003c/a\u003e\u003c/b\u003e ⭐ 1,161    \n   Simulate interactions, analyze performance, and gain actionable insights for conversational agents. Test, evaluate, and optimize your agent to ensure reliable real-world deployment.  \n   🔗 [intellagent-doc.plurai.ai](https://intellagent-doc.plurai.ai/)  \n\n96. \u003ca href=\"https://github.com/google-deepmind/concordia\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/concordia\"\u003econcordia\u003c/a\u003e\u003c/b\u003e ⭐ 1,155    \n   Concordia is a library to facilitate construction and use of generative agent-based models to simulate interactions of agents in grounded physical, social, or digital space.  \n\n97. \u003ca href=\"https://github.com/strnad/crewai-studio\"\u003estrnad/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/strnad/crewai-studio\"\u003eCrewAI-Studio\u003c/a\u003e\u003c/b\u003e ⭐ 1,144    \n   agentic,gui,automation  \n\n98. \u003ca href=\"https://github.com/thudm/cogagent\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/cogagent\"\u003eCogAgent\u003c/a\u003e\u003c/b\u003e ⭐ 1,124    \n   An open-sourced end-to-end VLM-based GUI Agent  \n\n99. \u003ca href=\"https://github.com/victordibia/autogen-ui\"\u003evictordibia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/victordibia/autogen-ui\"\u003eautogen-ui\u003c/a\u003e\u003c/b\u003e ⭐ 981    \n   Web UI for AutoGen (A Framework Multi-Agent LLM Applications)  \n\n100. \u003ca href=\"https://github.com/thytu/agentarium\"\u003ethytu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thytu/agentarium\"\u003eAgentarium\u003c/a\u003e\u003c/b\u003e ⭐ 933    \n   Framework for managing and orchestrating AI agents with ease. Agentarium provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments.  \n\n101. \u003ca href=\"https://github.com/alpha-innovator/internagent\"\u003ealpha-innovator/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alpha-innovator/internagent\"\u003eInternAgent\u003c/a\u003e\u003c/b\u003e ⭐ 840    \n   When Agent Becomes the Scientist – Building Closed-Loop System from Hypothesis to Verification  \n   🔗 [discovery.intern-ai.org.cn/home](https://discovery.intern-ai.org.cn/home)  \n\n102. \u003ca href=\"https://github.com/deedy/mac_computer_use\"\u003edeedy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deedy/mac_computer_use\"\u003emac_computer_use\u003c/a\u003e\u003c/b\u003e ⭐ 831    \n   A fork of Anthropic Computer Use that you can run on Mac computers to give Claude and other AI models autonomous access to your computer.  \n   🔗 [x.com/deedydas/status/1849481225041559910](https://x.com/deedydas/status/1849481225041559910)  \n\n103. \u003ca href=\"https://github.com/codingmoh/open-codex\"\u003ecodingmoh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/codingmoh/open-codex\"\u003eopen-codex\u003c/a\u003e\u003c/b\u003e ⭐ 665    \n   Open Codex is a fully open-source command-line AI assistant inspired by OpenAI Codex, supporting local language models like phi-4-mini and full integration with Ollama.  \n\n104. \u003ca href=\"https://github.com/salesforceairesearch/agentlite\"\u003esalesforceairesearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforceairesearch/agentlite\"\u003eAgentLite\u003c/a\u003e\u003c/b\u003e ⭐ 641    \n   AgentLite is a research-oriented library designed for building and advancing LLM-based task-oriented agent systems. It simplifies the implementation of new agent/multi-agent architectures, enabling easy orchestration of multiple agents through a manager agent.  \n\n105. \u003ca href=\"https://github.com/quantalogic/quantalogic\"\u003equantalogic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantalogic/quantalogic\"\u003equantalogic\u003c/a\u003e\u003c/b\u003e ⭐ 461    \n   QuantaLogic is a ReAct (Reasoning \u0026 Action) framework for building advanced AI agents. The cli version include coding capabilities comparable to Aider.  \n\n106. \u003ca href=\"https://github.com/agentscope-ai/agentscope-runtime\"\u003eagentscope-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agentscope-ai/agentscope-runtime\"\u003eagentscope-runtime\u003c/a\u003e\u003c/b\u003e ⭐ 375    \n   AgentScope Runtime: secure sandboxed tool execution and scalable agent deployment  \n   🔗 [runtime.agentscope.io](https://runtime.agentscope.io/)  \n\n107. \u003ca href=\"https://github.com/mannaandpoem/openmanus\"\u003emannaandpoem/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mannaandpoem/openmanus\"\u003eOpenManus\u003c/a\u003e\u003c/b\u003e ⭐ 306    \n   Open source version of Manus, the general AI agent  \n\n108. \u003ca href=\"https://github.com/sakanaai/ai-scientist-iclr2025-workshop-experiment\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/ai-scientist-iclr2025-workshop-experiment\"\u003eAI-Scientist-ICLR2025-Workshop-Experiment\u003c/a\u003e\u003c/b\u003e ⭐ 279    \n   A paper produced by The AI Scientist passed a peer-review process at a workshop in a top machine learning conference  \n\n109. \u003ca href=\"https://github.com/prithivirajdamodaran/route0x\"\u003eprithivirajdamodaran/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prithivirajdamodaran/route0x\"\u003eRoute0x\u003c/a\u003e\u003c/b\u003e ⭐ 119    \n   A production-grade query routing solution, leveraging LLMs while optimizing for cost per query  \n\n## Code Quality\n\nCode quality tooling: linters, formatters, pre-commit hooks, unused code removal.  \n\n1. \u003ca href=\"https://github.com/astral-sh/ruff\"\u003eastral-sh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/astral-sh/ruff\"\u003eruff\u003c/a\u003e\u003c/b\u003e ⭐ 45,341    \n   An extremely fast Python linter and code formatter, written in Rust.  \n   🔗 [docs.astral.sh/ruff](https://docs.astral.sh/ruff)  \n\n2. \u003ca href=\"https://github.com/psf/black\"\u003epsf/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/psf/black\"\u003eblack\u003c/a\u003e\u003c/b\u003e ⭐ 41,322    \n   The uncompromising Python code formatter  \n   🔗 [black.readthedocs.io/en/stable](https://black.readthedocs.io/en/stable/)  \n\n3. \u003ca href=\"https://github.com/pre-commit/pre-commit\"\u003epre-commit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pre-commit/pre-commit\"\u003epre-commit\u003c/a\u003e\u003c/b\u003e ⭐ 14,844    \n   A framework for managing and maintaining multi-language pre-commit hooks.  \n   🔗 [pre-commit.com](https://pre-commit.com)  \n\n4. \u003ca href=\"https://github.com/google/yapf\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/yapf\"\u003eyapf\u003c/a\u003e\u003c/b\u003e ⭐ 13,977    \n   A formatter for Python files  \n\n5. \u003ca href=\"https://github.com/sqlfluff/sqlfluff\"\u003esqlfluff/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sqlfluff/sqlfluff\"\u003esqlfluff\u003c/a\u003e\u003c/b\u003e ⭐ 9,443    \n   A modular SQL linter and auto-formatter with support for multiple dialects and templated code.  \n   🔗 [www.sqlfluff.com](https://www.sqlfluff.com)  \n\n6. \u003ca href=\"https://github.com/pycqa/isort\"\u003epycqa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycqa/isort\"\u003eisort\u003c/a\u003e\u003c/b\u003e ⭐ 6,893    \n   A Python utility / library to sort imports.  \n   🔗 [pycqa.github.io/isort](https://pycqa.github.io/isort/)  \n\n7. \u003ca href=\"https://github.com/davidhalter/jedi\"\u003edavidhalter/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/davidhalter/jedi\"\u003ejedi\u003c/a\u003e\u003c/b\u003e ⭐ 6,100    \n   Awesome autocompletion, static analysis and refactoring library for python  \n   🔗 [jedi.readthedocs.io](http://jedi.readthedocs.io)  \n\n8. \u003ca href=\"https://github.com/pycqa/pylint\"\u003epycqa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycqa/pylint\"\u003epylint\u003c/a\u003e\u003c/b\u003e ⭐ 5,638    \n   It's not just a linter that annoys you!  \n   🔗 [pylint.readthedocs.io/en/latest](https://pylint.readthedocs.io/en/latest/)  \n\n9. \u003ca href=\"https://github.com/jendrikseipp/vulture\"\u003ejendrikseipp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jendrikseipp/vulture\"\u003evulture\u003c/a\u003e\u003c/b\u003e ⭐ 4,291    \n   Find dead Python code  \n\n10. \u003ca href=\"https://github.com/asottile/pyupgrade\"\u003easottile/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/asottile/pyupgrade\"\u003epyupgrade\u003c/a\u003e\u003c/b\u003e ⭐ 4,031    \n   A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.  \n\n11. \u003ca href=\"https://github.com/pycqa/flake8\"\u003epycqa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycqa/flake8\"\u003eflake8\u003c/a\u003e\u003c/b\u003e ⭐ 3,752    \n   flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.  \n   🔗 [flake8.pycqa.org](https://flake8.pycqa.org)  \n\n12. \u003ca href=\"https://github.com/wemake-services/wemake-python-styleguide\"\u003ewemake-services/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wemake-services/wemake-python-styleguide\"\u003ewemake-python-styleguide\u003c/a\u003e\u003c/b\u003e ⭐ 2,813    \n   The strictest and most opinionated python linter ever!  \n   🔗 [wemake-python-styleguide.rtfd.io](https://wemake-python-styleguide.rtfd.io)  \n\n13. \u003ca href=\"https://github.com/python-lsp/python-lsp-server\"\u003epython-lsp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-lsp/python-lsp-server\"\u003epython-lsp-server\u003c/a\u003e\u003c/b\u003e ⭐ 2,467    \n   Fork of the python-language-server project, maintained by the Spyder IDE team and the community  \n\n14. \u003ca href=\"https://github.com/tconbeer/sqlfmt\"\u003etconbeer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tconbeer/sqlfmt\"\u003esqlfmt\u003c/a\u003e\u003c/b\u003e ⭐ 504    \n   sqlfmt formats your dbt SQL files so you don't have to  \n   🔗 [sqlfmt.com](https://sqlfmt.com)  \n\n## Crypto and Blockchain\n\nCryptocurrency and blockchain libraries: trading bots, API integration, Ethereum virtual machine, solidity.  \n\n1. \u003ca href=\"https://github.com/freqtrade/freqtrade\"\u003efreqtrade/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/freqtrade/freqtrade\"\u003efreqtrade\u003c/a\u003e\u003c/b\u003e ⭐ 46,244    \n   Free, open source crypto trading bot  \n   🔗 [www.freqtrade.io](https://www.freqtrade.io)  \n\n2. \u003ca href=\"https://github.com/ccxt/ccxt\"\u003eccxt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ccxt/ccxt\"\u003eccxt\u003c/a\u003e\u003c/b\u003e ⭐ 40,661    \n   A cryptocurrency trading API with more than 100 exchanges in JavaScript / TypeScript / Python / C# / PHP / Go   \n   🔗 [docs.ccxt.com](https://docs.ccxt.com)  \n\n3. \u003ca href=\"https://github.com/crytic/slither\"\u003ecrytic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/crytic/slither\"\u003eslither\u003c/a\u003e\u003c/b\u003e ⭐ 6,100    \n   Static Analyzer for Solidity and Vyper  \n   🔗 [blog.trailofbits.com/2018/10/19/slither-a-solidity-static-analysis-framework](https://blog.trailofbits.com/2018/10/19/slither-a-solidity-static-analysis-framework/)  \n\n4. \u003ca href=\"https://github.com/ethereum/web3.py\"\u003eethereum/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ethereum/web3.py\"\u003eweb3.py\u003c/a\u003e\u003c/b\u003e ⭐ 5,472    \n   A python interface for interacting with the Ethereum blockchain and ecosystem.  \n   🔗 [web3py.readthedocs.io](http://web3py.readthedocs.io)  \n\n5. \u003ca href=\"https://github.com/ethereum/consensus-specs\"\u003eethereum/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ethereum/consensus-specs\"\u003econsensus-specs\u003c/a\u003e\u003c/b\u003e ⭐ 3,876    \n   Ethereum Proof-of-Stake Consensus Specifications  \n   🔗 [ethereum.github.io/consensus-specs](https://ethereum.github.io/consensus-specs/)  \n\n6. \u003ca href=\"https://github.com/cyberpunkmetalhead/binance-volatility-trading-bot\"\u003ecyberpunkmetalhead/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cyberpunkmetalhead/binance-volatility-trading-bot\"\u003eBinance-volatility-trading-bot\u003c/a\u003e\u003c/b\u003e ⭐ 3,490    \n   This is a fully functioning Binance trading bot that measures the volatility of every coin on Binance and places trades with the highest gaining coins If you like this project consider donating though the Brave browser to allow me to continuously improve the script.  \n\n7. \u003ca href=\"https://github.com/bmoscon/cryptofeed\"\u003ebmoscon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bmoscon/cryptofeed\"\u003ecryptofeed\u003c/a\u003e\u003c/b\u003e ⭐ 2,674    \n   Cryptocurrency Exchange Websocket Data Feed Handler  \n\n8. \u003ca href=\"https://github.com/binance/binance-public-data\"\u003ebinance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/binance/binance-public-data\"\u003ebinance-public-data\u003c/a\u003e\u003c/b\u003e ⭐ 2,193    \n   Details on how to get Binance public data  \n\n9. \u003ca href=\"https://github.com/coinbase/agentkit\"\u003ecoinbase/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/coinbase/agentkit\"\u003eagentkit\u003c/a\u003e\u003c/b\u003e ⭐ 1,042    \n   AgentKit is Coinbase Developer Platform's framework for easily enabling AI agents to take actions onchain. It is designed to be framework-agnostic, so you can use it with any AI framework, and wallet-agnostic  \n   🔗 [docs.cdp.coinbase.com/agentkit/docs/welcome](https://docs.cdp.coinbase.com/agentkit/docs/welcome)  \n\n10. \u003ca href=\"https://github.com/dylanhogg/awesome-crypto\"\u003edylanhogg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dylanhogg/awesome-crypto\"\u003eawesome-crypto\u003c/a\u003e\u003c/b\u003e ⭐ 83    \n   A list of awesome crypto and blockchain projects  \n   🔗 [www.awesomecrypto.xyz](https://www.awesomecrypto.xyz/)  \n\n## Data\n\nGeneral data libraries: data processing, serialisation, formats, databases, SQL, connectors, web crawlers, data generation/augmentation/checks.  \n\n1. \u003ca href=\"https://github.com/microsoft/markitdown\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/markitdown\"\u003emarkitdown\u003c/a\u003e\u003c/b\u003e ⭐ 85,645    \n   A utility for converting files to Markdown, supports: PDF, PPT, Word, Excel, Images etc  \n\n2. \u003ca href=\"https://github.com/scrapy/scrapy\"\u003escrapy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/scrapy/scrapy\"\u003escrapy\u003c/a\u003e\u003c/b\u003e ⭐ 59,530    \n   Scrapy, a fast high-level web crawling \u0026 scraping framework for Python.  \n   🔗 [scrapy.org](https://scrapy.org)  \n\n3. \u003ca href=\"https://github.com/pathwaycom/pathway\"\u003epathwaycom/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pathwaycom/pathway\"\u003epathway\u003c/a\u003e\u003c/b\u003e ⭐ 57,882    \n   Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.  \n   🔗 [pathway.com](https://pathway.com)  \n\n4. \u003ca href=\"https://github.com/ds4sd/docling\"\u003eds4sd/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ds4sd/docling\"\u003edocling\u003c/a\u003e\u003c/b\u003e ⭐ 50,964    \n   Docling parses documents and exports them to the desired format with ease and speed.  \n   🔗 [docling-project.github.io/docling](https://docling-project.github.io/docling)  \n\n5. \u003ca href=\"https://github.com/apache/spark\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/spark\"\u003espark\u003c/a\u003e\u003c/b\u003e ⭐ 42,688    \n   Apache Spark - A unified analytics engine for large-scale data processing  \n   🔗 [spark.apache.org](https://spark.apache.org/)  \n\n6. \u003ca href=\"https://github.com/mindsdb/mindsdb\"\u003emindsdb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mindsdb/mindsdb\"\u003emindsdb\u003c/a\u003e\u003c/b\u003e ⭐ 38,308    \n   Federated Query Engine for AI - The only MCP Server you'll ever need  \n   🔗 [mindsdb.com](https://mindsdb.com)  \n\n7. \u003ca href=\"https://github.com/jaidedai/easyocr\"\u003ejaidedai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jaidedai/easyocr\"\u003eEasyOCR\u003c/a\u003e\u003c/b\u003e ⭐ 28,818    \n   Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.  \n   🔗 [www.jaided.ai](https://www.jaided.ai)  \n\n8. \u003ca href=\"https://github.com/qdrant/qdrant\"\u003eqdrant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qdrant/qdrant\"\u003eqdrant\u003c/a\u003e\u003c/b\u003e ⭐ 28,373    \n   Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/  \n   🔗 [qdrant.tech](https://qdrant.tech)  \n\n9. \u003ca href=\"https://github.com/getredash/redash\"\u003egetredash/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/getredash/redash\"\u003eredash\u003c/a\u003e\u003c/b\u003e ⭐ 28,172    \n   Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.  \n   🔗 [redash.io](http://redash.io/)  \n\n10. \u003ca href=\"https://github.com/humansignal/label-studio\"\u003ehumansignal/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/humansignal/label-studio\"\u003elabel-studio\u003c/a\u003e\u003c/b\u003e ⭐ 26,250    \n   Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats.  \n   🔗 [labelstud.io](https://labelstud.io)  \n\n11. \u003ca href=\"https://github.com/chroma-core/chroma\"\u003echroma-core/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chroma-core/chroma\"\u003echroma\u003c/a\u003e\u003c/b\u003e ⭐ 25,709    \n   Open-source search and retrieval database for AI applications.  \n   🔗 [www.trychroma.com](https://www.trychroma.com/)  \n\n12. \u003ca href=\"https://github.com/airbytehq/airbyte\"\u003eairbytehq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/airbytehq/airbyte\"\u003eairbyte\u003c/a\u003e\u003c/b\u003e ⭐ 20,539    \n   The leading data integration platform for ETL / ELT data pipelines from APIs, databases \u0026 files to data warehouses, data lakes \u0026 data lakehouses. Both self-hosted and Cloud-hosted.  \n   🔗 [airbyte.com](https://airbyte.com)  \n\n13. \u003ca href=\"https://github.com/joke2k/faker\"\u003ejoke2k/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/joke2k/faker\"\u003efaker\u003c/a\u003e\u003c/b\u003e ⭐ 19,043    \n   Faker is a Python package that generates fake data for you.  \n   🔗 [faker.readthedocs.io](https://faker.readthedocs.io)  \n\n14. \u003ca href=\"https://github.com/avaiga/taipy\"\u003eavaiga/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/avaiga/taipy\"\u003etaipy\u003c/a\u003e\u003c/b\u003e ⭐ 19,023    \n   Turns Data and AI algorithms into production-ready web applications in no time.  \n   🔗 [www.taipy.io](https://www.taipy.io)  \n\n15. \u003ca href=\"https://github.com/tiangolo/sqlmodel\"\u003etiangolo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tiangolo/sqlmodel\"\u003esqlmodel\u003c/a\u003e\u003c/b\u003e ⭐ 17,536    \n   SQL databases in Python, designed for simplicity, compatibility, and robustness.  \n   🔗 [sqlmodel.tiangolo.com](https://sqlmodel.tiangolo.com/)  \n\n16. \u003ca href=\"https://github.com/binux/pyspider\"\u003ebinux/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/binux/pyspider\"\u003epyspider\u003c/a\u003e\u003c/b\u003e ⭐ 17,032    \n   A Powerful Spider(Web Crawler) System in Python.  \n   🔗 [docs.pyspider.org](http://docs.pyspider.org/)  \n\n17. \u003ca href=\"https://github.com/apache/arrow\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/arrow\"\u003earrow\u003c/a\u003e\u003c/b\u003e ⭐ 16,429    \n   Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics  \n   🔗 [arrow.apache.org](https://arrow.apache.org/)  \n\n18. \u003ca href=\"https://github.com/twintproject/twint\"\u003etwintproject/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/twintproject/twint\"\u003etwint\u003c/a\u003e\u003c/b\u003e ⭐ 16,295    \n   An advanced Twitter scraping \u0026 OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.  \n\n19. \u003ca href=\"https://github.com/weaviate/weaviate\"\u003eweaviate/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/weaviate/weaviate\"\u003eweaviate\u003c/a\u003e\u003c/b\u003e ⭐ 15,466    \n   Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.  \n   🔗 [weaviate.io/developers/weaviate](https://weaviate.io/developers/weaviate/)  \n\n20. \u003ca href=\"https://github.com/cyclotruc/gitingest\"\u003ecyclotruc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cyclotruc/gitingest\"\u003egitingest\u003c/a\u003e\u003c/b\u003e ⭐ 13,754    \n   Turn any Git repository into a prompt-friendly text ingest for LLMs.  \n   🔗 [gitingest.com](https://gitingest.com)  \n\n21. \u003ca href=\"https://github.com/redis/redis-py\"\u003eredis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/redis/redis-py\"\u003eredis-py\u003c/a\u003e\u003c/b\u003e ⭐ 13,436    \n   Redis Python client  \n\n22. \u003ca href=\"https://github.com/s0md3v/photon\"\u003es0md3v/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/s0md3v/photon\"\u003ePhoton\u003c/a\u003e\u003c/b\u003e ⭐ 12,622    \n   Incredibly fast crawler designed for OSINT.  \n\n23. \u003ca href=\"https://github.com/googleapis/genai-toolbox\"\u003egoogleapis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/googleapis/genai-toolbox\"\u003egenai-toolbox\u003c/a\u003e\u003c/b\u003e ⭐ 12,598    \n   MCP Toolbox for Databases is an open source MCP server for databases. Develop tools easier, faster, and more securely by handling connection pooling, authentication.  \n   🔗 [googleapis.github.io/genai-toolbox/getting-started/introduction](https://googleapis.github.io/genai-toolbox/getting-started/introduction/)  \n\n24. \u003ca href=\"https://github.com/coleifer/peewee\"\u003ecoleifer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/coleifer/peewee\"\u003epeewee\u003c/a\u003e\u003c/b\u003e ⭐ 11,909    \n   a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb  \n   🔗 [docs.peewee-orm.com](http://docs.peewee-orm.com/)  \n\n25. \u003ca href=\"https://github.com/sqlalchemy/sqlalchemy\"\u003esqlalchemy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sqlalchemy/sqlalchemy\"\u003esqlalchemy\u003c/a\u003e\u003c/b\u003e ⭐ 11,420    \n   The Database Toolkit for Python  \n   🔗 [www.sqlalchemy.org](https://www.sqlalchemy.org)  \n\n26. \u003ca href=\"https://github.com/simonw/datasette\"\u003esimonw/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/simonw/datasette\"\u003edatasette\u003c/a\u003e\u003c/b\u003e ⭐ 10,709    \n   An open source multi-tool for exploring and publishing data  \n   🔗 [datasette.io](https://datasette.io)  \n\n27. \u003ca href=\"https://github.com/gristlabs/grist-core\"\u003egristlabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gristlabs/grist-core\"\u003egrist-core\u003c/a\u003e\u003c/b\u003e ⭐ 10,465    \n   Grist is the evolution of spreadsheets.  \n   🔗 [www.getgrist.com](https://www.getgrist.com)  \n\n28. \u003ca href=\"https://github.com/voxel51/fiftyone\"\u003evoxel51/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/voxel51/fiftyone\"\u003efiftyone\u003c/a\u003e\u003c/b\u003e ⭐ 10,272    \n   Refine high-quality datasets and visual AI models  \n   🔗 [fiftyone.ai](https://fiftyone.ai)  \n\n29. \u003ca href=\"https://github.com/bigscience-workshop/petals\"\u003ebigscience-workshop/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bigscience-workshop/petals\"\u003epetals\u003c/a\u003e\u003c/b\u003e ⭐ 9,880    \n   🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading  \n   🔗 [petals.dev](https://petals.dev)  \n\n30. \u003ca href=\"https://github.com/yzhao062/pyod\"\u003eyzhao062/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yzhao062/pyod\"\u003epyod\u003c/a\u003e\u003c/b\u003e ⭐ 9,685    \n   A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques  \n   🔗 [pyod.readthedocs.io](http://pyod.readthedocs.io)  \n\n31. \u003ca href=\"https://github.com/tobymao/sqlglot\"\u003etobymao/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tobymao/sqlglot\"\u003esqlglot\u003c/a\u003e\u003c/b\u003e ⭐ 8,843    \n   Python SQL Parser and Transpiler  \n   🔗 [sqlglot.com](https://sqlglot.com/)  \n\n32. \u003ca href=\"https://github.com/lancedb/lancedb\"\u003elancedb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lancedb/lancedb\"\u003elancedb\u003c/a\u003e\u003c/b\u003e ⭐ 8,604    \n   Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.  \n   🔗 [lancedb.com/docs](https://lancedb.com/docs)  \n\n33. \u003ca href=\"https://github.com/kaggle/kaggle-api\"\u003ekaggle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kaggle/kaggle-api\"\u003ekaggle-api\u003c/a\u003e\u003c/b\u003e ⭐ 7,106    \n   Official Kaggle API  \n\n34. \u003ca href=\"https://github.com/alirezamika/autoscraper\"\u003ealirezamika/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alirezamika/autoscraper\"\u003eautoscraper\u003c/a\u003e\u003c/b\u003e ⭐ 7,076    \n   A Smart, Automatic, Fast and Lightweight Web Scraper for Python  \n\n35. \u003ca href=\"https://github.com/ibis-project/ibis\"\u003eibis-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ibis-project/ibis\"\u003eibis\u003c/a\u003e\u003c/b\u003e ⭐ 6,360    \n   Ibis is a Python library that provides a lightweight, universal interface for data wrangling. It helps Python users explore and transform data of any size, stored anywhere.  \n   🔗 [ibis-project.org](https://ibis-project.org)  \n\n36. \u003ca href=\"https://github.com/madmaze/pytesseract\"\u003emadmaze/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/madmaze/pytesseract\"\u003epytesseract\u003c/a\u003e\u003c/b\u003e ⭐ 6,301    \n   A Python wrapper for Google Tesseract  \n\n37. \u003ca href=\"https://github.com/vi3k6i5/flashtext\"\u003evi3k6i5/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vi3k6i5/flashtext\"\u003eflashtext\u003c/a\u003e\u003c/b\u003e ⭐ 5,701    \n   Extract Keywords from sentence or Replace keywords in sentences.  \n\n38. \u003ca href=\"https://github.com/rapidai/rapidocr\"\u003erapidai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rapidai/rapidocr\"\u003eRapidOCR\u003c/a\u003e\u003c/b\u003e ⭐ 5,700    \n   📄 Awesome OCR multiple programing languages toolkits based on ONNXRuntime, OpenVINO, PaddlePaddle and PyTorch.  \n   🔗 [rapidai.github.io/rapidocrdocs](https://rapidai.github.io/RapidOCRDocs)  \n\n39. \u003ca href=\"https://github.com/airbnb/knowledge-repo\"\u003eairbnb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/airbnb/knowledge-repo\"\u003eknowledge-repo\u003c/a\u003e\u003c/b\u003e ⭐ 5,540    \n   A next-generation curated knowledge sharing platform for data scientists and other technical professions.  \n\n40. \u003ca href=\"https://github.com/superduperdb/superduperdb\"\u003esuperduperdb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/superduperdb/superduperdb\"\u003esuperduper\u003c/a\u003e\u003c/b\u003e ⭐ 5,251    \n   Superduper: End-to-end framework for building custom AI applications and agents.  \n   🔗 [superduper.io](https://superduper.io)  \n\n41. \u003ca href=\"https://github.com/adbar/trafilatura\"\u003eadbar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/adbar/trafilatura\"\u003etrafilatura\u003c/a\u003e\u003c/b\u003e ⭐ 5,219    \n   Python \u0026 Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML  \n   🔗 [trafilatura.readthedocs.io](https://trafilatura.readthedocs.io)  \n\n42. \u003ca href=\"https://github.com/giskard-ai/giskard\"\u003egiskard-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/giskard-ai/giskard\"\u003egiskard-oss\u003c/a\u003e\u003c/b\u003e ⭐ 5,083    \n   🐢 Open-Source Evaluation \u0026 Testing library for LLM Agents  \n   🔗 [docs.giskard.ai](https://docs.giskard.ai)  \n\n43. \u003ca href=\"https://github.com/facebookresearch/augly\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/augly\"\u003eAugLy\u003c/a\u003e\u003c/b\u003e ⭐ 5,072    \n   A data augmentations library for audio, image, text, and video.  \n   🔗 [ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models](https://ai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models/)  \n\n44. \u003ca href=\"https://github.com/dlt-hub/dlt\"\u003edlt-hub/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dlt-hub/dlt\"\u003edlt\u003c/a\u003e\u003c/b\u003e ⭐ 4,828    \n   data load tool (dlt) is an open source Python library that makes data loading easy 🛠️   \n   🔗 [dlthub.com/docs](https://dlthub.com/docs)  \n\n45. \u003ca href=\"https://github.com/lk-geimfari/mimesis\"\u003elk-geimfari/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lk-geimfari/mimesis\"\u003emimesis\u003c/a\u003e\u003c/b\u003e ⭐ 4,773    \n   Mimesis is a fast Python library for generating fake data in multiple languages.  \n   🔗 [mimesis.name](https://mimesis.name)  \n\n46. \u003ca href=\"https://github.com/jazzband/tablib\"\u003ejazzband/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jazzband/tablib\"\u003etablib\u003c/a\u003e\u003c/b\u003e ⭐ 4,753    \n   Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, \u0026c.  \n   🔗 [tablib.readthedocs.io](https://tablib.readthedocs.io/)  \n\n47. \u003ca href=\"https://github.com/amundsen-io/amundsen\"\u003eamundsen-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/amundsen-io/amundsen\"\u003eamundsen\u003c/a\u003e\u003c/b\u003e ⭐ 4,719    \n   Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.  \n   🔗 [www.amundsen.io/amundsen](https://www.amundsen.io/amundsen/)  \n\n48. \u003ca href=\"https://github.com/mangiucugna/json_repair\"\u003emangiucugna/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mangiucugna/json_repair\"\u003ejson_repair\u003c/a\u003e\u003c/b\u003e ⭐ 4,417    \n   A python module to repair invalid JSON from LLMs  \n   🔗 [pypi.org/project/json-repair](https://pypi.org/project/json-repair/)  \n\n49. \u003ca href=\"https://github.com/rom1504/img2dataset\"\u003erom1504/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rom1504/img2dataset\"\u003eimg2dataset\u003c/a\u003e\u003c/b\u003e ⭐ 4,345    \n   Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.  \n\n50. \u003ca href=\"https://github.com/mlabonne/llm-datasets\"\u003emlabonne/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlabonne/llm-datasets\"\u003ellm-datasets\u003c/a\u003e\u003c/b\u003e ⭐ 4,190    \n   Curated list of datasets and tools for post-training.  \n   🔗 [mlabonne.github.io/blog](https://mlabonne.github.io/blog)  \n\n51. \u003ca href=\"https://github.com/deepchecks/deepchecks\"\u003edeepchecks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepchecks/deepchecks\"\u003edeepchecks\u003c/a\u003e\u003c/b\u003e ⭐ 3,968    \n   Deepchecks: Tests for Continuous Validation of ML Models \u0026 Data. Deepchecks is a holistic open-source solution for all of your AI \u0026 ML validation needs, enabling to thoroughly test your data and models from research to production.  \n   🔗 [docs.deepchecks.com/stable](https://docs.deepchecks.com/stable)  \n\n52. \u003ca href=\"https://github.com/sqlalchemy/alembic\"\u003esqlalchemy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sqlalchemy/alembic\"\u003ealembic\u003c/a\u003e\u003c/b\u003e ⭐ 3,909    \n   A database migrations tool for SQLAlchemy.  \n\n53. \u003ca href=\"https://github.com/run-llama/llama-hub\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/llama-hub\"\u003ellama-hub\u003c/a\u003e\u003c/b\u003e ⭐ 3,483    \n   A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain  \n   🔗 [llamahub.ai](https://llamahub.ai/)  \n\n54. \u003ca href=\"https://github.com/sdv-dev/sdv\"\u003esdv-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sdv-dev/sdv\"\u003eSDV\u003c/a\u003e\u003c/b\u003e ⭐ 3,394    \n   Synthetic data generation for tabular data  \n   🔗 [docs.sdv.dev/sdv](https://docs.sdv.dev/sdv)  \n\n55. \u003ca href=\"https://github.com/docarray/docarray\"\u003edocarray/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/docarray/docarray\"\u003edocarray\u003c/a\u003e\u003c/b\u003e ⭐ 3,110    \n   Represent, send, store and search multimodal data  \n   🔗 [docs.docarray.org](https://docs.docarray.org/)  \n\n56. \u003ca href=\"https://github.com/datafold/data-diff\"\u003edatafold/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/datafold/data-diff\"\u003edata-diff\u003c/a\u003e\u003c/b\u003e ⭐ 2,992    \n   Compare tables within or across databases  \n   🔗 [docs.datafold.com](https://docs.datafold.com)  \n\n57. \u003ca href=\"https://github.com/huggingface/datatrove\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/datatrove\"\u003edatatrove\u003c/a\u003e\u003c/b\u003e ⭐ 2,848    \n   DataTrove is a library to process, filter and deduplicate text data at a very large scale. It provides a set of prebuilt commonly used processing blocks with a framework to easily add custom functionality  \n\n58. \u003ca href=\"https://github.com/pynamodb/pynamodb\"\u003epynamodb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pynamodb/pynamodb\"\u003ePynamoDB\u003c/a\u003e\u003c/b\u003e ⭐ 2,643    \n   A pythonic interface to Amazon's DynamoDB  \n   🔗 [pynamodb.readthedocs.io](http://pynamodb.readthedocs.io)  \n\n59. \u003ca href=\"https://github.com/aminalaee/sqladmin\"\u003eaminalaee/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aminalaee/sqladmin\"\u003esqladmin\u003c/a\u003e\u003c/b\u003e ⭐ 2,620    \n   SQLAlchemy Admin for FastAPI and Starlette  \n   🔗 [aminalaee.github.io/sqladmin](https://aminalaee.github.io/sqladmin/)  \n\n60. \u003ca href=\"https://github.com/pikepdf/pikepdf\"\u003epikepdf/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pikepdf/pikepdf\"\u003epikepdf\u003c/a\u003e\u003c/b\u003e ⭐ 2,616    \n   A Python library for reading and writing PDF, powered by QPDF  \n   🔗 [pikepdf.readthedocs.io](https://pikepdf.readthedocs.io/)  \n\n61. \u003ca href=\"https://github.com/sfu-db/connector-x\"\u003esfu-db/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sfu-db/connector-x\"\u003econnector-x\u003c/a\u003e\u003c/b\u003e ⭐ 2,537    \n   Fastest library to load data from DB to DataFrames in Rust and Python  \n   🔗 [sfu-db.github.io/connector-x](https://sfu-db.github.io/connector-x)  \n\n62. \u003ca href=\"https://github.com/uqfoundation/dill\"\u003euqfoundation/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/uqfoundation/dill\"\u003edill\u003c/a\u003e\u003c/b\u003e ⭐ 2,423    \n   serialize all of Python  \n   🔗 [dill.rtfd.io](http://dill.rtfd.io)  \n\n63. \u003ca href=\"https://github.com/milvus-io/bootcamp\"\u003emilvus-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/milvus-io/bootcamp\"\u003ebootcamp\u003c/a\u003e\u003c/b\u003e ⭐ 2,360    \n   Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.  \n   🔗 [milvus.io](https://milvus.io)  \n\n64. \u003ca href=\"https://github.com/emirozer/fake2db\"\u003eemirozer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/emirozer/fake2db\"\u003efake2db\u003c/a\u003e\u003c/b\u003e ⭐ 2,352    \n   Generate fake but valid data filled databases for test purposes using most popular patterns(AFAIK). Current support is sqlite, mysql, postgresql, mongodb, redis, couchdb.  \n\n65. \u003ca href=\"https://github.com/accenture/ampligraph\"\u003eaccenture/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/accenture/ampligraph\"\u003eAmpliGraph\u003c/a\u003e\u003c/b\u003e ⭐ 2,227    \n   Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org  \n\n66. \u003ca href=\"https://github.com/collerek/ormar\"\u003ecollerek/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/collerek/ormar\"\u003eormar\u003c/a\u003e\u003c/b\u003e ⭐ 1,794    \n   python async orm with fastapi in mind and pydantic validation  \n   🔗 [collerek.github.io/ormar](https://collerek.github.io/ormar/)  \n\n67. \u003ca href=\"https://github.com/huggingface/aisheets\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/aisheets\"\u003eaisheets\u003c/a\u003e\u003c/b\u003e ⭐ 1,621    \n   Build, enrich, and transform datasets using AI models with no code. Deploy locally or on the Hub with access to thousands of open models.  \n   🔗 [huggingface.co/spaces/aisheets/sheets](https://huggingface.co/spaces/aisheets/sheets)  \n\n68. \u003ca href=\"https://github.com/d-star-ai/dsrag\"\u003ed-star-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/d-star-ai/dsrag\"\u003edsRAG\u003c/a\u003e\u003c/b\u003e ⭐ 1,551    \n   A retrieval engine for unstructured data. It is especially good at handling challenging queries over dense text, like financial reports, legal documents, and academic papers.  \n\n69. \u003ca href=\"https://github.com/quixio/quix-streams\"\u003equixio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quixio/quix-streams\"\u003equix-streams\u003c/a\u003e\u003c/b\u003e ⭐ 1,513    \n   Python Streaming DataFrames for Kafka  \n   🔗 [docs.quix.io](https://docs.quix.io)  \n\n70. \u003ca href=\"https://github.com/meta-llama/synthetic-data-kit\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/synthetic-data-kit\"\u003esynthetic-data-kit\u003c/a\u003e\u003c/b\u003e ⭐ 1,474    \n   Tool for generating high-quality synthetic datasets to fine-tune LLMs. Generate Reasoning Traces, QA Pairs, save them to a fine-tuning format with a simple CLI.  \n   🔗 [pypi.org/project/synthetic-data-kit](https://pypi.org/project/synthetic-data-kit/)  \n\n71. \u003ca href=\"https://github.com/igorbenav/fastcrud\"\u003eigorbenav/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/igorbenav/fastcrud\"\u003efastcrud\u003c/a\u003e\u003c/b\u003e ⭐ 1,467    \n   FastCRUD is a Python package for FastAPI, offering robust async CRUD operations and flexible endpoint creation utilities.  \n   🔗 [benavlabs.github.io/fastcrud](https://benavlabs.github.io/fastcrud/)  \n\n72. \u003ca href=\"https://github.com/mchong6/jojogan\"\u003emchong6/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mchong6/jojogan\"\u003eJoJoGAN\u003c/a\u003e\u003c/b\u003e ⭐ 1,439    \n   Official PyTorch repo for JoJoGAN: One Shot Face Stylization  \n\n73. \u003ca href=\"https://github.com/apache/iceberg-python\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/iceberg-python\"\u003eiceberg-python\u003c/a\u003e\u003c/b\u003e ⭐ 984    \n   PyIceberg is a Python library for programmatic access to Iceberg table metadata as well as to table data in Iceberg format.  \n   🔗 [py.iceberg.apache.org](https://py.iceberg.apache.org/)  \n\n74. \u003ca href=\"https://github.com/weaviate/recipes\"\u003eweaviate/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/weaviate/recipes\"\u003erecipes\u003c/a\u003e\u003c/b\u003e ⭐ 934    \n   This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!  \n\n75. \u003ca href=\"https://github.com/ibm/data-prep-kit\"\u003eibm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ibm/data-prep-kit\"\u003edata-prep-kit\u003c/a\u003e\u003c/b\u003e ⭐ 892    \n   Data Prep Kit is a community project to democratize and accelerate unstructured data preparation for LLM app developers  \n   🔗 [data-prep-kit.github.io/data-prep-kit](https://data-prep-kit.github.io/data-prep-kit/)  \n\n76. \u003ca href=\"https://github.com/macbre/sql-metadata\"\u003emacbre/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/macbre/sql-metadata\"\u003esql-metadata\u003c/a\u003e\u003c/b\u003e ⭐ 879    \n   Uses tokenized query returned by python-sqlparse and generates query metadata  \n   🔗 [pypi.python.org/pypi/sql-metadata](https://pypi.python.org/pypi/sql-metadata)  \n\n77. \u003ca href=\"https://github.com/stackloklabs/promptwright\"\u003estackloklabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stackloklabs/promptwright\"\u003edeepfabric\u003c/a\u003e\u003c/b\u003e ⭐ 824    \n   Promptwright is a Python library designed for generating large synthetic datasets using LLMs  \n   🔗 [docs.deepfabric.dev](http://docs.deepfabric.dev)  \n\n78. \u003ca href=\"https://github.com/nvidia-nemo/datadesigner\"\u003envidia-nemo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia-nemo/datadesigner\"\u003eDataDesigner\u003c/a\u003e\u003c/b\u003e ⭐ 653    \n   Create synthetic datasets that go beyond simple LLM prompting. Covers diverse statistical distributions, meaningful correlations between fields, or validated high-quality outputs.  \n   🔗 [nvidia-nemo.github.io/datadesigner](https://nvidia-nemo.github.io/DataDesigner/)  \n\n79. \u003ca href=\"https://github.com/koaning/bulk\"\u003ekoaning/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/koaning/bulk\"\u003ebulk\u003c/a\u003e\u003c/b\u003e ⭐ 598    \n   Bulk is a quick UI developer tool to apply some bulk labels.  \n\n80. \u003ca href=\"https://github.com/titan-systems/titan\"\u003etitan-systems/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/titan-systems/titan\"\u003etitan\u003c/a\u003e\u003c/b\u003e ⭐ 478    \n   Snowflake infrastructure-as-code. Provision environments, automate deploys, CI/CD. Manage RBAC, users, roles, and data access. Declarative Python Resource API.  \n\n81. \u003ca href=\"https://github.com/pmgraham/datagrunt\"\u003epmgraham/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pmgraham/datagrunt\"\u003edatagrunt\u003c/a\u003e\u003c/b\u003e ⭐ 10    \n   Datagrunt is a Python library designed to simplify the way you work with CSV files. It provides a streamlined approach to reading, processing, and transforming your data into various formats, making data manipulation efficient and intuitive.  \n   🔗 [www.datagrunt.io](https://www.datagrunt.io)  \n\n## Debugging\n\nDebugging and tracing tools.  \n\n1. \u003ca href=\"https://github.com/cool-rr/pysnooper\"\u003ecool-rr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cool-rr/pysnooper\"\u003ePySnooper\u003c/a\u003e\u003c/b\u003e ⭐ 16,596    \n   Never use print for debugging again  \n\n2. \u003ca href=\"https://github.com/gruns/icecream\"\u003egruns/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gruns/icecream\"\u003eicecream\u003c/a\u003e\u003c/b\u003e ⭐ 10,003    \n   🍦 Never use print() to debug again.  \n\n3. \u003ca href=\"https://github.com/shobrook/rebound\"\u003eshobrook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shobrook/rebound\"\u003erebound\u003c/a\u003e\u003c/b\u003e ⭐ 4,135    \n   Instant Stack Overflow results whenever an exception is thrown  \n\n## Diffusion Text to Image\n\nText-to-image diffusion model libraries, tools and apps for generating images from natural language.  \n\n1. \u003ca href=\"https://github.com/automatic1111/stable-diffusion-webui\"\u003eautomatic1111/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/automatic1111/stable-diffusion-webui\"\u003estable-diffusion-webui\u003c/a\u003e\u003c/b\u003e ⭐ 160,173    \n   Stable Diffusion web UI  \n\n2. \u003ca href=\"https://github.com/comfyanonymous/comfyui\"\u003ecomfyanonymous/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/comfyanonymous/comfyui\"\u003eComfyUI\u003c/a\u003e\u003c/b\u003e ⭐ 101,251    \n   The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.  \n   🔗 [www.comfy.org](https://www.comfy.org/)  \n\n3. \u003ca href=\"https://github.com/compvis/stable-diffusion\"\u003ecompvis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/compvis/stable-diffusion\"\u003estable-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 72,246    \n   A latent text-to-image diffusion model  \n   🔗 [ommer-lab.com/research/latent-diffusion-models](https://ommer-lab.com/research/latent-diffusion-models/)  \n\n4. \u003ca href=\"https://github.com/lllyasviel/controlnet\"\u003elllyasviel/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lllyasviel/controlnet\"\u003eControlNet\u003c/a\u003e\u003c/b\u003e ⭐ 33,589    \n   Let us control diffusion models!  \n\n5. \u003ca href=\"https://github.com/huggingface/diffusers\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/diffusers\"\u003ediffusers\u003c/a\u003e\u003c/b\u003e ⭐ 32,569    \n   🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.  \n   🔗 [huggingface.co/docs/diffusers](https://huggingface.co/docs/diffusers)  \n\n6. \u003ca href=\"https://github.com/stability-ai/generative-models\"\u003estability-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stability-ai/generative-models\"\u003egenerative-models\u003c/a\u003e\u003c/b\u003e ⭐ 26,844    \n   Generative Models by Stability AI  \n\n7. \u003ca href=\"https://github.com/invoke-ai/invokeai\"\u003einvoke-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/invoke-ai/invokeai\"\u003eInvokeAI\u003c/a\u003e\u003c/b\u003e ⭐ 26,601    \n   Invoke is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, and serves as the foundation for multiple commercial products.  \n   🔗 [invoke-ai.github.io/invokeai](https://invoke-ai.github.io/InvokeAI/)  \n\n8. \u003ca href=\"https://github.com/openbmb/minicpm-v\"\u003eopenbmb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openbmb/minicpm-v\"\u003eMiniCPM-V\u003c/a\u003e\u003c/b\u003e ⭐ 22,680    \n   MiniCPM-V 4.5: A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone  \n\n9. \u003ca href=\"https://github.com/apple/ml-stable-diffusion\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/ml-stable-diffusion\"\u003eml-stable-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 17,782    \n   Stable Diffusion with Core ML on Apple Silicon  \n\n10. \u003ca href=\"https://github.com/borisdayma/dalle-mini\"\u003eborisdayma/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/borisdayma/dalle-mini\"\u003edalle-mini\u003c/a\u003e\u003c/b\u003e ⭐ 14,813    \n   DALL·E Mini - Generate images from a text prompt  \n   🔗 [www.craiyon.com](https://www.craiyon.com)  \n\n11. \u003ca href=\"https://github.com/compvis/latent-diffusion\"\u003ecompvis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/compvis/latent-diffusion\"\u003elatent-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 13,801    \n   High-Resolution Image Synthesis with Latent Diffusion Models  \n\n12. \u003ca href=\"https://github.com/divamgupta/diffusionbee-stable-diffusion-ui\"\u003edivamgupta/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/divamgupta/diffusionbee-stable-diffusion-ui\"\u003ediffusionbee-stable-diffusion-ui\u003c/a\u003e\u003c/b\u003e ⭐ 13,497    \n   Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.  \n   🔗 [diffusionbee.com](https://diffusionbee.com)  \n\n13. \u003ca href=\"https://github.com/facebookresearch/dinov2\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/dinov2\"\u003edinov2\u003c/a\u003e\u003c/b\u003e ⭐ 12,286    \n   PyTorch code and models for the DINOv2 self-supervised learning method.  \n\n14. \u003ca href=\"https://github.com/instantid/instantid\"\u003einstantid/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/instantid/instantid\"\u003eInstantID\u003c/a\u003e\u003c/b\u003e ⭐ 11,900    \n   InstantID: Zero-shot Identity-Preserving Generation in Seconds 🔥  \n   🔗 [instantid.github.io](https://instantid.github.io/)  \n\n15. \u003ca href=\"https://github.com/lucidrains/dalle2-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/dalle2-pytorch\"\u003eDALLE2-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 11,337    \n   Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network,  in Pytorch  \n\n16. \u003ca href=\"https://github.com/opengvlab/internvl\"\u003eopengvlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/opengvlab/internvl\"\u003eInternVL\u003c/a\u003e\u003c/b\u003e ⭐ 9,736    \n   [CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o.  接近GPT-4o表现的开源多模态对话模型  \n   🔗 [internvl.readthedocs.io/en/latest](https://internvl.readthedocs.io/en/latest/)  \n\n17. \u003ca href=\"https://github.com/idea-research/groundingdino\"\u003eidea-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/idea-research/groundingdino\"\u003eGroundingDINO\u003c/a\u003e\u003c/b\u003e ⭐ 9,626    \n   [ECCV 2024] Official implementation of the paper \"Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection\"  \n   🔗 [arxiv.org/abs/2303.05499](https://arxiv.org/abs/2303.05499)  \n\n18. \u003ca href=\"https://github.com/ashawkey/stable-dreamfusion\"\u003eashawkey/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ashawkey/stable-dreamfusion\"\u003estable-dreamfusion\u003c/a\u003e\u003c/b\u003e ⭐ 8,795    \n   Text-to-3D \u0026 Image-to-3D \u0026 Mesh Exportation with NeRF + Diffusion.  \n\n19. \u003ca href=\"https://github.com/carson-katri/dream-textures\"\u003ecarson-katri/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/carson-katri/dream-textures\"\u003edream-textures\u003c/a\u003e\u003c/b\u003e ⭐ 8,108    \n   Stable Diffusion built-in to Blender  \n\n20. \u003ca href=\"https://github.com/xavierxiao/dreambooth-stable-diffusion\"\u003exavierxiao/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xavierxiao/dreambooth-stable-diffusion\"\u003eDreambooth-Stable-Diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 7,754    \n   Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion  \n\n21. \u003ca href=\"https://github.com/timothybrooks/instruct-pix2pix\"\u003etimothybrooks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/timothybrooks/instruct-pix2pix\"\u003einstruct-pix2pix\u003c/a\u003e\u003c/b\u003e ⭐ 6,869    \n   PyTorch implementation of InstructPix2Pix, an instruction-based image editing model, based on the original CompVis/stable_diffusion repo.  \n\n22. \u003ca href=\"https://github.com/openai/consistency_models\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/consistency_models\"\u003econsistency_models\u003c/a\u003e\u003c/b\u003e ⭐ 6,469    \n   Official repo for consistency models.  \n\n23. \u003ca href=\"https://github.com/salesforce/blip\"\u003esalesforce/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforce/blip\"\u003eBLIP\u003c/a\u003e\u003c/b\u003e ⭐ 5,643    \n   PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation    \n\n24. \u003ca href=\"https://github.com/nateraw/stable-diffusion-videos\"\u003enateraw/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nateraw/stable-diffusion-videos\"\u003estable-diffusion-videos\u003c/a\u003e\u003c/b\u003e ⭐ 4,654    \n   Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts  \n\n25. \u003ca href=\"https://github.com/lkwq007/stablediffusion-infinity\"\u003elkwq007/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lkwq007/stablediffusion-infinity\"\u003establediffusion-infinity\u003c/a\u003e\u003c/b\u003e ⭐ 3,888    \n   Outpainting with Stable Diffusion on an infinite canvas  \n\n26. \u003ca href=\"https://github.com/jina-ai/discoart\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/discoart\"\u003ediscoart\u003c/a\u003e\u003c/b\u003e ⭐ 3,834    \n   🪩 Create Disco Diffusion artworks in one line  \n\n27. \u003ca href=\"https://github.com/openai/improved-diffusion\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/improved-diffusion\"\u003eimproved-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 3,783    \n   Release for Improved Denoising Diffusion Probabilistic Models  \n\n28. \u003ca href=\"https://github.com/open-compass/vlmevalkit\"\u003eopen-compass/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-compass/vlmevalkit\"\u003eVLMEvalKit\u003c/a\u003e\u003c/b\u003e ⭐ 3,741    \n   Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks  \n   🔗 [huggingface.co/spaces/opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard)  \n\n29. \u003ca href=\"https://github.com/mlc-ai/web-stable-diffusion\"\u003emlc-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlc-ai/web-stable-diffusion\"\u003eweb-stable-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 3,712    \n   Bringing stable diffusion models to web browsers. Everything runs inside the browser with no server support.   \n   🔗 [mlc.ai/web-stable-diffusion](https://mlc.ai/web-stable-diffusion)  \n\n30. \u003ca href=\"https://github.com/openai/glide-text2im\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/glide-text2im\"\u003eglide-text2im\u003c/a\u003e\u003c/b\u003e ⭐ 3,682    \n   GLIDE: a diffusion-based text-conditional image synthesis model  \n\n31. \u003ca href=\"https://github.com/google-research/big_vision\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/big_vision\"\u003ebig_vision\u003c/a\u003e\u003c/b\u003e ⭐ 3,333    \n   Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.  \n\n32. \u003ca href=\"https://github.com/saharmor/dalle-playground\"\u003esaharmor/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/saharmor/dalle-playground\"\u003edalle-playground\u003c/a\u003e\u003c/b\u003e ⭐ 2,749    \n   A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)  \n\n33. \u003ca href=\"https://github.com/stability-ai/stability-sdk\"\u003estability-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stability-ai/stability-sdk\"\u003estability-sdk\u003c/a\u003e\u003c/b\u003e ⭐ 2,434    \n   SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)  \n   🔗 [platform.stability.ai](https://platform.stability.ai/)  \n\n34. \u003ca href=\"https://github.com/thudm/cogvlm2\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/cogvlm2\"\u003eCogVLM2\u003c/a\u003e\u003c/b\u003e ⭐ 2,427    \n   GPT4V-level open-source multi-modal model based on Llama3-8B  \n\n35. \u003ca href=\"https://github.com/coyote-a/ultimate-upscale-for-automatic1111\"\u003ecoyote-a/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/coyote-a/ultimate-upscale-for-automatic1111\"\u003eultimate-upscale-for-automatic1111\u003c/a\u003e\u003c/b\u003e ⭐ 1,765    \n   Ultimate SD Upscale extension for AUTOMATIC1111 Stable Diffusion web UI  \n\n36. \u003ca href=\"https://github.com/divamgupta/stable-diffusion-tensorflow\"\u003edivamgupta/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/divamgupta/stable-diffusion-tensorflow\"\u003estable-diffusion-tensorflow\u003c/a\u003e\u003c/b\u003e ⭐ 1,613    \n   Stable Diffusion in TensorFlow / Keras  \n\n37. \u003ca href=\"https://github.com/nvlabs/prismer\"\u003envlabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvlabs/prismer\"\u003eprismer\u003c/a\u003e\u003c/b\u003e ⭐ 1,309    \n   The implementation of \"Prismer: A Vision-Language Model with Multi-Task Experts\".  \n   🔗 [shikun.io/projects/prismer](https://shikun.io/projects/prismer)  \n\n38. \u003ca href=\"https://github.com/chenyangqiqi/fatezero\"\u003echenyangqiqi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chenyangqiqi/fatezero\"\u003eFateZero\u003c/a\u003e\u003c/b\u003e ⭐ 1,159    \n   [ICCV 2023 Oral] \"FateZero: Fusing Attentions for Zero-shot Text-based Video Editing\"  \n   🔗 [fate-zero-edit.github.io](http://fate-zero-edit.github.io/)  \n\n39. \u003ca href=\"https://github.com/tanelp/tiny-diffusion\"\u003etanelp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tanelp/tiny-diffusion\"\u003etiny-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 978    \n   A minimal PyTorch implementation of probabilistic diffusion models for 2D datasets.  \n\n40. \u003ca href=\"https://github.com/gojasper/flash-diffusion\"\u003egojasper/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gojasper/flash-diffusion\"\u003eflash-diffusion\u003c/a\u003e\u003c/b\u003e ⭐ 650    \n   ⚡ Flash Diffusion ⚡: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation (AAAI 2025 Oral)  \n   🔗 [gojasper.github.io/flash-diffusion-project](https://gojasper.github.io/flash-diffusion-project/)  \n\n## Finance\n\nFinancial and quantitative libraries: investment research tools, market data, algorithmic trading, backtesting, financial derivatives.  \n\n1. \u003ca href=\"https://github.com/openbb-finance/openbbterminal\"\u003eopenbb-finance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openbb-finance/openbbterminal\"\u003eOpenBB\u003c/a\u003e\u003c/b\u003e ⭐ 59,275    \n   Financial data platform for analysts, quants and AI agents.  \n   🔗 [openbb.co](https://openbb.co)  \n\n2. \u003ca href=\"https://github.com/virattt/ai-hedge-fund\"\u003evirattt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/virattt/ai-hedge-fund\"\u003eai-hedge-fund\u003c/a\u003e\u003c/b\u003e ⭐ 45,480    \n   AI-powered hedge fund. The goal of this project is to explore the use of AI to make trading decisions.  \n\n3. \u003ca href=\"https://github.com/microsoft/qlib\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/qlib\"\u003eqlib\u003c/a\u003e\u003c/b\u003e ⭐ 35,948    \n   Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD...  \n   🔗 [qlib.readthedocs.io/en/latest](https://qlib.readthedocs.io/en/latest/)  \n\n4. \u003ca href=\"https://github.com/ranaroussi/yfinance\"\u003eranaroussi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ranaroussi/yfinance\"\u003eyfinance\u003c/a\u003e\u003c/b\u003e ⭐ 20,973    \n   Download market data from Yahoo! Finance's API  \n   🔗 [ranaroussi.github.io/yfinance](https://ranaroussi.github.io/yfinance)  \n\n5. \u003ca href=\"https://github.com/mementum/backtrader\"\u003emementum/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mementum/backtrader\"\u003ebacktrader\u003c/a\u003e\u003c/b\u003e ⭐ 20,206    \n   Python Backtesting library for trading strategies  \n   🔗 [www.backtrader.com](https://www.backtrader.com)  \n\n6. \u003ca href=\"https://github.com/quantopian/zipline\"\u003equantopian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantopian/zipline\"\u003ezipline\u003c/a\u003e\u003c/b\u003e ⭐ 19,357    \n   Zipline, a Pythonic Algorithmic Trading Library  \n   🔗 [www.zipline.io](https://www.zipline.io)  \n\n7. \u003ca href=\"https://github.com/ai4finance-foundation/fingpt\"\u003eai4finance-foundation/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ai4finance-foundation/fingpt\"\u003eFinGPT\u003c/a\u003e\u003c/b\u003e ⭐ 18,450    \n   FinGPT: Open-Source Financial Large Language Models!  Revolutionize 🔥    We release the trained model on HuggingFace.  \n   🔗 [ai4finance.org](https://ai4finance.org)  \n\n8. \u003ca href=\"https://github.com/quantconnect/lean\"\u003equantconnect/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantconnect/lean\"\u003eLean\u003c/a\u003e\u003c/b\u003e ⭐ 16,011    \n   Lean Algorithmic Trading Engine by QuantConnect (Python, C#)  \n   🔗 [lean.io](https://lean.io)  \n\n9. \u003ca href=\"https://github.com/ai4finance-foundation/finrl\"\u003eai4finance-foundation/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ai4finance-foundation/finrl\"\u003eFinRL\u003c/a\u003e\u003c/b\u003e ⭐ 13,790    \n   FinRL®:  Financial Reinforcement Learning. 🔥  \n   🔗 [ai4finance.org](https://ai4finance.org)  \n\n10. \u003ca href=\"https://github.com/ta-lib/ta-lib-python\"\u003eta-lib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ta-lib/ta-lib-python\"\u003eta-lib-python\u003c/a\u003e\u003c/b\u003e ⭐ 11,640    \n   Python wrapper for TA-Lib (http://ta-lib.org/).  \n   🔗 [ta-lib.github.io/ta-lib-python](http://ta-lib.github.io/ta-lib-python)  \n\n11. \u003ca href=\"https://github.com/shiyu-coder/kronos\"\u003eshiyu-coder/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shiyu-coder/kronos\"\u003eKronos\u003c/a\u003e\u003c/b\u003e ⭐ 10,153    \n   Open-source foundation model for financial candlesticks, trained on data from over 45 global exchanges  \n\n12. \u003ca href=\"https://github.com/goldmansachs/gs-quant\"\u003egoldmansachs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/goldmansachs/gs-quant\"\u003egs-quant\u003c/a\u003e\u003c/b\u003e ⭐ 9,845    \n   Python toolkit for quantitative finance  \n   🔗 [developer.gs.com/discover/products/gs-quant](https://developer.gs.com/discover/products/gs-quant/)  \n\n13. \u003ca href=\"https://github.com/kernc/backtesting.py\"\u003ekernc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kernc/backtesting.py\"\u003ebacktesting.py\u003c/a\u003e\u003c/b\u003e ⭐ 7,825    \n   🔎 📈 🐍 💰  Backtest trading strategies in Python.  \n   🔗 [kernc.github.io/backtesting.py](https://kernc.github.io/backtesting.py/)  \n\n14. \u003ca href=\"https://github.com/ranaroussi/quantstats\"\u003eranaroussi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ranaroussi/quantstats\"\u003equantstats\u003c/a\u003e\u003c/b\u003e ⭐ 6,619    \n   Portfolio analytics for quants, written in Python  \n\n15. \u003ca href=\"https://github.com/polakowo/vectorbt\"\u003epolakowo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/polakowo/vectorbt\"\u003evectorbt\u003c/a\u003e\u003c/b\u003e ⭐ 6,524    \n   Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.   \n   🔗 [vectorbt.dev](https://vectorbt.dev)  \n\n16. \u003ca href=\"https://github.com/quantopian/pyfolio\"\u003equantopian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantopian/pyfolio\"\u003epyfolio\u003c/a\u003e\u003c/b\u003e ⭐ 6,207    \n   Portfolio and risk analytics in Python  \n   🔗 [quantopian.github.io/pyfolio](https://quantopian.github.io/pyfolio)  \n\n17. \u003ca href=\"https://github.com/borisbanushev/stockpredictionai\"\u003eborisbanushev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/borisbanushev/stockpredictionai\"\u003estockpredictionai\u003c/a\u003e\u003c/b\u003e ⭐ 5,400    \n          In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Networ...  \n\n18. \u003ca href=\"https://github.com/google/tf-quant-finance\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/tf-quant-finance\"\u003etf-quant-finance\u003c/a\u003e\u003c/b\u003e ⭐ 5,194    \n   High-performance TensorFlow library for quantitative finance.  \n\n19. \u003ca href=\"https://github.com/gbeced/pyalgotrade\"\u003egbeced/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gbeced/pyalgotrade\"\u003epyalgotrade\u003c/a\u003e\u003c/b\u003e ⭐ 4,635    \n   Python Algorithmic Trading Library  \n   🔗 [gbeced.github.io/pyalgotrade](http://gbeced.github.io/pyalgotrade/)  \n\n20. \u003ca href=\"https://github.com/matplotlib/mplfinance\"\u003ematplotlib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/matplotlib/mplfinance\"\u003emplfinance\u003c/a\u003e\u003c/b\u003e ⭐ 4,272    \n   Financial Markets Data Visualization using Matplotlib  \n   🔗 [pypi.org/project/mplfinance](https://pypi.org/project/mplfinance/)  \n\n21. \u003ca href=\"https://github.com/quantopian/alphalens\"\u003equantopian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantopian/alphalens\"\u003ealphalens\u003c/a\u003e\u003c/b\u003e ⭐ 4,103    \n   Performance analysis of predictive (alpha) stock factors  \n   🔗 [quantopian.github.io/alphalens](http://quantopian.github.io/alphalens)  \n\n22. \u003ca href=\"https://github.com/zvtvz/zvt\"\u003ezvtvz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zvtvz/zvt\"\u003ezvt\u003c/a\u003e\u003c/b\u003e ⭐ 3,913    \n   modular quant framework.  \n   🔗 [zvt.readthedocs.io/en/latest](https://zvt.readthedocs.io/en/latest/)  \n\n23. \u003ca href=\"https://github.com/cuemacro/finmarketpy\"\u003ecuemacro/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cuemacro/finmarketpy\"\u003efinmarketpy\u003c/a\u003e\u003c/b\u003e ⭐ 3,701    \n   Python library for backtesting trading strategies \u0026 analyzing financial markets (formerly pythalesians)  \n   🔗 [www.cuemacro.com](http://www.cuemacro.com)  \n\n24. \u003ca href=\"https://github.com/domokane/financepy\"\u003edomokane/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/domokane/financepy\"\u003eFinancePy\u003c/a\u003e\u003c/b\u003e ⭐ 2,755    \n   A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.   \n\n25. \u003ca href=\"https://github.com/blankly-finance/blankly\"\u003eblankly-finance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blankly-finance/blankly\"\u003eblankly\u003c/a\u003e\u003c/b\u003e ⭐ 2,400    \n   🚀 💸  Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.  \n   🔗 [package.blankly.finance](https://package.blankly.finance)  \n\n26. \u003ca href=\"https://github.com/cuemacro/findatapy\"\u003ecuemacro/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cuemacro/findatapy\"\u003efindatapy\u003c/a\u003e\u003c/b\u003e ⭐ 1,968    \n   Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.  \n\n27. \u003ca href=\"https://github.com/ivebotunac/primoagent\"\u003eivebotunac/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ivebotunac/primoagent\"\u003ePrimoAgent\u003c/a\u003e\u003c/b\u003e ⭐ 268    \n   PrimoAgent is an multi agent AI stock analysis system built on LangGraph architecture that orchestrates four specialized agents to provide comprehensive daily trading insights and next-day price predictions  \n   🔗 [primoinvesting.com](https://primoinvesting.com/)  \n\n## Game Development\n\nGame development tools, engines and libraries.  \n\n1. \u003ca href=\"https://github.com/kitao/pyxel\"\u003ekitao/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kitao/pyxel\"\u003epyxel\u003c/a\u003e\u003c/b\u003e ⭐ 16,976    \n   A retro game engine for Python  \n\n2. \u003ca href=\"https://github.com/microsoft/trellis\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/trellis\"\u003eTRELLIS\u003c/a\u003e\u003c/b\u003e ⭐ 11,630    \n   A large 3D asset generation model. It takes in text or image prompts and generates high-quality 3D assets in various formats, such as Radiance Fields, 3D Gaussians, and meshes.  \n   🔗 [trellis3d.github.io](https://trellis3d.github.io)  \n\n3. \u003ca href=\"https://github.com/pygame/pygame\"\u003epygame/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pygame/pygame\"\u003epygame\u003c/a\u003e\u003c/b\u003e ⭐ 8,579    \n   🐍🎮 pygame (the library) is a Free and Open Source python programming language library for making multimedia applications like games built on top of the excellent SDL library. C, Python, Native, OpenGL.  \n   🔗 [www.pygame.org](https://www.pygame.org)  \n\n4. \u003ca href=\"https://github.com/panda3d/panda3d\"\u003epanda3d/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/panda3d/panda3d\"\u003epanda3d\u003c/a\u003e\u003c/b\u003e ⭐ 5,025    \n   Powerful, mature open-source cross-platform game engine for Python and C++, developed by Disney and CMU  \n   🔗 [www.panda3d.org](https://www.panda3d.org/)  \n\n5. \u003ca href=\"https://github.com/pyglet/pyglet\"\u003epyglet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyglet/pyglet\"\u003epyglet\u003c/a\u003e\u003c/b\u003e ⭐ 2,148    \n   pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications.  \n   🔗 [pyglet.org](http://pyglet.org)  \n\n6. \u003ca href=\"https://github.com/pythonarcade/arcade\"\u003epythonarcade/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pythonarcade/arcade\"\u003earcade\u003c/a\u003e\u003c/b\u003e ⭐ 1,959    \n   Easy to use Python library for creating 2D arcade games.  \n   🔗 [arcade.academy](http://arcade.academy)  \n\n## GIS\n\nGeospatial libraries: raster and vector data formats, interactive mapping and visualisation, computing frameworks for processing images, projections.  \n\n1. \u003ca href=\"https://github.com/domlysz/blendergis\"\u003edomlysz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/domlysz/blendergis\"\u003eBlenderGIS\u003c/a\u003e\u003c/b\u003e ⭐ 8,737    \n   Blender addons to make the bridge between Blender and geographic data  \n\n2. \u003ca href=\"https://github.com/python-visualization/folium\"\u003epython-visualization/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-visualization/folium\"\u003efolium\u003c/a\u003e\u003c/b\u003e ⭐ 7,308    \n   Python Data. Leaflet.js Maps.   \n   🔗 [python-visualization.github.io/folium](https://python-visualization.github.io/folium/)  \n\n3. \u003ca href=\"https://github.com/originalankur/maptoposter\"\u003eoriginalankur/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/originalankur/maptoposter\"\u003emaptoposter\u003c/a\u003e\u003c/b\u003e ⭐ 7,190    \n   Transform your favorite cities into beautiful, minimalist designs. MapToPoster lets you create and export visually striking map posters with code.  \n\n4. \u003ca href=\"https://github.com/osgeo/gdal\"\u003eosgeo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/osgeo/gdal\"\u003egdal\u003c/a\u003e\u003c/b\u003e ⭐ 5,729    \n   GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.  \n   🔗 [gdal.org](https://gdal.org)  \n\n5. \u003ca href=\"https://github.com/gboeing/osmnx\"\u003egboeing/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gboeing/osmnx\"\u003eosmnx\u003c/a\u003e\u003c/b\u003e ⭐ 5,549    \n   Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.  \n   🔗 [osmnx.readthedocs.io](https://osmnx.readthedocs.io)  \n\n6. \u003ca href=\"https://github.com/geopandas/geopandas\"\u003egeopandas/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/geopandas/geopandas\"\u003egeopandas\u003c/a\u003e\u003c/b\u003e ⭐ 5,027    \n   Python tools for geographic data  \n   🔗 [geopandas.org](http://geopandas.org/)  \n\n7. \u003ca href=\"https://github.com/shapely/shapely\"\u003eshapely/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shapely/shapely\"\u003eshapely\u003c/a\u003e\u003c/b\u003e ⭐ 4,358    \n   Manipulation and analysis of geometric objects  \n   🔗 [shapely.readthedocs.io/en/stable](https://shapely.readthedocs.io/en/stable/)  \n\n8. \u003ca href=\"https://github.com/opengeos/segment-geospatial\"\u003eopengeos/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/opengeos/segment-geospatial\"\u003esegment-geospatial\u003c/a\u003e\u003c/b\u003e ⭐ 3,863    \n   A Python package for segmenting geospatial data with the Segment Anything Model (SAM)  \n   🔗 [samgeo.gishub.org](https://samgeo.gishub.org)  \n\n9. \u003ca href=\"https://github.com/giswqs/geemap\"\u003egiswqs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/giswqs/geemap\"\u003egeemap\u003c/a\u003e\u003c/b\u003e ⭐ 3,856    \n   A Python package for interactive geospatial analysis and visualization with Google Earth Engine.  \n   🔗 [geemap.org](https://geemap.org)  \n\n10. \u003ca href=\"https://github.com/microsoft/torchgeo\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/torchgeo\"\u003etorchgeo\u003c/a\u003e\u003c/b\u003e ⭐ 3,841    \n   TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data  \n   🔗 [www.osgeo.org/projects/torchgeo](https://www.osgeo.org/projects/torchgeo/)  \n\n11. \u003ca href=\"https://github.com/opengeos/leafmap\"\u003eopengeos/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/opengeos/leafmap\"\u003eleafmap\u003c/a\u003e\u003c/b\u003e ⭐ 3,656    \n   A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment  \n   🔗 [leafmap.org](https://leafmap.org)  \n\n12. \u003ca href=\"https://github.com/holoviz/datashader\"\u003eholoviz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/holoviz/datashader\"\u003edatashader\u003c/a\u003e\u003c/b\u003e ⭐ 3,501    \n   Quickly and accurately render even the largest data.  \n   🔗 [datashader.org](http://datashader.org)  \n\n13. \u003ca href=\"https://github.com/rasterio/rasterio\"\u003erasterio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasterio/rasterio\"\u003erasterio\u003c/a\u003e\u003c/b\u003e ⭐ 2,474    \n   Rasterio reads and writes geospatial raster datasets  \n   🔗 [rasterio.readthedocs.io](https://rasterio.readthedocs.io/)  \n\n14. \u003ca href=\"https://github.com/plant99/felicette\"\u003eplant99/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plant99/felicette\"\u003efelicette\u003c/a\u003e\u003c/b\u003e ⭐ 1,831    \n   Satellite imagery for dummies.  \n\n15. \u003ca href=\"https://github.com/microsoft/globalmlbuildingfootprints\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/globalmlbuildingfootprints\"\u003eGlobalMLBuildingFootprints\u003c/a\u003e\u003c/b\u003e ⭐ 1,772    \n   Worldwide building footprints derived from satellite imagery   \n\n16. \u003ca href=\"https://github.com/pysal/pysal\"\u003epysal/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pysal/pysal\"\u003epysal\u003c/a\u003e\u003c/b\u003e ⭐ 1,458    \n   PySAL: Python Spatial Analysis Library Meta-Package  \n   🔗 [pysal.org/pysal](http://pysal.org/pysal)  \n\n17. \u003ca href=\"https://github.com/residentmario/geoplot\"\u003eresidentmario/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/residentmario/geoplot\"\u003egeoplot\u003c/a\u003e\u003c/b\u003e ⭐ 1,190    \n   High-level geospatial data visualization library for Python.  \n   🔗 [residentmario.github.io/geoplot/index.html](https://residentmario.github.io/geoplot/index.html)  \n\n## Graph\n\nGraphs and network libraries: network analysis, graph machine learning, visualisation.  \n\n1. \u003ca href=\"https://github.com/networkx/networkx\"\u003enetworkx/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/networkx/networkx\"\u003enetworkx\u003c/a\u003e\u003c/b\u003e ⭐ 16,546    \n   Network Analysis in Python  \n   🔗 [networkx.org](https://networkx.org)  \n\n2. \u003ca href=\"https://github.com/stellargraph/stellargraph\"\u003estellargraph/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stellargraph/stellargraph\"\u003estellargraph\u003c/a\u003e\u003c/b\u003e ⭐ 3,044    \n   StellarGraph - Machine Learning on Graphs  \n   🔗 [stellargraph.readthedocs.io](https://stellargraph.readthedocs.io/)  \n\n3. \u003ca href=\"https://github.com/microsoft/graspologic\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/graspologic\"\u003egraspologic\u003c/a\u003e\u003c/b\u003e ⭐ 961    \n   graspologic is a package for graph statistical algorithms  \n   🔗 [graspologic-org.github.io/graspologic](https://graspologic-org.github.io/graspologic/)  \n\n4. \u003ca href=\"https://github.com/dylanhogg/llmgraph\"\u003edylanhogg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dylanhogg/llmgraph\"\u003ellmgraph\u003c/a\u003e\u003c/b\u003e ⭐ 496    \n   Create knowledge graphs with LLMs  \n\n## GUI\n\nGraphical user interface libraries and toolkits.  \n\n1. \u003ca href=\"https://github.com/hoffstadt/dearpygui\"\u003ehoffstadt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hoffstadt/dearpygui\"\u003eDearPyGui\u003c/a\u003e\u003c/b\u003e ⭐ 15,133    \n   Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies  \n   🔗 [dearpygui.readthedocs.io/en/latest](https://dearpygui.readthedocs.io/en/latest/)  \n\n2. \u003ca href=\"https://github.com/pysimplegui/pysimplegui\"\u003epysimplegui/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pysimplegui/pysimplegui\"\u003ePySimpleGUI\u003c/a\u003e\u003c/b\u003e ⭐ 13,713    \n   Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.  \n   🔗 [www.pysimplegui.com](https://www.PySimpleGUI.com)  \n\n3. \u003ca href=\"https://github.com/parthjadhav/tkinter-designer\"\u003eparthjadhav/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/parthjadhav/tkinter-designer\"\u003eTkinter-Designer\u003c/a\u003e\u003c/b\u003e ⭐ 10,170    \n   An easy and fast way to create a Python GUI 🐍  \n\n4. \u003ca href=\"https://github.com/samuelcolvin/fastui\"\u003esamuelcolvin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/samuelcolvin/fastui\"\u003eFastUI\u003c/a\u003e\u003c/b\u003e ⭐ 8,947    \n   FastUI is a new way to build web application user interfaces defined by declarative Python code.  \n   🔗 [fastui-demo.onrender.com](https://fastui-demo.onrender.com)  \n\n5. \u003ca href=\"https://github.com/r0x0r/pywebview\"\u003er0x0r/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/r0x0r/pywebview\"\u003epywebview\u003c/a\u003e\u003c/b\u003e ⭐ 5,687    \n   Build GUI for your Python program with JavaScript, HTML, and CSS  \n   🔗 [pywebview.flowrl.com](https://pywebview.flowrl.com)  \n\n6. \u003ca href=\"https://github.com/beeware/toga\"\u003ebeeware/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/beeware/toga\"\u003etoga\u003c/a\u003e\u003c/b\u003e ⭐ 5,291    \n   A Python native, OS native GUI toolkit.  \n   🔗 [toga.readthedocs.io/en/latest](https://toga.readthedocs.io/en/latest/)  \n\n## Jupyter\n\nJupyter and JupyterLab and Notebook tools, libraries and plugins.  \n\n1. \u003ca href=\"https://github.com/marimo-team/marimo\"\u003emarimo-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marimo-team/marimo\"\u003emarimo\u003c/a\u003e\u003c/b\u003e ⭐ 18,645    \n   A reactive Python notebook: run a cell or interact with a UI element, and marimo automatically runs dependent cells, keeping code and outputs consistent. marimo notebooks are stored as pure Python, executable as scripts, and deployable as apps.  \n   🔗 [marimo.io](https://marimo.io)  \n\n2. \u003ca href=\"https://github.com/jupyterlab/jupyterlab\"\u003ejupyterlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab\"\u003ejupyterlab\u003c/a\u003e\u003c/b\u003e ⭐ 14,996    \n   JupyterLab computational environment.  \n   🔗 [jupyterlab.readthedocs.io](https://jupyterlab.readthedocs.io/)  \n\n3. \u003ca href=\"https://github.com/jupyter/notebook\"\u003ejupyter/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jupyter/notebook\"\u003enotebook\u003c/a\u003e\u003c/b\u003e ⭐ 12,908    \n   Jupyter Interactive Notebook  \n   🔗 [jupyter-notebook.readthedocs.io](https://jupyter-notebook.readthedocs.io/)  \n\n4. \u003ca href=\"https://github.com/garrettj403/scienceplots\"\u003egarrettj403/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/garrettj403/scienceplots\"\u003eSciencePlots\u003c/a\u003e\u003c/b\u003e ⭐ 8,518    \n   Matplotlib styles for scientific plotting  \n\n5. \u003ca href=\"https://github.com/mwouts/jupytext\"\u003emwouts/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mwouts/jupytext\"\u003ejupytext\u003c/a\u003e\u003c/b\u003e ⭐ 7,096    \n   Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts  \n   🔗 [jupytext.readthedocs.io](https://jupytext.readthedocs.io)  \n\n6. \u003ca href=\"https://github.com/nteract/papermill\"\u003enteract/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nteract/papermill\"\u003epapermill\u003c/a\u003e\u003c/b\u003e ⭐ 6,358    \n   📚 Parameterize, execute, and analyze notebooks  \n   🔗 [papermill.readthedocs.io/en/latest](http://papermill.readthedocs.io/en/latest/)  \n\n7. \u003ca href=\"https://github.com/voila-dashboards/voila\"\u003evoila-dashboards/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/voila-dashboards/voila\"\u003evoila\u003c/a\u003e\u003c/b\u003e ⭐ 5,886    \n   Voilà turns Jupyter notebooks into standalone web applications  \n   🔗 [voila.readthedocs.io](https://voila.readthedocs.io)  \n\n8. \u003ca href=\"https://github.com/connorferster/handcalcs\"\u003econnorferster/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/connorferster/handcalcs\"\u003ehandcalcs\u003c/a\u003e\u003c/b\u003e ⭐ 5,801    \n   Python library for converting Python calculations into rendered latex.  \n\n9. \u003ca href=\"https://github.com/jupyterlite/jupyterlite\"\u003ejupyterlite/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jupyterlite/jupyterlite\"\u003ejupyterlite\u003c/a\u003e\u003c/b\u003e ⭐ 4,738    \n   Wasm powered Jupyter running in the browser 💡  \n   🔗 [jupyterlite.rtfd.io/en/stable/try/lab](https://jupyterlite.rtfd.io/en/stable/try/lab)  \n\n10. \u003ca href=\"https://github.com/executablebooks/jupyter-book\"\u003eexecutablebooks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/executablebooks/jupyter-book\"\u003ejupyter-book\u003c/a\u003e\u003c/b\u003e ⭐ 4,207    \n   Create beautiful, publication-quality books and documents from computational content.  \n   🔗 [jupyterbook.org](https://jupyterbook.org)  \n\n11. \u003ca href=\"https://github.com/jupyterlab/jupyterlab-desktop\"\u003ejupyterlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jupyterlab/jupyterlab-desktop\"\u003ejupyterlab-desktop\u003c/a\u003e\u003c/b\u003e ⭐ 4,180    \n   JupyterLab desktop application, based on Electron.  \n\n12. \u003ca href=\"https://github.com/jupyterlab/jupyter-ai\"\u003ejupyterlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jupyterlab/jupyter-ai\"\u003ejupyter-ai\u003c/a\u003e\u003c/b\u003e ⭐ 4,094    \n   A generative AI extension for JupyterLab  \n   🔗 [jupyter-ai.readthedocs.io](https://jupyter-ai.readthedocs.io/)  \n\n13. \u003ca href=\"https://github.com/mito-ds/monorepo\"\u003emito-ds/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mito-ds/monorepo\"\u003emito\u003c/a\u003e\u003c/b\u003e ⭐ 2,609    \n   Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet  \n   🔗 [trymito.io](https://trymito.io)  \n\n14. \u003ca href=\"https://github.com/deepnote/deepnote\"\u003edeepnote/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepnote/deepnote\"\u003edeepnote\u003c/a\u003e\u003c/b\u003e ⭐ 2,585    \n   Deepnote is a successor of Jupyter. It uses the Deepnote kernel which is more powerful but still backwards compatible so you can seamlessly move between both, but adds an AI agent, sleek UI, new block types, and native data integrations.  \n   🔗 [deepnote.com/?utm_source=github\u0026utm_medium=github\u0026utm_campaign=github\u0026utm_content=readme_main](https://deepnote.com/?utm_source=github\u0026utm_medium=github\u0026utm_campaign=github\u0026utm_content=readme_main)  \n\n15. \u003ca href=\"https://github.com/koaning/drawdata\"\u003ekoaning/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/koaning/drawdata\"\u003edrawdata\u003c/a\u003e\u003c/b\u003e ⭐ 1,608    \n   Draw datasets from within Python notebooks.  \n   🔗 [koaning.github.io/drawdata](https://koaning.github.io/drawdata/)  \n\n16. \u003ca href=\"https://github.com/infuseai/colab-xterm\"\u003einfuseai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/infuseai/colab-xterm\"\u003ecolab-xterm\u003c/a\u003e\u003c/b\u003e ⭐ 477    \n   Open a terminal in colab, including the free tier.  \n\n## LLMs and ChatGPT\n\nLarge language model and GPT libraries and frameworks: auto-gpt, agents, QnA, chain-of-thought workflows, API integations. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#natural-language-processing\"\u003eNatural Language Processing\u003c/a\u003e category for crossover.  \n\n1. \u003ca href=\"https://github.com/significant-gravitas/autogpt\"\u003esignificant-gravitas/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/significant-gravitas/autogpt\"\u003eAutoGPT\u003c/a\u003e\u003c/b\u003e ⭐ 181,398    \n   AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.  \n   🔗 [agpt.co](https://agpt.co)  \n\n2. \u003ca href=\"https://github.com/open-webui/open-webui\"\u003eopen-webui/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-webui/open-webui\"\u003eopen-webui\u003c/a\u003e\u003c/b\u003e ⭐ 121,671    \n   Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with built-in inference engine for RAG  \n   🔗 [openwebui.com](https://openwebui.com)  \n\n3. \u003ca href=\"https://github.com/deepseek-ai/deepseek-v3\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/deepseek-v3\"\u003eDeepSeek-V3\u003c/a\u003e\u003c/b\u003e ⭐ 101,281    \n   A strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.  \n\n4. \u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003eggerganov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ggerganov/llama.cpp\"\u003ellama.cpp\u003c/a\u003e\u003c/b\u003e ⭐ 93,624    \n   LLM inference in C/C++  \n\n5. \u003ca href=\"https://github.com/nomic-ai/gpt4all\"\u003enomic-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nomic-ai/gpt4all\"\u003egpt4all\u003c/a\u003e\u003c/b\u003e ⭐ 77,052    \n   GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.  \n   🔗 [nomic.ai/gpt4all](https://nomic.ai/gpt4all)  \n\n6. \u003ca href=\"https://github.com/modelcontextprotocol/servers\"\u003emodelcontextprotocol/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modelcontextprotocol/servers\"\u003eservers\u003c/a\u003e\u003c/b\u003e ⭐ 77,017    \n   A collection of reference implementations for the Model Context Protocol (MCP), as well as references to community built servers  \n   🔗 [modelcontextprotocol.io](https://modelcontextprotocol.io)  \n\n7. \u003ca href=\"https://github.com/infiniflow/ragflow\"\u003einfiniflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/infiniflow/ragflow\"\u003eragflow\u003c/a\u003e\u003c/b\u003e ⭐ 72,047    \n   RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs  \n   🔗 [ragflow.io](https://ragflow.io)  \n\n8. \u003ca href=\"https://github.com/vllm-project/vllm\"\u003evllm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vllm-project/vllm\"\u003evllm\u003c/a\u003e\u003c/b\u003e ⭐ 68,388    \n   A high-throughput and memory-efficient inference and serving engine for LLMs  \n   🔗 [vllm.ai](https://vllm.ai)  \n\n9. \u003ca href=\"https://github.com/hiyouga/llama-factory\"\u003ehiyouga/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hiyouga/llama-factory\"\u003eLlamaFactory\u003c/a\u003e\u003c/b\u003e ⭐ 66,357    \n   Unified Efficient Fine-Tuning of 100+ LLMs \u0026 VLMs (ACL 2024)  \n   🔗 [llamafactory.readthedocs.io](https://llamafactory.readthedocs.io)  \n\n10. \u003ca href=\"https://github.com/xtekky/gpt4free\"\u003extekky/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xtekky/gpt4free\"\u003egpt4free\u003c/a\u003e\u003c/b\u003e ⭐ 65,698    \n   The official gpt4free repository | various collection of powerful language models | o4, o3 and deepseek r1, gpt-4.1, gemini 2.5  \n   🔗 [t.me/g4f_channel](https://t.me/g4f_channel)  \n\n11. \u003ca href=\"https://github.com/killianlucas/open-interpreter\"\u003ekillianlucas/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/killianlucas/open-interpreter\"\u003eopen-interpreter\u003c/a\u003e\u003c/b\u003e ⭐ 61,792    \n   A natural language interface for computers  \n   🔗 [openinterpreter.com](http://openinterpreter.com/)  \n\n12. \u003ca href=\"https://github.com/facebookresearch/llama\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/llama\"\u003ellama\u003c/a\u003e\u003c/b\u003e ⭐ 59,082    \n   Inference code for Llama models  \n\n13. \u003ca href=\"https://github.com/unclecode/crawl4ai\"\u003eunclecode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/unclecode/crawl4ai\"\u003ecrawl4ai\u003c/a\u003e\u003c/b\u003e ⭐ 58,929    \n   AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.  \n   🔗 [crawl4ai.com](https://crawl4ai.com)  \n\n14. \u003ca href=\"https://github.com/imartinez/privategpt\"\u003eimartinez/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/imartinez/privategpt\"\u003eprivate-gpt\u003c/a\u003e\u003c/b\u003e ⭐ 57,072    \n   Interact with your documents using the power of GPT, 100% privately, no data leaks  \n   🔗 [privategpt.dev](https://privategpt.dev)  \n\n15. \u003ca href=\"https://github.com/gpt-engineer-org/gpt-engineer\"\u003egpt-engineer-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gpt-engineer-org/gpt-engineer\"\u003egpt-engineer\u003c/a\u003e\u003c/b\u003e ⭐ 55,205    \n   CLI platform to experiment with codegen. Precursor to: https://lovable.dev  \n\n16. \u003ca href=\"https://github.com/pathwaycom/llm-app\"\u003epathwaycom/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pathwaycom/llm-app\"\u003ellm-app\u003c/a\u003e\u003c/b\u003e ⭐ 54,566    \n   Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.  \n   🔗 [pathway.com/developers/templates](https://pathway.com/developers/templates/)  \n\n17. \u003ca href=\"https://github.com/karpathy/nanogpt\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/nanogpt\"\u003enanoGPT\u003c/a\u003e\u003c/b\u003e ⭐ 52,283    \n   The simplest, fastest repository for training/finetuning medium-sized GPTs.  \n\n18. \u003ca href=\"https://github.com/xai-org/grok-1\"\u003exai-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xai-org/grok-1\"\u003egrok-1\u003c/a\u003e\u003c/b\u003e ⭐ 51,370    \n   This repository contains JAX example code for loading and running the Grok-1 open-weights model.  \n\n19. \u003ca href=\"https://github.com/unslothai/unsloth\"\u003eunslothai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/unslothai/unsloth\"\u003eunsloth\u003c/a\u003e\u003c/b\u003e ⭐ 51,098    \n   Fine-tuning \u0026 Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.  \n   🔗 [unsloth.ai/docs](https://unsloth.ai/docs)  \n\n20. \u003ca href=\"https://github.com/oobabooga/text-generation-webui\"\u003eoobabooga/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/oobabooga/text-generation-webui\"\u003etext-generation-webui\u003c/a\u003e\u003c/b\u003e ⭐ 45,923    \n   The definitive Web UI for local AI, with powerful features and easy setup.  \n   🔗 [oobabooga.gumroad.com/l/deep_reason](https://oobabooga.gumroad.com/l/deep_reason)  \n\n21. \u003ca href=\"https://github.com/hpcaitech/colossalai\"\u003ehpcaitech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hpcaitech/colossalai\"\u003eColossalAI\u003c/a\u003e\u003c/b\u003e ⭐ 41,330    \n   Making large AI models cheaper, faster and more accessible  \n   🔗 [www.colossalai.org](https://www.colossalai.org)  \n\n22. \u003ca href=\"https://github.com/thudm/chatglm-6b\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/chatglm-6b\"\u003eChatGLM-6B\u003c/a\u003e\u003c/b\u003e ⭐ 41,222    \n   ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型  \n\n23. \u003ca href=\"https://github.com/karpathy/nanochat\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/nanochat\"\u003enanochat\u003c/a\u003e\u003c/b\u003e ⭐ 40,732    \n   A full-stack implementation of an LLM like ChatGPT in a single, clean, minimal, hackable, dependency-lite codebase  \n\n24. \u003ca href=\"https://github.com/exo-explore/exo\"\u003eexo-explore/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/exo-explore/exo\"\u003eexo\u003c/a\u003e\u003c/b\u003e ⭐ 40,421    \n   Run your own AI cluster at home. Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi etc  \n\n25. \u003ca href=\"https://github.com/lm-sys/fastchat\"\u003elm-sys/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lm-sys/fastchat\"\u003eFastChat\u003c/a\u003e\u003c/b\u003e ⭐ 39,381    \n   An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.  \n\n26. \u003ca href=\"https://github.com/quivrhq/quivr\"\u003equivrhq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quivrhq/quivr\"\u003equivr\u003c/a\u003e\u003c/b\u003e ⭐ 38,879    \n   Opiniated RAG for integrating GenAI in your apps 🧠   Focus on your product rather than the RAG. Easy integration in existing products with customisation!  Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.   \n   🔗 [core.quivr.com](https://core.quivr.com)  \n\n27. \u003ca href=\"https://github.com/danielmiessler/fabric\"\u003edanielmiessler/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/danielmiessler/fabric\"\u003eFabric\u003c/a\u003e\u003c/b\u003e ⭐ 38,472    \n   Fabric is an open-source framework for augmenting humans using AI. It provides a modular system for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.  \n   🔗 [danielmiessler.com/p/fabric-origin-story](https://danielmiessler.com/p/fabric-origin-story)  \n\n28. \u003ca href=\"https://github.com/laion-ai/open-assistant\"\u003elaion-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/laion-ai/open-assistant\"\u003eOpen-Assistant\u003c/a\u003e\u003c/b\u003e ⭐ 37,468    \n   OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.  \n   🔗 [open-assistant.io](https://open-assistant.io)  \n\n29. \u003ca href=\"https://github.com/berriai/litellm\"\u003eberriai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/berriai/litellm\"\u003elitellm\u003c/a\u003e\u003c/b\u003e ⭐ 34,399    \n   Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]  \n   🔗 [docs.litellm.ai/docs](https://docs.litellm.ai/docs/)  \n\n30. \u003ca href=\"https://github.com/moymix/taskmatrix\"\u003emoymix/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/moymix/taskmatrix\"\u003eTaskMatrix\u003c/a\u003e\u003c/b\u003e ⭐ 34,284    \n   Connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.  \n\n31. \u003ca href=\"https://github.com/pythagora-io/gpt-pilot\"\u003epythagora-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pythagora-io/gpt-pilot\"\u003egpt-pilot\u003c/a\u003e\u003c/b\u003e ⭐ 33,749    \n   The first real AI developer  \n\n32. \u003ca href=\"https://github.com/khoj-ai/khoj\"\u003ekhoj-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/khoj-ai/khoj\"\u003ekhoj\u003c/a\u003e\u003c/b\u003e ⭐ 32,272    \n   Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI  \n   🔗 [khoj.dev](https://khoj.dev)  \n\n33. \u003ca href=\"https://github.com/stanfordnlp/dspy\"\u003estanfordnlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stanfordnlp/dspy\"\u003edspy\u003c/a\u003e\u003c/b\u003e ⭐ 31,759    \n   DSPy: The framework for programming—not prompting—language models  \n   🔗 [dspy.ai](https://dspy.ai)  \n\n34. \u003ca href=\"https://github.com/anthropics/anthropic-cookbook\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/anthropic-cookbook\"\u003eclaude-cookbooks\u003c/a\u003e\u003c/b\u003e ⭐ 31,709    \n   Provides code and guides designed to help developers build with Claude, offering copy-able code snippets that you can easily integrate into your own projects.  \n\n35. \u003ca href=\"https://github.com/microsoft/graphrag\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/graphrag\"\u003egraphrag\u003c/a\u003e\u003c/b\u003e ⭐ 30,500    \n   A modular graph-based Retrieval-Augmented Generation (RAG) system  \n   🔗 [microsoft.github.io/graphrag](https://microsoft.github.io/graphrag/)  \n\n36. \u003ca href=\"https://github.com/tatsu-lab/stanford_alpaca\"\u003etatsu-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tatsu-lab/stanford_alpaca\"\u003estanford_alpaca\u003c/a\u003e\u003c/b\u003e ⭐ 30,273    \n   Code and documentation to train Stanford's Alpaca models, and generate the data.  \n   🔗 [crfm.stanford.edu/2023/03/13/alpaca.html](https://crfm.stanford.edu/2023/03/13/alpaca.html)  \n\n37. \u003ca href=\"https://github.com/meta-llama/llama3\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/llama3\"\u003ellama3\u003c/a\u003e\u003c/b\u003e ⭐ 29,193    \n   The official Meta Llama 3 GitHub site  \n\n38. \u003ca href=\"https://github.com/karpathy/llm.c\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/llm.c\"\u003ellm.c\u003c/a\u003e\u003c/b\u003e ⭐ 28,693    \n   LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython  \n\n39. \u003ca href=\"https://github.com/microsoft/semantic-kernel\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/semantic-kernel\"\u003esemantic-kernel\u003c/a\u003e\u003c/b\u003e ⭐ 27,079    \n   An SDK that integrates LLMs like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java  \n   🔗 [aka.ms/semantic-kernel](https://aka.ms/semantic-kernel)  \n\n40. \u003ca href=\"https://github.com/qwenlm/qwen3\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen3\"\u003eQwen3\u003c/a\u003e\u003c/b\u003e ⭐ 26,271    \n   Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.  \n\n41. \u003ca href=\"https://github.com/huggingface/open-r1\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/open-r1\"\u003eopen-r1\u003c/a\u003e\u003c/b\u003e ⭐ 25,840    \n   The goal of this repo is to build the missing pieces of the R1 pipeline such that everybody can reproduce and build on top of it  \n\n42. \u003ca href=\"https://github.com/microsoft/bitnet\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/bitnet\"\u003eBitNet\u003c/a\u003e\u003c/b\u003e ⭐ 25,810    \n   Official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels, that support fast and lossless inference of 1.58-bit models  \n\n43. \u003ca href=\"https://github.com/vision-cair/minigpt-4\"\u003evision-cair/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vision-cair/minigpt-4\"\u003eMiniGPT-4\u003c/a\u003e\u003c/b\u003e ⭐ 25,765    \n   Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)  \n   🔗 [minigpt-4.github.io](https://minigpt-4.github.io)  \n\n44. \u003ca href=\"https://github.com/cinnamon/kotaemon\"\u003ecinnamon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cinnamon/kotaemon\"\u003ekotaemon\u003c/a\u003e\u003c/b\u003e ⭐ 24,865    \n   An open-source RAG UI for chatting with your documents. Built with both end users and developers in mind  \n   🔗 [cinnamon.github.io/kotaemon](https://cinnamon.github.io/kotaemon/)  \n\n45. \u003ca href=\"https://github.com/openai/gpt-2\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/gpt-2\"\u003egpt-2\u003c/a\u003e\u003c/b\u003e ⭐ 24,567    \n   Code for the paper \"Language Models are Unsupervised Multitask Learners\"  \n   🔗 [openai.com/blog/better-language-models](https://openai.com/blog/better-language-models/)  \n\n46. \u003ca href=\"https://github.com/microsoft/jarvis\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/jarvis\"\u003eJARVIS\u003c/a\u003e\u003c/b\u003e ⭐ 24,513    \n   JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf  \n\n47. \u003ca href=\"https://github.com/haotian-liu/llava\"\u003ehaotian-liu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/haotian-liu/llava\"\u003eLLaVA\u003c/a\u003e\u003c/b\u003e ⭐ 24,364    \n   [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.  \n   🔗 [llava.hliu.cc](https://llava.hliu.cc)  \n\n48. \u003ca href=\"https://github.com/nirdiamant/rag_techniques\"\u003enirdiamant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nirdiamant/rag_techniques\"\u003eRAG_Techniques\u003c/a\u003e\u003c/b\u003e ⭐ 24,339    \n   The most comprehensive and dynamic collections of Retrieval-Augmented Generation (RAG) tutorials available today. This repository serves as a hub for cutting-edge techniques aimed at enhancing the accuracy, efficiency, and contextual richness of RAG systems.  \n\n49. \u003ca href=\"https://github.com/deepset-ai/haystack\"\u003edeepset-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepset-ai/haystack\"\u003ehaystack\u003c/a\u003e\u003c/b\u003e ⭐ 23,959    \n   AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversatio...  \n   🔗 [haystack.deepset.ai](https://haystack.deepset.ai)  \n\n50. \u003ca href=\"https://github.com/google/langextract\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/langextract\"\u003elangextract\u003c/a\u003e\u003c/b\u003e ⭐ 23,629    \n   Library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions  \n   🔗 [pypi.org/project/langextract](https://pypi.org/project/langextract/)  \n\n51. \u003ca href=\"https://github.com/karpathy/mingpt\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/mingpt\"\u003eminGPT\u003c/a\u003e\u003c/b\u003e ⭐ 23,333    \n   A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training  \n\n52. \u003ca href=\"https://github.com/sgl-project/sglang\"\u003esgl-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sgl-project/sglang\"\u003esglang\u003c/a\u003e\u003c/b\u003e ⭐ 22,668    \n   SGLang is a high-performance serving framework for large language models and multimodal models.  \n   🔗 [sglang.io](https://sglang.io)  \n\n53. \u003ca href=\"https://github.com/vanna-ai/vanna\"\u003evanna-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vanna-ai/vanna\"\u003evanna\u003c/a\u003e\u003c/b\u003e ⭐ 22,377    \n   RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality.  \n   🔗 [vanna.ai/docs](https://vanna.ai/docs/)  \n\n54. \u003ca href=\"https://github.com/mlc-ai/mlc-llm\"\u003emlc-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlc-ai/mlc-llm\"\u003emlc-llm\u003c/a\u003e\u003c/b\u003e ⭐ 21,931    \n   Universal LLM Deployment Engine with ML Compilation  \n   🔗 [llm.mlc.ai](https://llm.mlc.ai/)  \n\n55. \u003ca href=\"https://github.com/dao-ailab/flash-attention\"\u003edao-ailab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dao-ailab/flash-attention\"\u003eflash-attention\u003c/a\u003e\u003c/b\u003e ⭐ 21,806    \n   Fast and memory-efficient exact attention  \n\n56. \u003ca href=\"https://github.com/modelcontextprotocol/python-sdk\"\u003emodelcontextprotocol/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modelcontextprotocol/python-sdk\"\u003epython-sdk\u003c/a\u003e\u003c/b\u003e ⭐ 21,299    \n   The Model Context Protocol allows applications to provide context for LLMs in a standardized way, separating the concerns of providing context from the actual LLM interaction.  \n   🔗 [modelcontextprotocol.github.io/python-sdk](https://modelcontextprotocol.github.io/python-sdk/)  \n\n57. \u003ca href=\"https://github.com/openai/chatgpt-retrieval-plugin\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/chatgpt-retrieval-plugin\"\u003echatgpt-retrieval-plugin\u003c/a\u003e\u003c/b\u003e ⭐ 21,229    \n   The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.  \n\n58. \u003ca href=\"https://github.com/guidance-ai/guidance\"\u003eguidance-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/guidance-ai/guidance\"\u003eguidance\u003c/a\u003e\u003c/b\u003e ⭐ 21,221    \n   A guidance language for controlling large language models.  \n\n59. \u003ca href=\"https://github.com/rasahq/rasa\"\u003erasahq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasahq/rasa\"\u003erasa\u003c/a\u003e\u003c/b\u003e ⭐ 20,993    \n   💬   Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants  \n   🔗 [rasa.com/docs/rasa](https://rasa.com/docs/rasa/)  \n\n60. \u003ca href=\"https://github.com/huggingface/peft\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/peft\"\u003epeft\u003c/a\u003e\u003c/b\u003e ⭐ 20,514    \n   🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.  \n   🔗 [huggingface.co/docs/peft](https://huggingface.co/docs/peft)  \n\n61. \u003ca href=\"https://github.com/qwenlm/qwen\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen\"\u003eQwen\u003c/a\u003e\u003c/b\u003e ⭐ 20,214    \n   The official repo of Qwen (通义千问) chat \u0026 pretrained large language model proposed by Alibaba Cloud.  \n\n62. \u003ca href=\"https://github.com/skyvern-ai/skyvern\"\u003eskyvern-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/skyvern-ai/skyvern\"\u003eskyvern\u003c/a\u003e\u003c/b\u003e ⭐ 20,197    \n   Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions.  \n   🔗 [www.skyvern.com](https://www.skyvern.com)  \n\n63. \u003ca href=\"https://github.com/stitionai/devika\"\u003estitionai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stitionai/devika\"\u003edevika\u003c/a\u003e\u003c/b\u003e ⭐ 19,489    \n   Devika is an advanced AI software engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective.  \n   🔗 [opcode.sh](https://opcode.sh)  \n\n64. \u003ca href=\"https://github.com/karpathy/llama2.c\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/llama2.c\"\u003ellama2.c\u003c/a\u003e\u003c/b\u003e ⭐ 19,130    \n   Inference Llama 2 in one file of pure C  \n\n65. \u003ca href=\"https://github.com/tloen/alpaca-lora\"\u003etloen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tloen/alpaca-lora\"\u003ealpaca-lora\u003c/a\u003e\u003c/b\u003e ⭐ 18,980    \n   Instruct-tune LLaMA on consumer hardware  \n\n66. \u003ca href=\"https://github.com/volcengine/verl\"\u003evolcengine/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/volcengine/verl\"\u003everl\u003c/a\u003e\u003c/b\u003e ⭐ 18,644    \n   veRL is a flexible, efficient and production-ready RL training library for large language models (LLMs).  \n   🔗 [verl.readthedocs.io/en/latest/index.html](https://verl.readthedocs.io/en/latest/index.html)  \n\n67. \u003ca href=\"https://github.com/facebookresearch/llama-recipes\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/llama-recipes\"\u003ellama-cookbook\u003c/a\u003e\u003c/b\u003e ⭐ 18,166    \n   Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services    \n   🔗 [www.llama.com](https://www.llama.com/)  \n\n68. \u003ca href=\"https://github.com/openai/evals\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/evals\"\u003eevals\u003c/a\u003e\u003c/b\u003e ⭐ 17,586    \n   Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.  \n\n69. \u003ca href=\"https://github.com/idea-research/grounded-segment-anything\"\u003eidea-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/idea-research/grounded-segment-anything\"\u003eGrounded-Segment-Anything\u003c/a\u003e\u003c/b\u003e ⭐ 17,359    \n   Grounded SAM: Marrying Grounding DINO with Segment Anything \u0026 Stable Diffusion \u0026 Recognize Anything - Automatically Detect , Segment and Generate Anything  \n   🔗 [arxiv.org/abs/2401.14159](https://arxiv.org/abs/2401.14159)  \n\n70. \u003ca href=\"https://github.com/mlc-ai/web-llm\"\u003emlc-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlc-ai/web-llm\"\u003eweb-llm\u003c/a\u003e\u003c/b\u003e ⭐ 17,171    \n   High-performance In-browser LLM Inference Engine   \n   🔗 [webllm.mlc.ai](https://webllm.mlc.ai)  \n\n71. \u003ca href=\"https://github.com/transformeroptimus/superagi\"\u003etransformeroptimus/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/transformeroptimus/superagi\"\u003eSuperAGI\u003c/a\u003e\u003c/b\u003e ⭐ 17,105    \n   \u003c⚡️\u003e SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage \u0026 run useful autonomous agents quickly and reliably.  \n   🔗 [superagi.com](https://superagi.com/)  \n\n72. \u003ca href=\"https://github.com/kvcache-ai/ktransformers\"\u003ekvcache-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kvcache-ai/ktransformers\"\u003ektransformers\u003c/a\u003e\u003c/b\u003e ⭐ 16,385    \n   A Flexible Framework for LLM Inference Optimizations - allows researchers to replace original torch modules with optimized variants  \n   🔗 [kvcache-ai.github.io/ktransformers](https://kvcache-ai.github.io/ktransformers/)  \n\n73. \u003ca href=\"https://github.com/facebookresearch/codellama\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/codellama\"\u003ecodellama\u003c/a\u003e\u003c/b\u003e ⭐ 16,357    \n   Inference code for CodeLlama models  \n\n74. \u003ca href=\"https://github.com/mayooear/gpt4-pdf-chatbot-langchain\"\u003emayooear/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mayooear/gpt4-pdf-chatbot-langchain\"\u003eai-pdf-chatbot-langchain\u003c/a\u003e\u003c/b\u003e ⭐ 16,314    \n   AI PDF chatbot agent built with LangChain \u0026 LangGraph   \n   🔗 [www.youtube.com/watch?v=of6soldiewu](https://www.youtube.com/watch?v=OF6SolDiEwU)  \n\n75. \u003ca href=\"https://github.com/thudm/chatglm2-6b\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/chatglm2-6b\"\u003eChatGLM2-6B\u003c/a\u003e\u003c/b\u003e ⭐ 15,680    \n   ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型  \n\n76. \u003ca href=\"https://github.com/nvidia/megatron-lm\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/megatron-lm\"\u003eMegatron-LM\u003c/a\u003e\u003c/b\u003e ⭐ 14,998    \n   Ongoing research training transformer models at scale  \n   🔗 [docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html)  \n\n77. \u003ca href=\"https://github.com/qwenlm/qwen3-coder\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen3-coder\"\u003eQwen3-Coder\u003c/a\u003e\u003c/b\u003e ⭐ 14,967    \n   Qwen3-Coder is the code version of Qwen3, the large language model series developed by Qwen team, Alibaba Cloud.  \n\n78. \u003ca href=\"https://github.com/fauxpilot/fauxpilot\"\u003efauxpilot/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fauxpilot/fauxpilot\"\u003efauxpilot\u003c/a\u003e\u003c/b\u003e ⭐ 14,759    \n   FauxPilot - an open-source alternative to GitHub Copilot server  \n\n79. \u003ca href=\"https://github.com/llmware-ai/llmware\"\u003ellmware-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/llmware-ai/llmware\"\u003ellmware\u003c/a\u003e\u003c/b\u003e ⭐ 14,460    \n   Unified framework for building enterprise RAG pipelines with small, specialized models  \n   🔗 [llmware-ai.github.io/llmware](https://llmware-ai.github.io/llmware/)  \n\n80. \u003ca href=\"https://github.com/blinkdl/rwkv-lm\"\u003eblinkdl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blinkdl/rwkv-lm\"\u003eRWKV-LM\u003c/a\u003e\u003c/b\u003e ⭐ 14,321    \n   RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 \"Goose\". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and f...  \n\n81. \u003ca href=\"https://github.com/swivid/f5-tts\"\u003eswivid/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/swivid/f5-tts\"\u003eF5-TTS\u003c/a\u003e\u003c/b\u003e ⭐ 13,999    \n   Official code for \"F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching\"  \n   🔗 [arxiv.org/abs/2410.06885](https://arxiv.org/abs/2410.06885)  \n\n82. \u003ca href=\"https://github.com/karpathy/llm-council\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/llm-council\"\u003ellm-council\u003c/a\u003e\u003c/b\u003e ⭐ 13,742    \n   LLM Council works together to answer your hardest questions  \n\n83. \u003ca href=\"https://github.com/anthropics/anthropic-quickstarts\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/anthropic-quickstarts\"\u003eclaude-quickstarts\u003c/a\u003e\u003c/b\u003e ⭐ 13,639    \n   A collection of projects designed to help developers quickly get started with building applications using the Anthropic API. Each quickstart provides a foundation that you can easily build upon and customize for your specific needs.  \n\n84. \u003ca href=\"https://github.com/canner/wrenai\"\u003ecanner/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/canner/wrenai\"\u003eWrenAI\u003c/a\u003e\u003c/b\u003e ⭐ 13,527    \n   Open-source GenBI AI Agent that empowers data-driven teams to chat with their data to generate Text-to-SQL, charts, spreadsheets, reports, and BI.  \n   🔗 [getwren.ai/oss](https://getwren.ai/oss)  \n\n85. \u003ca href=\"https://github.com/andrewyng/aisuite\"\u003eandrewyng/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/andrewyng/aisuite\"\u003eaisuite\u003c/a\u003e\u003c/b\u003e ⭐ 13,384    \n   Simple, unified interface to multiple Generative AI providers. aisuite makes it easy for developers to use multiple LLM through a standardized interface.  \n\n86. \u003ca href=\"https://github.com/dottxt-ai/outlines\"\u003edottxt-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dottxt-ai/outlines\"\u003eoutlines\u003c/a\u003e\u003c/b\u003e ⭐ 13,289    \n   Structured Text Generation from LLMs  \n   🔗 [dottxt-ai.github.io/outlines](https://dottxt-ai.github.io/outlines/)  \n\n87. \u003ca href=\"https://github.com/microsoft/lora\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/lora\"\u003eLoRA\u003c/a\u003e\u003c/b\u003e ⭐ 13,196    \n   Code for loralib, an implementation of \"LoRA: Low-Rank Adaptation of Large Language Models\"  \n   🔗 [arxiv.org/abs/2106.09685](https://arxiv.org/abs/2106.09685)  \n\n88. \u003ca href=\"https://github.com/lightning-ai/lit-gpt\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/lit-gpt\"\u003elitgpt\u003c/a\u003e\u003c/b\u003e ⭐ 13,116    \n   20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.  \n   🔗 [lightning.ai](https://lightning.ai)  \n\n89. \u003ca href=\"https://github.com/lightning-ai/litgpt\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/litgpt\"\u003elitgpt\u003c/a\u003e\u003c/b\u003e ⭐ 13,116    \n   20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.  \n   🔗 [lightning.ai](https://lightning.ai)  \n\n90. \u003ca href=\"https://github.com/qwenlm/qwen-agent\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen-agent\"\u003eQwen-Agent\u003c/a\u003e\u003c/b\u003e ⭐ 13,021    \n   Agent framework and applications built upon Qwen\u003e=3.0, featuring Function Calling, MCP, Code Interpreter, RAG, Chrome extension, etc.  \n   🔗 [pypi.org/project/qwen-agent](https://pypi.org/project/qwen-agent/)  \n\n91. \u003ca href=\"https://github.com/paddlepaddle/paddlenlp\"\u003epaddlepaddle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/paddlepaddle/paddlenlp\"\u003ePaddleNLP\u003c/a\u003e\u003c/b\u003e ⭐ 12,904    \n   Easy-to-use and powerful LLM and SLM library with awesome model zoo.  \n   🔗 [paddlenlp.readthedocs.io](https://paddlenlp.readthedocs.io)  \n\n92. \u003ca href=\"https://github.com/shishirpatil/gorilla\"\u003eshishirpatil/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shishirpatil/gorilla\"\u003egorilla\u003c/a\u003e\u003c/b\u003e ⭐ 12,697    \n   Enables LLMs to use tools by invoking APIs. Given a query, Gorilla comes up with the semantically and syntactically correct API.  \n   🔗 [gorilla.cs.berkeley.edu](https://gorilla.cs.berkeley.edu/)  \n\n93. \u003ca href=\"https://github.com/jiayi-pan/tinyzero\"\u003ejiayi-pan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jiayi-pan/tinyzero\"\u003eTinyZero\u003c/a\u003e\u003c/b\u003e ⭐ 12,624    \n   TinyZero is a reproduction of DeepSeek R1 Zero in countdown and multiplication tasks.  \n\n94. \u003ca href=\"https://github.com/explodinggradients/ragas\"\u003eexplodinggradients/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/explodinggradients/ragas\"\u003eragas\u003c/a\u003e\u003c/b\u003e ⭐ 12,352    \n   Supercharge Your LLM Application Evaluations 🚀  \n   🔗 [docs.ragas.io](https://docs.ragas.io)  \n\n95. \u003ca href=\"https://github.com/modelscope/ms-swift\"\u003emodelscope/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modelscope/ms-swift\"\u003ems-swift\u003c/a\u003e\u003c/b\u003e ⭐ 12,316    \n   Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3, Qwen3-MoE, DeepSeek-R1, GLM4.5, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Llava, Phi4, ...) (AAAI 2025).  \n   🔗 [swift.readthedocs.io/zh-cn/v3.12](https://swift.readthedocs.io/zh-cn/v3.12/)  \n\n96. \u003ca href=\"https://github.com/sapientinc/hrm\"\u003esapientinc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sapientinc/hrm\"\u003eHRM\u003c/a\u003e\u003c/b\u003e ⭐ 12,274    \n   Hierarchical Reasoning Model (HRM), a novel recurrent architecture that attains significant computational depth while maintaining both training stability and efficiency  \n   🔗 [sapient.inc](https://sapient.inc)  \n\n97. \u003ca href=\"https://github.com/google-research/vision_transformer\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/vision_transformer\"\u003evision_transformer\u003c/a\u003e\u003c/b\u003e ⭐ 12,247    \n   Vision Transformer and MLP-Mixer Architectures  \n\n98. \u003ca href=\"https://github.com/instructor-ai/instructor\"\u003einstructor-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/instructor-ai/instructor\"\u003einstructor\u003c/a\u003e\u003c/b\u003e ⭐ 12,199    \n   Instructor is a Python library that makes it a breeze to work with structured outputs from large language models (LLMs). Built on top of Pydantic, it provides a simple, transparent, and user-friendly API to manage validation, retries, and streaming responses.  \n   🔗 [python.useinstructor.com](https://python.useinstructor.com/)  \n\n99. \u003ca href=\"https://github.com/openlmlab/moss\"\u003eopenlmlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openlmlab/moss\"\u003eMOSS\u003c/a\u003e\u003c/b\u003e ⭐ 12,076    \n   An open-source tool-augmented conversational language model from Fudan University  \n   🔗 [txsun1997.github.io/blogs/moss.html](https://txsun1997.github.io/blogs/moss.html)  \n\n100. \u003ca href=\"https://github.com/h2oai/h2ogpt\"\u003eh2oai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/h2oai/h2ogpt\"\u003eh2ogpt\u003c/a\u003e\u003c/b\u003e ⭐ 12,004    \n   Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/  \n   🔗 [h2o.ai](http://h2o.ai)  \n\n101. \u003ca href=\"https://github.com/gibsonai/memori\"\u003egibsonai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gibsonai/memori\"\u003eMemori\u003c/a\u003e\u003c/b\u003e ⭐ 11,861    \n   Memori enables any LLM to remember conversations, learn from interactions, and maintain context across sessions  \n   🔗 [memorilabs.ai](https://memorilabs.ai)  \n\n102. \u003ca href=\"https://github.com/chainlit/chainlit\"\u003echainlit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chainlit/chainlit\"\u003echainlit\u003c/a\u003e\u003c/b\u003e ⭐ 11,430    \n   Build Conversational AI in minutes ⚡️  \n   🔗 [docs.chainlit.io](https://docs.chainlit.io)  \n\n103. \u003ca href=\"https://github.com/topoteretes/cognee\"\u003etopoteretes/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/topoteretes/cognee\"\u003ecognee\u003c/a\u003e\u003c/b\u003e ⭐ 11,265    \n   Memory for AI Agents in 6 lines of code  \n   🔗 [www.cognee.ai](https://www.cognee.ai)  \n\n104. \u003ca href=\"https://github.com/eleutherai/lm-evaluation-harness\"\u003eeleutherai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eleutherai/lm-evaluation-harness\"\u003elm-evaluation-harness\u003c/a\u003e\u003c/b\u003e ⭐ 11,265    \n   A framework for few-shot evaluation of language models.  \n   🔗 [www.eleuther.ai](https://www.eleuther.ai)  \n\n105. \u003ca href=\"https://github.com/axolotl-ai-cloud/axolotl\"\u003eaxolotl-ai-cloud/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/axolotl-ai-cloud/axolotl\"\u003eaxolotl\u003c/a\u003e\u003c/b\u003e ⭐ 11,143    \n   Go ahead and axolotl questions  \n   🔗 [docs.axolotl.ai](https://docs.axolotl.ai)  \n\n106. \u003ca href=\"https://github.com/geeeekexplorer/nano-vllm\"\u003egeeeekexplorer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/geeeekexplorer/nano-vllm\"\u003enano-vllm\u003c/a\u003e\u003c/b\u003e ⭐ 11,017    \n   A lightweight vLLM implementation built from scratch.  \n\n107. \u003ca href=\"https://github.com/microsoft/promptflow\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/promptflow\"\u003epromptflow\u003c/a\u003e\u003c/b\u003e ⭐ 11,001    \n   Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.  \n   🔗 [microsoft.github.io/promptflow](https://microsoft.github.io/promptflow/)  \n\n108. \u003ca href=\"https://github.com/artidoro/qlora\"\u003eartidoro/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/artidoro/qlora\"\u003eqlora\u003c/a\u003e\u003c/b\u003e ⭐ 10,821    \n   QLoRA: Efficient Finetuning of Quantized LLMs  \n   🔗 [arxiv.org/abs/2305.14314](https://arxiv.org/abs/2305.14314)  \n\n109. \u003ca href=\"https://github.com/databrickslabs/dolly\"\u003edatabrickslabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/databrickslabs/dolly\"\u003edolly\u003c/a\u003e\u003c/b\u003e ⭐ 10,802    \n   Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform  \n   🔗 [www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html)  \n\n110. \u003ca href=\"https://github.com/mistralai/mistral-src\"\u003emistralai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mistralai/mistral-src\"\u003emistral-inference\u003c/a\u003e\u003c/b\u003e ⭐ 10,632    \n   Official inference library for Mistral models  \n   🔗 [mistral.ai](https://mistral.ai/)  \n\n111. \u003ca href=\"https://github.com/e2b-dev/e2b\"\u003ee2b-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/e2b-dev/e2b\"\u003eE2B\u003c/a\u003e\u003c/b\u003e ⭐ 10,604    \n   E2B is an open-source infrastructure that allows you to run AI-generated code in secure isolated sandboxes in the cloud  \n   🔗 [e2b.dev/docs](https://e2b.dev/docs)  \n\n112. \u003ca href=\"https://github.com/karpathy/minbpe\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/minbpe\"\u003eminbpe\u003c/a\u003e\u003c/b\u003e ⭐ 10,284    \n   Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.  \n\n113. \u003ca href=\"https://github.com/promptfoo/promptfoo\"\u003epromptfoo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/promptfoo/promptfoo\"\u003epromptfoo\u003c/a\u003e\u003c/b\u003e ⭐ 10,096    \n   Test your prompts, agents, and RAGs. AI Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.  \n   🔗 [promptfoo.dev](https://promptfoo.dev)  \n\n114. \u003ca href=\"https://github.com/pipecat-ai/pipecat\"\u003epipecat-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pipecat-ai/pipecat\"\u003epipecat\u003c/a\u003e\u003c/b\u003e ⭐ 9,974    \n   Open Source framework for voice and multimodal conversational AI  \n   🔗 [pipecat.ai](https://pipecat.ai)  \n\n115. \u003ca href=\"https://github.com/abetlen/llama-cpp-python\"\u003eabetlen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/abetlen/llama-cpp-python\"\u003ellama-cpp-python\u003c/a\u003e\u003c/b\u003e ⭐ 9,922    \n   Simple Python bindings for @ggerganov's llama.cpp library.  \n   🔗 [llama-cpp-python.readthedocs.io](https://llama-cpp-python.readthedocs.io)  \n\n116. \u003ca href=\"https://github.com/mshumer/gpt-prompt-engineer\"\u003emshumer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mshumer/gpt-prompt-engineer\"\u003egpt-prompt-engineer\u003c/a\u003e\u003c/b\u003e ⭐ 9,631    \n   Simply input a description of your task and some test cases, and the system will generate, test, and rank a multitude of prompts to find the ones that perform the best.  \n\n117. \u003ca href=\"https://github.com/blinkdl/chatrwkv\"\u003eblinkdl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blinkdl/chatrwkv\"\u003eChatRWKV\u003c/a\u003e\u003c/b\u003e ⭐ 9,511    \n   ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.  \n\n118. \u003ca href=\"https://github.com/skypilot-org/skypilot\"\u003eskypilot-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/skypilot-org/skypilot\"\u003eskypilot\u003c/a\u003e\u003c/b\u003e ⭐ 9,367    \n   Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access \u0026 manage all AI compute (Kubernetes, 20+ clouds, or on-prem).  \n   🔗 [docs.skypilot.co](https://docs.skypilot.co/)  \n\n119. \u003ca href=\"https://github.com/vikhyat/moondream\"\u003evikhyat/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vikhyat/moondream\"\u003emoondream\u003c/a\u003e\u003c/b\u003e ⭐ 9,264    \n   A tiny open-source computer-vision language model designed to run efficiently on edge devices  \n   🔗 [moondream.ai](https://moondream.ai)  \n\n120. \u003ca href=\"https://github.com/jzhang38/tinyllama\"\u003ejzhang38/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jzhang38/tinyllama\"\u003eTinyLlama\u003c/a\u003e\u003c/b\u003e ⭐ 8,878    \n   The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.  \n\n121. \u003ca href=\"https://github.com/vaibhavs10/insanely-fast-whisper\"\u003evaibhavs10/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vaibhavs10/insanely-fast-whisper\"\u003einsanely-fast-whisper\u003c/a\u003e\u003c/b\u003e ⭐ 8,787    \n   An opinionated CLI to transcribe Audio files w/ Whisper on-device! Powered by 🤗 Transformers, Optimum \u0026 flash-attn  \n\n122. \u003ca href=\"https://github.com/thudm/codegeex\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/codegeex\"\u003eCodeGeeX\u003c/a\u003e\u003c/b\u003e ⭐ 8,743    \n   CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)  \n   🔗 [codegeex.cn](https://codegeex.cn)  \n\n123. \u003ca href=\"https://github.com/bytedance/dolphin\"\u003ebytedance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedance/dolphin\"\u003eDolphin\u003c/a\u003e\u003c/b\u003e ⭐ 8,727    \n   A novel multimodal document image parsing model following an analyze-then-parse paradigm  \n\n124. \u003ca href=\"https://github.com/lyogavin/airllm\"\u003elyogavin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lyogavin/airllm\"\u003eairllm\u003c/a\u003e\u003c/b\u003e ⭐ 8,713    \n   AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning. And you can run 405B Llama3.1 on 8GB vram now.  \n\n125. \u003ca href=\"https://github.com/apple/ml-ferret\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/ml-ferret\"\u003eml-ferret\u003c/a\u003e\u003c/b\u003e ⭐ 8,676    \n   Ferret: Refer and Ground Anything Anywhere at Any Granularity  \n\n126. \u003ca href=\"https://github.com/sjtu-ipads/powerinfer\"\u003esjtu-ipads/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sjtu-ipads/powerinfer\"\u003ePowerInfer\u003c/a\u003e\u003c/b\u003e ⭐ 8,591    \n   High-speed Large Language Model Serving for Local Deployment  \n\n127. \u003ca href=\"https://github.com/optimalscale/lmflow\"\u003eoptimalscale/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/optimalscale/lmflow\"\u003eLMFlow\u003c/a\u003e\u003c/b\u003e ⭐ 8,502    \n   An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.  \n   🔗 [optimalscale.github.io/lmflow](https://optimalscale.github.io/LMFlow/)  \n\n128. \u003ca href=\"https://github.com/eleutherai/gpt-neo\"\u003eeleutherai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eleutherai/gpt-neo\"\u003egpt-neo\u003c/a\u003e\u003c/b\u003e ⭐ 8,288    \n   An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.  \n   🔗 [www.eleuther.ai](https://www.eleuther.ai)  \n\n129. \u003ca href=\"https://github.com/lianjiatech/belle\"\u003elianjiatech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lianjiatech/belle\"\u003eBELLE\u003c/a\u003e\u003c/b\u003e ⭐ 8,284    \n   BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）  \n\n130. \u003ca href=\"https://github.com/future-house/paper-qa\"\u003efuture-house/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/future-house/paper-qa\"\u003epaper-qa\u003c/a\u003e\u003c/b\u003e ⭐ 8,025    \n   High-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature  \n   🔗 [futurehouse.gitbook.io/futurehouse-cookbook](https://futurehouse.gitbook.io/futurehouse-cookbook)  \n\n131. \u003ca href=\"https://github.com/plachtaa/vall-e-x\"\u003eplachtaa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plachtaa/vall-e-x\"\u003eVALL-E-X\u003c/a\u003e\u003c/b\u003e ⭐ 7,959    \n   An open source implementation of Microsoft's VALL-E X zero-shot TTS model. Demo is available in https://plachtaa.github.io/vallex/  \n\n132. \u003ca href=\"https://github.com/zilliztech/gptcache\"\u003ezilliztech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zilliztech/gptcache\"\u003eGPTCache\u003c/a\u003e\u003c/b\u003e ⭐ 7,913    \n   Semantic cache for LLMs. Fully integrated with LangChain and llama_index.   \n   🔗 [gptcache.readthedocs.io](https://gptcache.readthedocs.io)  \n\n133. \u003ca href=\"https://github.com/01-ai/yi\"\u003e01-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/01-ai/yi\"\u003eYi\u003c/a\u003e\u003c/b\u003e ⭐ 7,846    \n   The Yi series models are the next generation of open-source large language models trained from scratch by 01.AI.  \n   🔗 [01.ai](https://01.ai)  \n\n134. \u003ca href=\"https://github.com/thudm/glm-130b\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/glm-130b\"\u003eGLM-130B\u003c/a\u003e\u003c/b\u003e ⭐ 7,677    \n   GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)  \n\n135. \u003ca href=\"https://github.com/sweepai/sweep\"\u003esweepai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sweepai/sweep\"\u003esweep\u003c/a\u003e\u003c/b\u003e ⭐ 7,628    \n   Sweep: AI coding assistant for JetBrains  \n   🔗 [sweep.dev](https://sweep.dev)  \n\n136. \u003ca href=\"https://github.com/bigcode-project/starcoder\"\u003ebigcode-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bigcode-project/starcoder\"\u003estarcoder\u003c/a\u003e\u003c/b\u003e ⭐ 7,534    \n   Home of StarCoder: fine-tuning \u0026 inference!  \n\n137. \u003ca href=\"https://github.com/vectifyai/pageindex\"\u003evectifyai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vectifyai/pageindex\"\u003ePageIndex\u003c/a\u003e\u003c/b\u003e ⭐ 7,532    \n   A document indexing system that builds search tree structures from long documents, making them ready for reasoning-based RAG  \n   🔗 [pageindex.ai](https://pageindex.ai)  \n\n138. \u003ca href=\"https://github.com/openlm-research/open_llama\"\u003eopenlm-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openlm-research/open_llama\"\u003eopen_llama\u003c/a\u003e\u003c/b\u003e ⭐ 7,529    \n   OpenLLaMA: An Open Reproduction of LLaMA  \n\n139. \u003ca href=\"https://github.com/weaviate/verba\"\u003eweaviate/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/weaviate/verba\"\u003eVerba\u003c/a\u003e\u003c/b\u003e ⭐ 7,525    \n   Retrieval Augmented Generation (RAG) chatbot powered by Weaviate  \n\n140. \u003ca href=\"https://github.com/boundaryml/baml\"\u003eboundaryml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/boundaryml/baml\"\u003ebaml\u003c/a\u003e\u003c/b\u003e ⭐ 7,442    \n   The AI framework that adds the engineering to prompt engineering (Python/TS/Ruby/Java/C#/Rust/Go compatible)  \n   🔗 [docs.boundaryml.com](https://docs.boundaryml.com)  \n\n141. \u003ca href=\"https://github.com/eleutherai/gpt-neox\"\u003eeleutherai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eleutherai/gpt-neox\"\u003egpt-neox\u003c/a\u003e\u003c/b\u003e ⭐ 7,370    \n   An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries  \n   🔗 [www.eleuther.ai](https://www.eleuther.ai/)  \n\n142. \u003ca href=\"https://github.com/mit-han-lab/streaming-llm\"\u003emit-han-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mit-han-lab/streaming-llm\"\u003estreaming-llm\u003c/a\u003e\u003c/b\u003e ⭐ 7,172    \n   [ICLR 2024] Efficient Streaming Language Models with Attention Sinks  \n   🔗 [arxiv.org/abs/2309.17453](https://arxiv.org/abs/2309.17453)  \n\n143. \u003ca href=\"https://github.com/bhaskatripathi/pdfgpt\"\u003ebhaskatripathi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bhaskatripathi/pdfgpt\"\u003epdfGPT\u003c/a\u003e\u003c/b\u003e ⭐ 7,168    \n   PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!  \n   🔗 [huggingface.co/spaces/bhaskartripathi/pdfchatter](https://huggingface.co/spaces/bhaskartripathi/pdfChatter)  \n\n144. \u003ca href=\"https://github.com/apple/ml-fastvlm\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/ml-fastvlm\"\u003eml-fastvlm\u003c/a\u003e\u003c/b\u003e ⭐ 7,167    \n   FastVLM: Efficient Vision Encoding for Vision Language Models  \n\n145. \u003ca href=\"https://github.com/internlm/internlm\"\u003einternlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/internlm/internlm\"\u003eInternLM\u003c/a\u003e\u003c/b\u003e ⭐ 7,142    \n   Official release of InternLM series (InternLM, InternLM2, InternLM2.5, InternLM3).  \n   🔗 [internlm.readthedocs.io](https://internlm.readthedocs.io/)  \n\n146. \u003ca href=\"https://github.com/apple/corenet\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/corenet\"\u003ecorenet\u003c/a\u003e\u003c/b\u003e ⭐ 7,021    \n   CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.  \n\n147. \u003ca href=\"https://github.com/nirdiamant/prompt_engineering\"\u003enirdiamant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nirdiamant/prompt_engineering\"\u003ePrompt_Engineering\u003c/a\u003e\u003c/b\u003e ⭐ 7,013    \n   A comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies.  \n\n148. \u003ca href=\"https://github.com/k-dense-ai/claude-scientific-skills\"\u003ek-dense-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/k-dense-ai/claude-scientific-skills\"\u003eclaude-scientific-skills\u003c/a\u003e\u003c/b\u003e ⭐ 6,957    \n   A set of ready to use scientific skills for Claude  \n   🔗 [k-dense.ai](https://k-dense.ai)  \n\n149. \u003ca href=\"https://github.com/anthropics/knowledge-work-plugins\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/knowledge-work-plugins\"\u003eknowledge-work-plugins\u003c/a\u003e\u003c/b\u003e ⭐ 6,910    \n   Knowledge Work Plugins that turn Claude into a specialist for your role, team, and company  \n\n150. \u003ca href=\"https://github.com/lmcache/lmcache\"\u003elmcache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lmcache/lmcache\"\u003eLMCache\u003c/a\u003e\u003c/b\u003e ⭐ 6,765    \n   LMCache is an LLM serving engine extension to reduce TTFT and increase throughput, especially under long-context scenarios  \n   🔗 [lmcache.ai](https://lmcache.ai/)  \n\n151. \u003ca href=\"https://github.com/langchain-ai/opengpts\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/opengpts\"\u003eopengpts\u003c/a\u003e\u003c/b\u003e ⭐ 6,757    \n   An open source effort to create a similar experience to OpenAI's GPTs and Assistants API.  \n\n152. \u003ca href=\"https://github.com/arcee-ai/mergekit\"\u003earcee-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arcee-ai/mergekit\"\u003emergekit\u003c/a\u003e\u003c/b\u003e ⭐ 6,705    \n   Tools for merging pretrained large language models.  \n\n153. \u003ca href=\"https://github.com/minedojo/voyager\"\u003eminedojo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minedojo/voyager\"\u003eVoyager\u003c/a\u003e\u003c/b\u003e ⭐ 6,616    \n   An Open-Ended Embodied Agent with Large Language Models  \n   🔗 [voyager.minedojo.org](https://voyager.minedojo.org/)  \n\n154. \u003ca href=\"https://github.com/open-compass/opencompass\"\u003eopen-compass/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-compass/opencompass\"\u003eopencompass\u003c/a\u003e\u003c/b\u003e ⭐ 6,595    \n   OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.  \n   🔗 [opencompass.org.cn](https://opencompass.org.cn/)  \n\n155. \u003ca href=\"https://github.com/run-llama/rags\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/rags\"\u003erags\u003c/a\u003e\u003c/b\u003e ⭐ 6,532    \n   RAGs is a Streamlit app that lets you create a RAG pipeline from a data source using natural language.  \n\n156. \u003ca href=\"https://github.com/qwenlm/qwen-vl\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen-vl\"\u003eQwen-VL\u003c/a\u003e\u003c/b\u003e ⭐ 6,499    \n   The official repo of Qwen-VL (通义千问-VL) chat \u0026 pretrained large vision language model proposed by Alibaba Cloud.  \n\n157. \u003ca href=\"https://github.com/yuliang-liu/monkeyocr\"\u003eyuliang-liu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yuliang-liu/monkeyocr\"\u003eMonkeyOCR\u003c/a\u003e\u003c/b\u003e ⭐ 6,447    \n   A lightweight LMM-based Document Parsing Model with a Structure-Recognition-Relation Triplet Paradigm  \n\n158. \u003ca href=\"https://github.com/nat/openplayground\"\u003enat/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nat/openplayground\"\u003eopenplayground\u003c/a\u003e\u003c/b\u003e ⭐ 6,370    \n   An LLM playground you can run on your laptop  \n\n159. \u003ca href=\"https://github.com/guardrails-ai/guardrails\"\u003eguardrails-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/guardrails-ai/guardrails\"\u003eguardrails\u003c/a\u003e\u003c/b\u003e ⭐ 6,300    \n   Open-source Python package for specifying structure and type, validating and correcting the outputs of large language models (LLMs)  \n   🔗 [www.guardrailsai.com/docs](https://www.guardrailsai.com/docs)  \n\n160. \u003ca href=\"https://github.com/allenai/olmo\"\u003eallenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/allenai/olmo\"\u003eOLMo\u003c/a\u003e\u003c/b\u003e ⭐ 6,294    \n   OLMo is a repository for training and using AI2's state-of-the-art open language models. It is designed by scientists, for scientists.  \n   🔗 [allenai.org/olmo](https://allenai.org/olmo)  \n\n161. \u003ca href=\"https://github.com/langchain-ai/chat-langchain\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/chat-langchain\"\u003echat-langchain\u003c/a\u003e\u003c/b\u003e ⭐ 6,228    \n   Locally hosted chatbot specifically focused on question answering over the LangChain documentation  \n   🔗 [chat.langchain.com](https://chat.langchain.com)  \n\n162. \u003ca href=\"https://github.com/pytorch-labs/gpt-fast\"\u003epytorch-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch-labs/gpt-fast\"\u003egpt-fast\u003c/a\u003e\u003c/b\u003e ⭐ 6,181    \n   Simple and efficient pytorch-native transformer text generation in \u003c1000 LOC of python.  \n\n163. \u003ca href=\"https://github.com/lightning-ai/lit-llama\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/lit-llama\"\u003elit-llama\u003c/a\u003e\u003c/b\u003e ⭐ 6,093    \n   Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.  \n\n164. \u003ca href=\"https://github.com/linkedin/liger-kernel\"\u003elinkedin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/linkedin/liger-kernel\"\u003eLiger-Kernel\u003c/a\u003e\u003c/b\u003e ⭐ 6,065    \n   Efficient Triton Kernels for LLM Training  \n   🔗 [linkedin.github.io/liger-kernel](https://linkedin.github.io/Liger-Kernel/)  \n\n165. \u003ca href=\"https://github.com/microsoft/llmlingua\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/llmlingua\"\u003eLLMLingua\u003c/a\u003e\u003c/b\u003e ⭐ 5,784    \n   [EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.   \n   🔗 [llmlingua.com](https://llmlingua.com/)  \n\n166. \u003ca href=\"https://github.com/microsoft/promptbase\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/promptbase\"\u003epromptbase\u003c/a\u003e\u003c/b\u003e ⭐ 5,726    \n   promptbase is an evolving collection of resources, best practices, and example scripts for eliciting the best performance from foundation models.  \n\n167. \u003ca href=\"https://github.com/meta-pytorch/torchtune\"\u003emeta-pytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-pytorch/torchtune\"\u003etorchtune\u003c/a\u003e\u003c/b\u003e ⭐ 5,650    \n   a PyTorch library for easily authoring, post-training, and experimenting with recipes for SFT, knowledge distillation, DPO, PPO, GRPO, and quantization-aware training  \n   🔗 [pytorch.org/torchtune/main](https://pytorch.org/torchtune/main/)  \n\n168. \u003ca href=\"https://github.com/nvidia/nemo-guardrails\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/nemo-guardrails\"\u003eGuardrails\u003c/a\u003e\u003c/b\u003e ⭐ 5,538    \n   NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.  \n   🔗 [docs.nvidia.com/nemo/guardrails/latest/index.html](https://docs.nvidia.com/nemo/guardrails/latest/index.html)  \n\n169. \u003ca href=\"https://github.com/openbmb/toolbench\"\u003eopenbmb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openbmb/toolbench\"\u003eToolBench\u003c/a\u003e\u003c/b\u003e ⭐ 5,492    \n   [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.  \n   🔗 [openbmb.github.io/toolbench](https://openbmb.github.io/ToolBench/)  \n\n170. \u003ca href=\"https://github.com/dsdanielpark/bard-api\"\u003edsdanielpark/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dsdanielpark/bard-api\"\u003eBard-API\u003c/a\u003e\u003c/b\u003e ⭐ 5,226    \n   The unofficial python package that returns response of Google Bard through cookie value.  \n   🔗 [pypi.org/project/bardapi](https://pypi.org/project/bardapi/)  \n\n171. \u003ca href=\"https://github.com/katanaml/sparrow\"\u003ekatanaml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/katanaml/sparrow\"\u003esparrow\u003c/a\u003e\u003c/b\u003e ⭐ 5,097    \n   Sparrow is a solution for efficient data extraction and processing from various documents and images like invoices and receipts  \n   🔗 [sparrow.katanaml.io](https://sparrow.katanaml.io)  \n\n172. \u003ca href=\"https://github.com/agiresearch/aios\"\u003eagiresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agiresearch/aios\"\u003eAIOS\u003c/a\u003e\u003c/b\u003e ⭐ 4,967    \n   AIOS, a Large Language Model (LLM) Agent operating system, embeds large language model into Operating Systems (OS) as the brain of the OS, enabling an operating system \"with soul\" -- an important step towards AGI.  \n   🔗 [docs.aios.foundation](https://docs.aios.foundation)  \n\n173. \u003ca href=\"https://github.com/togethercomputer/redpajama-data\"\u003etogethercomputer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/togethercomputer/redpajama-data\"\u003eRedPajama-Data\u003c/a\u003e\u003c/b\u003e ⭐ 4,922    \n   The RedPajama-Data repository contains code for preparing large datasets for training large language models.  \n\n174. \u003ca href=\"https://github.com/1rgs/jsonformer\"\u003e1rgs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/1rgs/jsonformer\"\u003ejsonformer\u003c/a\u003e\u003c/b\u003e ⭐ 4,891    \n   A Bulletproof Way to Generate Structured JSON from Language Models  \n\n175. \u003ca href=\"https://github.com/h2oai/h2o-llmstudio\"\u003eh2oai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/h2oai/h2o-llmstudio\"\u003eh2o-llmstudio\u003c/a\u003e\u003c/b\u003e ⭐ 4,781    \n   H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/  \n   🔗 [h2o.ai](https://h2o.ai)  \n\n176. \u003ca href=\"https://github.com/flashinfer-ai/flashinfer\"\u003eflashinfer-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/flashinfer-ai/flashinfer\"\u003eflashinfer\u003c/a\u003e\u003c/b\u003e ⭐ 4,744    \n   FlashInfer is a library and kernel generator for Large Language Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, SparseAttention, PageAttention, Sampling  \n   🔗 [flashinfer.ai](https://flashinfer.ai)  \n\n177. \u003ca href=\"https://github.com/kiln-ai/kiln\"\u003ekiln-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kiln-ai/kiln\"\u003eKiln\u003c/a\u003e\u003c/b\u003e ⭐ 4,593    \n   Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.  \n   🔗 [kiln.tech](https://kiln.tech)  \n\n178. \u003ca href=\"https://github.com/vllm-project/aibrix\"\u003evllm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vllm-project/aibrix\"\u003eaibrix\u003c/a\u003e\u003c/b\u003e ⭐ 4,584    \n   AIBrix delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored specifically to enterprise needs.  \n\n179. \u003ca href=\"https://github.com/kyegomez/tree-of-thoughts\"\u003ekyegomez/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kyegomez/tree-of-thoughts\"\u003etree-of-thoughts\u003c/a\u003e\u003c/b\u003e ⭐ 4,564    \n   Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%   \n   🔗 [discord.gg/qutxnk2nmf](https://discord.gg/qUtxnK2NMf)  \n\n180. \u003ca href=\"https://github.com/yizhongw/self-instruct\"\u003eyizhongw/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yizhongw/self-instruct\"\u003eself-instruct\u003c/a\u003e\u003c/b\u003e ⭐ 4,563    \n   Aligning pretrained language models with instruction data generated by themselves.  \n\n181. \u003ca href=\"https://github.com/lm-sys/routellm\"\u003elm-sys/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lm-sys/routellm\"\u003eRouteLLM\u003c/a\u003e\u003c/b\u003e ⭐ 4,553    \n   A framework for serving and evaluating LLM routers - save LLM costs without compromising quality  \n\n182. \u003ca href=\"https://github.com/marker-inc-korea/autorag\"\u003emarker-inc-korea/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marker-inc-korea/autorag\"\u003eAutoRAG\u003c/a\u003e\u003c/b\u003e ⭐ 4,541    \n   AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation \u0026 Optimization with AutoML-Style Automation  \n   🔗 [marker-inc-korea.github.io/autorag](https://marker-inc-korea.github.io/AutoRAG/)  \n\n183. \u003ca href=\"https://github.com/microsoft/biogpt\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/biogpt\"\u003eBioGPT\u003c/a\u003e\u003c/b\u003e ⭐ 4,486    \n   Implementation of BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining  \n\n184. \u003ca href=\"https://github.com/hiyouga/easyr1\"\u003ehiyouga/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hiyouga/easyr1\"\u003eEasyR1\u003c/a\u003e\u003c/b\u003e ⭐ 4,475    \n   EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL  \n   🔗 [verl.readthedocs.io](https://verl.readthedocs.io)  \n\n185. \u003ca href=\"https://github.com/llm-attacks/llm-attacks\"\u003ellm-attacks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/llm-attacks/llm-attacks\"\u003ellm-attacks\u003c/a\u003e\u003c/b\u003e ⭐ 4,460    \n   This is the official repository for \"Universal and Transferable Adversarial Attacks on Aligned Language Models\"  \n   🔗 [llm-attacks.org](https://llm-attacks.org/)  \n\n186. \u003ca href=\"https://github.com/turboderp/exllamav2\"\u003eturboderp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/turboderp/exllamav2\"\u003eexllamav2\u003c/a\u003e\u003c/b\u003e ⭐ 4,425    \n   A fast inference library for running LLMs locally on modern consumer-class GPUs  \n\n187. \u003ca href=\"https://github.com/huggingface/text-embeddings-inference\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/text-embeddings-inference\"\u003etext-embeddings-inference\u003c/a\u003e\u003c/b\u003e ⭐ 4,417    \n   A blazing fast inference solution for text embeddings models  \n   🔗 [huggingface.co/docs/text-embeddings-inference/quick_tour](https://huggingface.co/docs/text-embeddings-inference/quick_tour)  \n\n188. \u003ca href=\"https://github.com/ragapp/ragapp\"\u003eragapp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ragapp/ragapp\"\u003eragapp\u003c/a\u003e\u003c/b\u003e ⭐ 4,393    \n   The easiest way to use Agentic RAG in any enterprise  \n\n189. \u003ca href=\"https://github.com/instruction-tuning-with-gpt-4/gpt-4-llm\"\u003einstruction-tuning-with-gpt-4/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/instruction-tuning-with-gpt-4/gpt-4-llm\"\u003eGPT-4-LLM\u003c/a\u003e\u003c/b\u003e ⭐ 4,342    \n   Instruction Tuning with GPT-4  \n   🔗 [instruction-tuning-with-gpt-4.github.io](https://instruction-tuning-with-gpt-4.github.io/)  \n\n190. \u003ca href=\"https://github.com/openai/simple-evals\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/simple-evals\"\u003esimple-evals\u003c/a\u003e\u003c/b\u003e ⭐ 4,314    \n   Lightweight library for evaluating language models  \n\n191. \u003ca href=\"https://github.com/truefoundry/cognita\"\u003etruefoundry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/truefoundry/cognita\"\u003ecognita\u003c/a\u003e\u003c/b\u003e ⭐ 4,314    \n   RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry   \n   🔗 [cognita.truefoundry.com](https://cognita.truefoundry.com)  \n\n192. \u003ca href=\"https://github.com/neo4j-labs/llm-graph-builder\"\u003eneo4j-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neo4j-labs/llm-graph-builder\"\u003ellm-graph-builder\u003c/a\u003e\u003c/b\u003e ⭐ 4,294    \n   Transform unstructured data into a structured Knowledge Graph stored in Neo4j with LLMs  \n   🔗 [llm-graph-builder.neo4jlabs.com](https://llm-graph-builder.neo4jlabs.com/)  \n\n193. \u003ca href=\"https://github.com/p-e-w/heretic\"\u003ep-e-w/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/p-e-w/heretic\"\u003eheretic\u003c/a\u003e\u003c/b\u003e ⭐ 4,286    \n   Heretic is a tool that removes censorship from transformer-based LLMs without post-training  \n\n194. \u003ca href=\"https://github.com/microsoft/lmops\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/lmops\"\u003eLMOps\u003c/a\u003e\u003c/b\u003e ⭐ 4,262    \n   General technology for enabling AI capabilities w/ LLMs and MLLMs  \n   🔗 [aka.ms/generalai](https://aka.ms/GeneralAI)  \n\n195. \u003ca href=\"https://github.com/pytorch/executorch\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/executorch\"\u003eexecutorch\u003c/a\u003e\u003c/b\u003e ⭐ 4,182    \n   An end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices.  \n   🔗 [executorch.ai](https://executorch.ai)  \n\n196. \u003ca href=\"https://github.com/mshumer/gpt-llm-trainer\"\u003emshumer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mshumer/gpt-llm-trainer\"\u003egpt-llm-trainer\u003c/a\u003e\u003c/b\u003e ⭐ 4,168    \n   Input a description of your task, and the system will generate a dataset, parse it, and fine-tune a LLaMA 2 model for you  \n\n197. \u003ca href=\"https://github.com/openai/harmony\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/harmony\"\u003eharmony\u003c/a\u003e\u003c/b\u003e ⭐ 4,149    \n   Renderer for the harmony response format to be used with gpt-oss  \n\n198. \u003ca href=\"https://github.com/eth-sri/lmql\"\u003eeth-sri/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eth-sri/lmql\"\u003elmql\u003c/a\u003e\u003c/b\u003e ⭐ 4,136    \n   A language for constraint-guided and efficient LLM programming.  \n   🔗 [lmql.ai](https://lmql.ai)  \n\n199. \u003ca href=\"https://github.com/deep-agent/r1-v\"\u003edeep-agent/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deep-agent/r1-v\"\u003eR1-V\u003c/a\u003e\u003c/b\u003e ⭐ 4,025    \n   We are building a general framework for Reinforcement Learning with Verifiable Rewards (RLVR) in VLM.  RLVR outperforms chain-of-thought supervised fine-tuning (CoT-SFT) in both effectiveness and out-of-distribution (OOD) robustness for vision language models.  \n\n200. \u003ca href=\"https://github.com/sylphai-inc/adalflow\"\u003esylphai-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sylphai-inc/adalflow\"\u003eAdalFlow\u003c/a\u003e\u003c/b\u003e ⭐ 3,998    \n   Unified auto-differentiative framework for both zero-shot prompt optimization and few-shot optimization. It advances existing auto-optimization research, including Text-Grad and DsPy  \n   🔗 [adalflow.sylph.ai](http://adalflow.sylph.ai/)  \n\n201. \u003ca href=\"https://github.com/defog-ai/sqlcoder\"\u003edefog-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/defog-ai/sqlcoder\"\u003esqlcoder\u003c/a\u003e\u003c/b\u003e ⭐ 3,990    \n   SoTA LLM for converting natural language questions to SQL queries  \n\n202. \u003ca href=\"https://github.com/meta-llama/purplellama\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/purplellama\"\u003ePurpleLlama\u003c/a\u003e\u003c/b\u003e ⭐ 3,988    \n   Set of tools to assess and improve LLM security. An umbrella project to bring together tools and evals to help the community build responsibly with open genai models.  \n\n203. \u003ca href=\"https://github.com/bclavie/ragatouille\"\u003ebclavie/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bclavie/ragatouille\"\u003eRAGatouille\u003c/a\u003e\u003c/b\u003e ⭐ 3,828    \n   Bridging the gap between state-of-the-art research and alchemical RAG pipeline practices.  \n\n204. \u003ca href=\"https://github.com/ravenscroftj/turbopilot\"\u003eravenscroftj/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ravenscroftj/turbopilot\"\u003eturbopilot\u003c/a\u003e\u003c/b\u003e ⭐ 3,808    \n   Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU  \n\n205. \u003ca href=\"https://github.com/lightning-ai/litserve\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/litserve\"\u003eLitServe\u003c/a\u003e\u003c/b\u003e ⭐ 3,790    \n   A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.  \n   🔗 [lightning.ai/litserve?utm_source=litserve_readme\u0026utm_medium=referral\u0026utm_campaign=litserve_readme](https://lightning.ai/litserve?utm_source=litserve_readme\u0026utm_medium=referral\u0026utm_campaign=litserve_readme)  \n\n206. \u003ca href=\"https://github.com/agenta-ai/agenta\"\u003eagenta-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agenta-ai/agenta\"\u003eagenta\u003c/a\u003e\u003c/b\u003e ⭐ 3,787    \n   The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.  \n   🔗 [www.agenta.ai](http://www.agenta.ai)  \n\n207. \u003ca href=\"https://github.com/microsoft/promptwizard\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/promptwizard\"\u003ePromptWizard\u003c/a\u003e\u003c/b\u003e ⭐ 3,744    \n   PromptWizard is a discrete prompt optimization framework that employs a self-evolving mechanism where the LLM generates, critiques, and refines its own prompts and examples  \n\n208. \u003ca href=\"https://github.com/mmabrouk/chatgpt-wrapper\"\u003emmabrouk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mmabrouk/chatgpt-wrapper\"\u003ellm-workflow-engine\u003c/a\u003e\u003c/b\u003e ⭐ 3,718    \n   Power CLI and Workflow manager for LLMs (core package)  \n\n209. \u003ca href=\"https://github.com/predibase/lorax\"\u003epredibase/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/predibase/lorax\"\u003elorax\u003c/a\u003e\u003c/b\u003e ⭐ 3,682    \n   Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs  \n   🔗 [loraexchange.ai](https://loraexchange.ai)  \n\n210. \u003ca href=\"https://github.com/next-gpt/next-gpt\"\u003enext-gpt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/next-gpt/next-gpt\"\u003eNExT-GPT\u003c/a\u003e\u003c/b\u003e ⭐ 3,614    \n   Code and models for ICML 2024 paper, NExT-GPT: Any-to-Any Multimodal Large Language Model  \n   🔗 [next-gpt.github.io](https://next-gpt.github.io/)  \n\n211. \u003ca href=\"https://github.com/evolvinglmms-lab/lmms-eval\"\u003eevolvinglmms-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/evolvinglmms-lab/lmms-eval\"\u003elmms-eval\u003c/a\u003e\u003c/b\u003e ⭐ 3,588    \n   One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks  \n   🔗 [www.lmms-lab.com](https://www.lmms-lab.com)  \n\n212. \u003ca href=\"https://github.com/huggingface/smollm\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/smollm\"\u003esmollm\u003c/a\u003e\u003c/b\u003e ⭐ 3,575    \n   Everything about the SmolLM and SmolVLM family of models   \n   🔗 [huggingface.co/huggingfacetb](https://huggingface.co/HuggingFaceTB)  \n\n213. \u003ca href=\"https://github.com/verazuo/jailbreak_llms\"\u003everazuo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/verazuo/jailbreak_llms\"\u003ejailbreak_llms\u003c/a\u003e\u003c/b\u003e ⭐ 3,534    \n   Official repo for the ACM CCS 2024 paper \"Do Anything Now'': Characterizing and Evaluating In-The-Wild Jailbreak Prompts  \n   🔗 [jailbreak-llms.xinyueshen.me](https://jailbreak-llms.xinyueshen.me/)  \n\n214. \u003ca href=\"https://github.com/minimaxir/simpleaichat\"\u003eminimaxir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minimaxir/simpleaichat\"\u003esimpleaichat\u003c/a\u003e\u003c/b\u003e ⭐ 3,520    \n   Python package for easily interfacing with chat apps, with robust features and minimal code complexity.  \n\n215. \u003ca href=\"https://github.com/iryna-kondr/scikit-llm\"\u003eiryna-kondr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/iryna-kondr/scikit-llm\"\u003escikit-llm\u003c/a\u003e\u003c/b\u003e ⭐ 3,491    \n   Seamlessly integrate LLMs into scikit-learn.  \n   🔗 [beastbyte.ai](https://beastbyte.ai/)  \n\n216. \u003ca href=\"https://github.com/jaymody/picogpt\"\u003ejaymody/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jaymody/picogpt\"\u003epicoGPT\u003c/a\u003e\u003c/b\u003e ⭐ 3,438    \n   An unnecessarily tiny implementation of GPT-2 in NumPy.  \n\n217. \u003ca href=\"https://github.com/mit-han-lab/llm-awq\"\u003emit-han-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mit-han-lab/llm-awq\"\u003ellm-awq\u003c/a\u003e\u003c/b\u003e ⭐ 3,424    \n   AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration  \n\n218. \u003ca href=\"https://github.com/minimaxir/gpt-2-simple\"\u003eminimaxir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minimaxir/gpt-2-simple\"\u003egpt-2-simple\u003c/a\u003e\u003c/b\u003e ⭐ 3,406    \n   Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts  \n\n219. \u003ca href=\"https://github.com/novasky-ai/skythought\"\u003enovasky-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/novasky-ai/skythought\"\u003eSkyThought\u003c/a\u003e\u003c/b\u003e ⭐ 3,369    \n   Sky-T1: Train your own O1 preview model within $450  \n   🔗 [novasky-ai.github.io](https://novasky-ai.github.io/)  \n\n220. \u003ca href=\"https://github.com/deep-diver/llm-as-chatbot\"\u003edeep-diver/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deep-diver/llm-as-chatbot\"\u003eLLM-As-Chatbot\u003c/a\u003e\u003c/b\u003e ⭐ 3,332    \n   LLM as a Chatbot Service  \n\n221. \u003ca href=\"https://github.com/zou-group/textgrad\"\u003ezou-group/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zou-group/textgrad\"\u003etextgrad\u003c/a\u003e\u003c/b\u003e ⭐ 3,319    \n   TextGrad is a framework building automatic differentiation by implementing backpropagation through text feedback provided by LLMs, strongly building on the gradient metaphor.  \n   🔗 [textgrad.com](http://textgrad.com/)  \n\n222. \u003ca href=\"https://github.com/luodian/otter\"\u003eluodian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/luodian/otter\"\u003eOtter\u003c/a\u003e\u003c/b\u003e ⭐ 3,286    \n   🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.  \n   🔗 [otter-ntu.github.io](https://otter-ntu.github.io/)  \n\n223. \u003ca href=\"https://github.com/ruc-nlpir/flashrag\"\u003eruc-nlpir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ruc-nlpir/flashrag\"\u003eFlashRAG\u003c/a\u003e\u003c/b\u003e ⭐ 3,277    \n   FlashRAG is a Python toolkit for the reproduction and development of RAG research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 15 state-of-the-art RAG algorithms.  \n   🔗 [arxiv.org/abs/2405.13576](https://arxiv.org/abs/2405.13576)  \n\n224. \u003ca href=\"https://github.com/deepseek-ai/engram\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/engram\"\u003eEngram\u003c/a\u003e\u003c/b\u003e ⭐ 3,252    \n   Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models.  \n\n225. \u003ca href=\"https://github.com/googleapis/python-genai\"\u003egoogleapis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/googleapis/python-genai\"\u003epython-genai\u003c/a\u003e\u003c/b\u003e ⭐ 3,251    \n   Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications.  \n   🔗 [googleapis.github.io/python-genai](https://googleapis.github.io/python-genai/)  \n\n226. \u003ca href=\"https://github.com/cohere-ai/cohere-toolkit\"\u003ecohere-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cohere-ai/cohere-toolkit\"\u003ecohere-toolkit\u003c/a\u003e\u003c/b\u003e ⭐ 3,156    \n   Cohere Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications.  \n\n227. \u003ca href=\"https://github.com/microsoft/torchscale\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/torchscale\"\u003etorchscale\u003c/a\u003e\u003c/b\u003e ⭐ 3,132    \n   Foundation Architecture for (M)LLMs  \n   🔗 [aka.ms/generalai](https://aka.ms/GeneralAI)  \n\n228. \u003ca href=\"https://github.com/mistralai/mistral-finetune\"\u003emistralai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mistralai/mistral-finetune\"\u003emistral-finetune\u003c/a\u003e\u003c/b\u003e ⭐ 3,068    \n   A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models. It is based on LoRA.  \n\n229. \u003ca href=\"https://github.com/argilla-io/distilabel\"\u003eargilla-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/argilla-io/distilabel\"\u003edistilabel\u003c/a\u003e\u003c/b\u003e ⭐ 3,065    \n   Distilabel is the framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.  \n   🔗 [distilabel.argilla.io](https://distilabel.argilla.io)  \n\n230. \u003ca href=\"https://github.com/truera/trulens\"\u003etruera/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/truera/trulens\"\u003etrulens\u003c/a\u003e\u003c/b\u003e ⭐ 3,057    \n   Evaluation and Tracking for LLM Experiments and AI Agents  \n   🔗 [www.trulens.org](https://www.trulens.org/)  \n\n231. \u003ca href=\"https://github.com/noahshinn/reflexion\"\u003enoahshinn/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/noahshinn/reflexion\"\u003ereflexion\u003c/a\u003e\u003c/b\u003e ⭐ 3,042    \n   [NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning  \n\n232. \u003ca href=\"https://github.com/hegelai/prompttools\"\u003ehegelai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hegelai/prompttools\"\u003eprompttools\u003c/a\u003e\u003c/b\u003e ⭐ 2,998    \n   Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).  \n   🔗 [prompttools.readthedocs.io](http://prompttools.readthedocs.io)  \n\n233. \u003ca href=\"https://github.com/li-plus/chatglm.cpp\"\u003eli-plus/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/li-plus/chatglm.cpp\"\u003echatglm.cpp\u003c/a\u003e\u003c/b\u003e ⭐ 2,968    \n   C++ implementation of ChatGLM-6B \u0026 ChatGLM2-6B \u0026 ChatGLM3 \u0026 GLM4(V)  \n\n234. \u003ca href=\"https://github.com/freedomintelligence/llmzoo\"\u003efreedomintelligence/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/freedomintelligence/llmzoo\"\u003eLLMZoo\u003c/a\u003e\u003c/b\u003e ⭐ 2,951    \n   ⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡  \n\n235. \u003ca href=\"https://github.com/baichuan-inc/baichuan-13b\"\u003ebaichuan-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/baichuan-inc/baichuan-13b\"\u003eBaichuan-13B\u003c/a\u003e\u003c/b\u003e ⭐ 2,950    \n   A 13B large language model developed by Baichuan Intelligent Technology  \n   🔗 [huggingface.co/baichuan-inc/baichuan-13b-chat](https://huggingface.co/baichuan-inc/Baichuan-13B-Chat)  \n\n236. \u003ca href=\"https://github.com/eladlev/autoprompt\"\u003eeladlev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eladlev/autoprompt\"\u003eAutoPrompt\u003c/a\u003e\u003c/b\u003e ⭐ 2,915    \n   A prompt optimization framework designed to enhance and perfect your prompts for real-world use cases  \n\n237. \u003ca href=\"https://github.com/deepseek-ai/dualpipe\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/dualpipe\"\u003eDualPipe\u003c/a\u003e\u003c/b\u003e ⭐ 2,910    \n   DualPipe is an innovative bidirectional pipeline parallelism algorithm introduced in the DeepSeek-V3 Technical Report.  \n\n238. \u003ca href=\"https://github.com/alpha-vllm/llama2-accessory\"\u003ealpha-vllm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alpha-vllm/llama2-accessory\"\u003eLLaMA2-Accessory\u003c/a\u003e\u003c/b\u003e ⭐ 2,801    \n   An Open-source Toolkit for LLM Development  \n   🔗 [llama2-accessory.readthedocs.io](https://llama2-accessory.readthedocs.io/)  \n\n239. \u003ca href=\"https://github.com/juncongmoo/pyllama\"\u003ejuncongmoo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/juncongmoo/pyllama\"\u003epyllama\u003c/a\u003e\u003c/b\u003e ⭐ 2,799    \n   LLaMA: Open and Efficient Foundation Language Models  \n\n240. \u003ca href=\"https://github.com/janhq/cortex.cpp\"\u003ejanhq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/janhq/cortex.cpp\"\u003ecortex.cpp\u003c/a\u003e\u003c/b\u003e ⭐ 2,761    \n   Cortex is a Local AI API Platform that is used to run and customize LLMs.  \n   🔗 [cortex.so](https://cortex.so)  \n\n241. \u003ca href=\"https://github.com/langwatch/langwatch\"\u003elangwatch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langwatch/langwatch\"\u003elangwatch\u003c/a\u003e\u003c/b\u003e ⭐ 2,742    \n   LangWatch is an open platform for Observing, Evaluating and Optimizing your LLM and Agentic applications.  \n   🔗 [langwatch.ai](https://langwatch.ai)  \n\n242. \u003ca href=\"https://github.com/paperswithcode/galai\"\u003epaperswithcode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/paperswithcode/galai\"\u003egalai\u003c/a\u003e\u003c/b\u003e ⭐ 2,738    \n   Model API for GALACTICA  \n\n243. \u003ca href=\"https://github.com/roboflow/maestro\"\u003eroboflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/roboflow/maestro\"\u003emaestro\u003c/a\u003e\u003c/b\u003e ⭐ 2,656    \n   streamline the fine-tuning process for multimodal models: PaliGemma 2, Florence-2, and Qwen2.5-VL  \n   🔗 [maestro.roboflow.com](https://maestro.roboflow.com)  \n\n244. \u003ca href=\"https://github.com/vllm-project/llm-compressor\"\u003evllm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vllm-project/llm-compressor\"\u003ellm-compressor\u003c/a\u003e\u003c/b\u003e ⭐ 2,615    \n   Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM  \n   🔗 [docs.vllm.ai/projects/llm-compressor](https://docs.vllm.ai/projects/llm-compressor)  \n\n245. \u003ca href=\"https://github.com/spcl/graph-of-thoughts\"\u003espcl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spcl/graph-of-thoughts\"\u003egraph-of-thoughts\u003c/a\u003e\u003c/b\u003e ⭐ 2,587    \n   Official Implementation of \"Graph of Thoughts: Solving Elaborate Problems with Large Language Models\"  \n   🔗 [arxiv.org/pdf/2308.09687.pdf](https://arxiv.org/pdf/2308.09687.pdf)  \n\n246. \u003ca href=\"https://github.com/intel/neural-compressor\"\u003eintel/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/intel/neural-compressor\"\u003eneural-compressor\u003c/a\u003e\u003c/b\u003e ⭐ 2,573    \n   SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) \u0026 sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime  \n   🔗 [intel.github.io/neural-compressor](https://intel.github.io/neural-compressor/)  \n\n247. \u003ca href=\"https://github.com/ofa-sys/ofa\"\u003eofa-sys/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ofa-sys/ofa\"\u003eOFA\u003c/a\u003e\u003c/b\u003e ⭐ 2,552    \n   Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework  \n\n248. \u003ca href=\"https://github.com/young-geng/easylm\"\u003eyoung-geng/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/young-geng/easylm\"\u003eEasyLM\u003c/a\u003e\u003c/b\u003e ⭐ 2,507    \n   Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.  \n\n249. \u003ca href=\"https://github.com/huggingface/nanotron\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/nanotron\"\u003enanotron\u003c/a\u003e\u003c/b\u003e ⭐ 2,479    \n   Minimalistic large language model 3D-parallelism training  \n\n250. \u003ca href=\"https://github.com/illuin-tech/colpali\"\u003eilluin-tech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/illuin-tech/colpali\"\u003ecolpali\u003c/a\u003e\u003c/b\u003e ⭐ 2,468    \n   Code used for training the vision retrievers in the ColPali: Efficient Document Retrieval with Vision Language Models paper  \n   🔗 [huggingface.co/vidore](https://huggingface.co/vidore)  \n\n251. \u003ca href=\"https://github.com/protectai/llm-guard\"\u003eprotectai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/protectai/llm-guard\"\u003ellm-guard\u003c/a\u003e\u003c/b\u003e ⭐ 2,442    \n   Sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks for LLMs  \n   🔗 [protectai.github.io/llm-guard](https://protectai.github.io/llm-guard/)  \n\n252. \u003ca href=\"https://github.com/azure-samples/graphrag-accelerator\"\u003eazure-samples/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/azure-samples/graphrag-accelerator\"\u003egraphrag-accelerator\u003c/a\u003e\u003c/b\u003e ⭐ 2,410    \n   One-click deploy of a Knowledge Graph powered RAG (GraphRAG) in Azure  \n   🔗 [github.com/microsoft/graphrag](https://github.com/microsoft/graphrag)  \n\n253. \u003ca href=\"https://github.com/civitai/sd_civitai_extension\"\u003ecivitai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/civitai/sd_civitai_extension\"\u003esd_civitai_extension\u003c/a\u003e\u003c/b\u003e ⭐ 2,379    \n   All of the Civitai models inside Automatic 1111 Stable Diffusion Web UI  \n\n254. \u003ca href=\"https://github.com/uptrain-ai/uptrain\"\u003euptrain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/uptrain-ai/uptrain\"\u003euptrain\u003c/a\u003e\u003c/b\u003e ⭐ 2,338    \n   An open-source unified platform to evaluate and improve Generative AI applications. Provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases)  \n   🔗 [uptrain.ai](https://uptrain.ai/)  \n\n255. \u003ca href=\"https://github.com/facebookresearch/large_concept_model\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/large_concept_model\"\u003elarge_concept_model\u003c/a\u003e\u003c/b\u003e ⭐ 2,330    \n   Large Concept Models: Language modeling in a sentence representation space  \n\n256. \u003ca href=\"https://github.com/akariasai/self-rag\"\u003eakariasai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/akariasai/self-rag\"\u003eself-rag\u003c/a\u003e\u003c/b\u003e ⭐ 2,307    \n   This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.  \n   🔗 [selfrag.github.io](https://selfrag.github.io/)  \n\n257. \u003ca href=\"https://github.com/casper-hansen/autoawq\"\u003ecasper-hansen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/casper-hansen/autoawq\"\u003eAutoAWQ\u003c/a\u003e\u003c/b\u003e ⭐ 2,307    \n   AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. Documentation:  \n   🔗 [casper-hansen.github.io/autoawq](https://casper-hansen.github.io/AutoAWQ/)  \n\n258. \u003ca href=\"https://github.com/huggingface/lighteval\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/lighteval\"\u003elighteval\u003c/a\u003e\u003c/b\u003e ⭐ 2,280    \n   LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron.  \n   🔗 [huggingface.co/docs/lighteval/en/index](https://huggingface.co/docs/lighteval/en/index)  \n\n259. \u003ca href=\"https://github.com/ist-daslab/gptq\"\u003eist-daslab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ist-daslab/gptq\"\u003egptq\u003c/a\u003e\u003c/b\u003e ⭐ 2,247    \n   Code for the ICLR 2023 paper \"GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers\".  \n   🔗 [arxiv.org/abs/2210.17323](https://arxiv.org/abs/2210.17323)  \n\n260. \u003ca href=\"https://github.com/microsoft/megatron-deepspeed\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/megatron-deepspeed\"\u003eMegatron-DeepSpeed\u003c/a\u003e\u003c/b\u003e ⭐ 2,220    \n   Ongoing research training transformer language models at scale, including: BERT \u0026 GPT-2  \n\n261. \u003ca href=\"https://github.com/gepa-ai/gepa\"\u003egepa-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gepa-ai/gepa\"\u003egepa\u003c/a\u003e\u003c/b\u003e ⭐ 2,154    \n   GEPA (Genetic-Pareto) is a framework for optimizing arbitrary systems composed of text components—like AI prompts, code snippets, or textual specs—against any evaluation metric  \n\n262. \u003ca href=\"https://github.com/epfllm/meditron\"\u003eepfllm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/epfllm/meditron\"\u003emeditron\u003c/a\u003e\u003c/b\u003e ⭐ 2,136    \n   Meditron is a suite of open-source medical Large Language Models (LLMs).  \n   🔗 [huggingface.co/epfl-llm](https://huggingface.co/epfl-llm)  \n\n263. \u003ca href=\"https://github.com/tairov/llama2.mojo\"\u003etairov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tairov/llama2.mojo\"\u003ellama2.mojo\u003c/a\u003e\u003c/b\u003e ⭐ 2,115    \n   Inference Llama 2 in one file of pure 🔥  \n   🔗 [www.modular.com/blog/community-spotlight-how-i-built-llama2-by-aydyn-tairov](https://www.modular.com/blog/community-spotlight-how-i-built-llama2-by-aydyn-tairov)  \n\n264. \u003ca href=\"https://github.com/ai-hypercomputer/maxtext\"\u003eai-hypercomputer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ai-hypercomputer/maxtext\"\u003emaxtext\u003c/a\u003e\u003c/b\u003e ⭐ 2,106    \n   MaxText is a high performance, highly scalable, open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference.  \n   🔗 [maxtext.readthedocs.io](https://maxtext.readthedocs.io)  \n\n265. \u003ca href=\"https://github.com/facebookresearch/chameleon\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/chameleon\"\u003echameleon\u003c/a\u003e\u003c/b\u003e ⭐ 2,081    \n   Repository for Meta Chameleon, a mixed-modal early-fusion foundation model from FAIR.  \n   🔗 [arxiv.org/abs/2405.09818](https://arxiv.org/abs/2405.09818)  \n\n266. \u003ca href=\"https://github.com/openai/image-gpt\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/image-gpt\"\u003eimage-gpt\u003c/a\u003e\u003c/b\u003e ⭐ 2,079    \n   Archived. Code and models from the paper \"Generative Pretraining from Pixels\"  \n\n267. \u003ca href=\"https://github.com/lucidrains/toolformer-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/toolformer-pytorch\"\u003etoolformer-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 2,057    \n   Implementation of Toolformer, Language Models That Can Use Tools, by MetaAI  \n\n268. \u003ca href=\"https://github.com/google-gemini/genai-processors\"\u003egoogle-gemini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-gemini/genai-processors\"\u003egenai-processors\u003c/a\u003e\u003c/b\u003e ⭐ 2,043    \n   GenAI Processors is a lightweight Python library that enables efficient, parallel content processing.  \n\n269. \u003ca href=\"https://github.com/huggingface/picotron\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/picotron\"\u003epicotron\u003c/a\u003e\u003c/b\u003e ⭐ 2,008    \n   Minimalist \u0026 most-hackable repository for pre-training Llama-like models with 4D Parallelism (Data, Tensor, Pipeline, Context parallel)  \n\n270. \u003ca href=\"https://github.com/neulab/prompt2model\"\u003eneulab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neulab/prompt2model\"\u003eprompt2model\u003c/a\u003e\u003c/b\u003e ⭐ 2,006    \n   A system that takes a natural language task description to train a small special-purpose model that is conducive for deployment.  \n\n271. \u003ca href=\"https://github.com/minishlab/model2vec\"\u003eminishlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minishlab/model2vec\"\u003emodel2vec\u003c/a\u003e\u003c/b\u003e ⭐ 1,988    \n   Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance  \n   🔗 [minish.ai/packages/model2vec](https://minish.ai/packages/model2vec)  \n\n272. \u003ca href=\"https://github.com/noamgat/lm-format-enforcer\"\u003enoamgat/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/noamgat/lm-format-enforcer\"\u003elm-format-enforcer\u003c/a\u003e\u003c/b\u003e ⭐ 1,979    \n   Enforce the output format (JSON Schema, Regex etc) of a language model  \n\n273. \u003ca href=\"https://github.com/agentops-ai/tokencost\"\u003eagentops-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agentops-ai/tokencost\"\u003etokencost\u003c/a\u003e\u003c/b\u003e ⭐ 1,914    \n   Easy token price estimates for 400+ LLMs. TokenOps.  \n   🔗 [agentops.ai](https://agentops.ai)  \n\n274. \u003ca href=\"https://github.com/aiming-lab/simplemem\"\u003eaiming-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aiming-lab/simplemem\"\u003eSimpleMem\u003c/a\u003e\u003c/b\u003e ⭐ 1,871    \n   SimpleMem addresses the fundamental challenge of efficient long-term memory for LLM agents through a three-stage pipeline grounded in Semantic Lossless Compression.  \n\n275. \u003ca href=\"https://github.com/qwenlm/qwen-audio\"\u003eqwenlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qwenlm/qwen-audio\"\u003eQwen-Audio\u003c/a\u003e\u003c/b\u003e ⭐ 1,865    \n   The official repo of Qwen-Audio (通义千问-Audio) chat \u0026 pretrained large audio language model proposed by Alibaba Cloud.  \n\n276. \u003ca href=\"https://github.com/ray-project/llm-applications\"\u003eray-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ray-project/llm-applications\"\u003ellm-applications\u003c/a\u003e\u003c/b\u003e ⭐ 1,844    \n   A comprehensive guide to building RAG-based LLM applications for production.  \n\n277. \u003ca href=\"https://github.com/openai/gpt-discord-bot\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/gpt-discord-bot\"\u003egpt-discord-bot\u003c/a\u003e\u003c/b\u003e ⭐ 1,831    \n   Example Discord bot written in Python that uses the completions API to have conversations with the `text-davinci-003` model, and the moderations API to filter the messages.  \n\n278. \u003ca href=\"https://github.com/jennyzzt/dgm\"\u003ejennyzzt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jennyzzt/dgm\"\u003edgm\u003c/a\u003e\u003c/b\u003e ⭐ 1,800    \n   Self-improving system that iteratively modifies its own code and empirically validates each change  \n\n279. \u003ca href=\"https://github.com/1rgs/nanocode\"\u003e1rgs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/1rgs/nanocode\"\u003enanocode\u003c/a\u003e\u003c/b\u003e ⭐ 1,669    \n   Minimal Claude Code alternative. Single Python file, zero dependencies, ~250 lines.  \n\n280. \u003ca href=\"https://github.com/alexzhang13/rlm\"\u003ealexzhang13/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alexzhang13/rlm\"\u003erlm\u003c/a\u003e\u003c/b\u003e ⭐ 1,668    \n   Recursive Language Models (RLMs) are a task-agnostic inference paradigm for language models (LMs) to handle near-infinite length contexts  \n   🔗 [arxiv.org/abs/2512.24601v1](https://arxiv.org/abs/2512.24601v1)  \n\n281. \u003ca href=\"https://github.com/meetkai/functionary\"\u003emeetkai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meetkai/functionary\"\u003efunctionary\u003c/a\u003e\u003c/b\u003e ⭐ 1,592    \n   Chat language model that can use tools and interpret the results  \n\n282. \u003ca href=\"https://github.com/answerdotai/rerankers\"\u003eanswerdotai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/answerdotai/rerankers\"\u003ererankers\u003c/a\u003e\u003c/b\u003e ⭐ 1,590    \n   Welcome to rerankers! Our goal is to provide users with a simple API to use any reranking models.  \n\n283. \u003ca href=\"https://github.com/jina-ai/thinkgpt\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/thinkgpt\"\u003ethinkgpt\u003c/a\u003e\u003c/b\u003e ⭐ 1,583    \n   Agent techniques to augment your LLM and push it beyong its limits  \n\n284. \u003ca href=\"https://github.com/leochlon/hallbayes\"\u003eleochlon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/leochlon/hallbayes\"\u003epythea\u003c/a\u003e\u003c/b\u003e ⭐ 1,578    \n   Hallucination Risk Calculator \u0026 Prompt Re-engineering Toolkit (OpenAI-only)  \n\n285. \u003ca href=\"https://github.com/run-llama/semtools\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/semtools\"\u003esemtools\u003c/a\u003e\u003c/b\u003e ⭐ 1,563    \n   Semantic search and document parsing tools for the command line  \n\n286. \u003ca href=\"https://github.com/chatarena/chatarena\"\u003echatarena/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chatarena/chatarena\"\u003echatarena\u003c/a\u003e\u003c/b\u003e ⭐ 1,529    \n   ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.  \n\n287. \u003ca href=\"https://github.com/nirdiamant/controllable-rag-agent\"\u003enirdiamant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nirdiamant/controllable-rag-agent\"\u003eControllable-RAG-Agent\u003c/a\u003e\u003c/b\u003e ⭐ 1,529    \n   An advanced Retrieval-Augmented Generation (RAG) solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve  \n\n288. \u003ca href=\"https://github.com/run-llama/llama-lab\"\u003erun-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/run-llama/llama-lab\"\u003ellama-lab\u003c/a\u003e\u003c/b\u003e ⭐ 1,514    \n   Llama Lab is a repo dedicated to building cutting-edge projects using LlamaIndex  \n\n289. \u003ca href=\"https://github.com/mlc-ai/xgrammar\"\u003emlc-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlc-ai/xgrammar\"\u003exgrammar\u003c/a\u003e\u003c/b\u003e ⭐ 1,502    \n   XGrammar is an open-source library for efficient, flexible, and portable structured generation. It supports general context-free grammar to enable a broad range of structures while bringing careful system optimizations to enable fast executions.  \n   🔗 [xgrammar.mlc.ai/docs](https://xgrammar.mlc.ai/docs)  \n\n290. \u003ca href=\"https://github.com/cstankonrad/long_llama\"\u003ecstankonrad/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cstankonrad/long_llama\"\u003elong_llama\u003c/a\u003e\u003c/b\u003e ⭐ 1,463    \n   LongLLaMA is a large language model capable of handling long contexts. It is based on OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method.  \n\n291. \u003ca href=\"https://github.com/farizrahman4u/loopgpt\"\u003efarizrahman4u/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/farizrahman4u/loopgpt\"\u003eloopgpt\u003c/a\u003e\u003c/b\u003e ⭐ 1,459    \n   Re-implementation of Auto-GPT as a python package, written with modularity and extensibility in mind.  \n\n292. \u003ca href=\"https://github.com/sumandora/remove-refusals-with-transformers\"\u003esumandora/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sumandora/remove-refusals-with-transformers\"\u003eremove-refusals-with-transformers\u003c/a\u003e\u003c/b\u003e ⭐ 1,445    \n   A proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens  \n\n293. \u003ca href=\"https://github.com/mlfoundations/dclm\"\u003emlfoundations/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlfoundations/dclm\"\u003edclm\u003c/a\u003e\u003c/b\u003e ⭐ 1,409    \n   DataComp for Language Models  \n\n294. \u003ca href=\"https://github.com/facebookresearch/mobilellm\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/mobilellm\"\u003eMobileLLM\u003c/a\u003e\u003c/b\u003e ⭐ 1,405    \n   Training code of MobileLLM introduced in our work: \"MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases\"  \n\n295. \u003ca href=\"https://github.com/protectai/rebuff\"\u003eprotectai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/protectai/rebuff\"\u003erebuff\u003c/a\u003e\u003c/b\u003e ⭐ 1,399    \n   Rebuff is designed to protect AI applications from prompt injection (PI) attacks through a multi-layered defense  \n   🔗 [playground.rebuff.ai](https://playground.rebuff.ai)  \n\n296. \u003ca href=\"https://github.com/explosion/spacy-llm\"\u003eexplosion/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/explosion/spacy-llm\"\u003espacy-llm\u003c/a\u003e\u003c/b\u003e ⭐ 1,361    \n   🦙 Integrating LLMs into structured NLP pipelines  \n   🔗 [spacy.io/usage/large-language-models](https://spacy.io/usage/large-language-models)  \n\n297. \u003ca href=\"https://github.com/deepseek-ai/eplb\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/eplb\"\u003eEPLB\u003c/a\u003e\u003c/b\u003e ⭐ 1,336    \n   Expert Parallelism Load Balancer across GPUs  \n\n298. \u003ca href=\"https://github.com/keirp/automatic_prompt_engineer\"\u003ekeirp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/keirp/automatic_prompt_engineer\"\u003eautomatic_prompt_engineer\u003c/a\u003e\u003c/b\u003e ⭐ 1,336    \n   This repo contains code for the paper \"Large Language Models Are Human-Level Prompt Engineers\"  \n\n299. \u003ca href=\"https://github.com/hao-ai-lab/lookaheaddecoding\"\u003ehao-ai-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hao-ai-lab/lookaheaddecoding\"\u003eLookaheadDecoding\u003c/a\u003e\u003c/b\u003e ⭐ 1,315    \n   Break the Sequential Dependency of LLM Inference Using Lookahead Decoding  \n   🔗 [arxiv.org/abs/2402.02057](https://arxiv.org/abs/2402.02057)  \n\n300. \u003ca href=\"https://github.com/centerforaisafety/hle\"\u003ecenterforaisafety/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/centerforaisafety/hle\"\u003ehle\u003c/a\u003e\u003c/b\u003e ⭐ 1,312    \n   Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage  \n   🔗 [lastexam.ai](https://lastexam.ai)  \n\n301. \u003ca href=\"https://github.com/ray-project/ray-llm\"\u003eray-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ray-project/ray-llm\"\u003eray-llm\u003c/a\u003e\u003c/b\u003e ⭐ 1,264    \n   RayLLM - LLMs on Ray (Archived). Read README for more info.  \n   🔗 [docs.ray.io/en/latest](https://docs.ray.io/en/latest/)  \n\n302. \u003ca href=\"https://github.com/srush/minichain\"\u003esrush/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/srush/minichain\"\u003eMiniChain\u003c/a\u003e\u003c/b\u003e ⭐ 1,235    \n   A tiny library for coding with large language models.  \n   🔗 [srush-minichain.hf.space](https://srush-minichain.hf.space/)  \n\n303. \u003ca href=\"https://github.com/nousresearch/hermes-function-calling\"\u003enousresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nousresearch/hermes-function-calling\"\u003eHermes-Function-Calling\u003c/a\u003e\u003c/b\u003e ⭐ 1,188    \n   Code for the Hermes Pro Large Language Model to perform function calling based on the provided schema. It allows users to query the model and retrieve information related to stock prices, company fundamentals, financial statements  \n\n304. \u003ca href=\"https://github.com/cagostino/npcsh\"\u003ecagostino/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cagostino/npcsh\"\u003enpcpy\u003c/a\u003e\u003c/b\u003e ⭐ 1,177    \n   This repo leverages the power of LLMs to understand your natural language commands and questions, executing tasks, answering queries, and providing relevant information from local files and the web.  \n\n305. \u003ca href=\"https://github.com/cyberark/fuzzyai\"\u003ecyberark/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cyberark/fuzzyai\"\u003eFuzzyAI\u003c/a\u003e\u003c/b\u003e ⭐ 1,155    \n   A powerful tool for automated LLM fuzzing. It is designed to help developers and security researchers identify and mitigate potential jailbreaks in their LLM APIs.  \n\n306. \u003ca href=\"https://github.com/ibm/dromedary\"\u003eibm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ibm/dromedary\"\u003eDromedary\u003c/a\u003e\u003c/b\u003e ⭐ 1,144    \n   Dromedary: towards helpful, ethical and reliable LLMs.  \n\n307. \u003ca href=\"https://github.com/lupantech/chameleon-llm\"\u003elupantech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lupantech/chameleon-llm\"\u003echameleon-llm\u003c/a\u003e\u003c/b\u003e ⭐ 1,139    \n   Codes for \"Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models\".  \n   🔗 [chameleon-llm.github.io](https://chameleon-llm.github.io)  \n\n308. \u003ca href=\"https://github.com/safety-research/bloom\"\u003esafety-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/safety-research/bloom\"\u003ebloom\u003c/a\u003e\u003c/b\u003e ⭐ 1,132    \n   Bloom generates evaluation suites that probe LLMs for specific behaviors (sycophancy, self-preservation, political bias, etc.)  \n\n309. \u003ca href=\"https://github.com/utkusen/promptmap\"\u003eutkusen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/utkusen/promptmap\"\u003epromptmap\u003c/a\u003e\u003c/b\u003e ⭐ 1,098    \n   Vulnerability scanning tool that automatically tests prompt injection attacks on your LLM applications. It analyzes your LLM system prompts, runs them, and sends attack prompts to them.  \n\n310. \u003ca href=\"https://github.com/rlancemartin/auto-evaluator\"\u003erlancemartin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rlancemartin/auto-evaluator\"\u003eauto-evaluator\u003c/a\u003e\u003c/b\u003e ⭐ 1,091    \n   Evaluation tool for LLM QA chains  \n   🔗 [autoevaluator.langchain.com](https://autoevaluator.langchain.com/)  \n\n311. \u003ca href=\"https://github.com/datadreamer-dev/datadreamer\"\u003edatadreamer-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/datadreamer-dev/datadreamer\"\u003eDataDreamer\u003c/a\u003e\u003c/b\u003e ⭐ 1,088    \n   DataDreamer is a powerful open-source Python library for prompting, synthetic data generation, and training workflows. It is designed to be simple, extremely efficient, and research-grade.  \n   🔗 [datadreamer.dev](https://datadreamer.dev)  \n\n312. \u003ca href=\"https://github.com/ctlllll/llm-toolmaker\"\u003ectlllll/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ctlllll/llm-toolmaker\"\u003eLLM-ToolMaker\u003c/a\u003e\u003c/b\u003e ⭐ 1,056    \n   Large Language Models as Tool Makers  \n\n313. \u003ca href=\"https://github.com/wandb/weave\"\u003ewandb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wandb/weave\"\u003eweave\u003c/a\u003e\u003c/b\u003e ⭐ 1,047    \n   Weave is a toolkit for developing AI-powered applications, built by Weights \u0026 Biases.  \n   🔗 [wandb.me/weave](https://wandb.me/weave)  \n\n314. \u003ca href=\"https://github.com/prometheus-eval/prometheus-eval\"\u003eprometheus-eval/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prometheus-eval/prometheus-eval\"\u003eprometheus-eval\u003c/a\u003e\u003c/b\u003e ⭐ 1,032    \n   Evaluate your LLM's response with Prometheus and GPT4 💯  \n\n315. \u003ca href=\"https://github.com/pinecone-io/canopy\"\u003epinecone-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pinecone-io/canopy\"\u003ecanopy\u003c/a\u003e\u003c/b\u003e ⭐ 1,027    \n   Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone  \n   🔗 [www.pinecone.io](https://www.pinecone.io/)  \n\n316. \u003ca href=\"https://github.com/huggingface/optimum-nvidia\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/optimum-nvidia\"\u003eoptimum-nvidia\u003c/a\u003e\u003c/b\u003e ⭐ 1,027    \n   Optimum-NVIDIA delivers the best inference performance on the NVIDIA platform through Hugging Face. Run LLaMA 2 at 1,200 tokens/second (up to 28x faster than the framework)  \n\n317. \u003ca href=\"https://github.com/microsoft/llama-2-onnx\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/llama-2-onnx\"\u003eLlama-2-Onnx\u003c/a\u003e\u003c/b\u003e ⭐ 1,026    \n   A Microsoft optimized version of the Llama 2 model, available from Meta  \n\n318. \u003ca href=\"https://github.com/nomic-ai/pygpt4all\"\u003enomic-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nomic-ai/pygpt4all\"\u003epygpt4all\u003c/a\u003e\u003c/b\u003e ⭐ 1,016    \n   Official supported Python bindings for llama.cpp + gpt4all  \n   🔗 [nomic-ai.github.io/pygpt4all](https://nomic-ai.github.io/pygpt4all/)  \n\n319. \u003ca href=\"https://github.com/langchain-ai/langsmith-cookbook\"\u003elangchain-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langchain-ai/langsmith-cookbook\"\u003elangsmith-cookbook\u003c/a\u003e\u003c/b\u003e ⭐ 997    \n   LangSmith is a platform for building production-grade LLM applications.  \n   🔗 [langsmith-cookbook.vercel.app](https://langsmith-cookbook.vercel.app)  \n\n320. \u003ca href=\"https://github.com/ajndkr/lanarky\"\u003eajndkr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ajndkr/lanarky\"\u003elanarky\u003c/a\u003e\u003c/b\u003e ⭐ 995    \n   The web framework for building LLM microservices [deprecated]  \n   🔗 [lanarky.ajndkr.com](https://lanarky.ajndkr.com/)  \n\n321. \u003ca href=\"https://github.com/likejazz/llama3.np\"\u003elikejazz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/likejazz/llama3.np\"\u003ellama3.np\u003c/a\u003e\u003c/b\u003e ⭐ 992    \n   llama3.np is a pure NumPy implementation for Llama 3 model.  \n\n322. \u003ca href=\"https://github.com/thinking-machines-lab/batch_invariant_ops\"\u003ethinking-machines-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thinking-machines-lab/batch_invariant_ops\"\u003ebatch_invariant_ops\u003c/a\u003e\u003c/b\u003e ⭐ 951    \n   Defeating Nondeterminism in LLM Inference: fixing floating-point non-associativity  \n\n323. \u003ca href=\"https://github.com/soulter/hugging-chat-api\"\u003esoulter/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/soulter/hugging-chat-api\"\u003ehugging-chat-api\u003c/a\u003e\u003c/b\u003e ⭐ 936    \n   HuggingChat Python API🤗  \n\n324. \u003ca href=\"https://github.com/opengvlab/omniquant\"\u003eopengvlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/opengvlab/omniquant\"\u003eOmniQuant\u003c/a\u003e\u003c/b\u003e ⭐ 886    \n   [ICLR2024 spotlight] OmniQuant is a simple and powerful quantization technique for LLMs.   \n\n325. \u003ca href=\"https://github.com/salesforceairesearch/promptomatix\"\u003esalesforceairesearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforceairesearch/promptomatix\"\u003epromptomatix\u003c/a\u003e\u003c/b\u003e ⭐ 878    \n   An Automatic Prompt Optimization Framework. Structured approach to prompt optimization, ensuring consistency, cost-effectiveness, and high-quality outputs  \n\n326. \u003ca href=\"https://github.com/bytedtsinghua-sia/memagent\"\u003ebytedtsinghua-sia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedtsinghua-sia/memagent\"\u003eMemAgent\u003c/a\u003e\u003c/b\u003e ⭐ 871    \n   A MemAgent framework that can be extrapolated to 3.5M, along with a training framework for RL training of any agent workflow.  \n\n327. \u003ca href=\"https://github.com/safety-research/petri\"\u003esafety-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/safety-research/petri\"\u003epetri\u003c/a\u003e\u003c/b\u003e ⭐ 847    \n   Autonomously crafts environments, runs multi‑turn audits against a target model using human‑like messages and simulated tools, and then scores transcripts  \n   🔗 [safety-research.github.io/petri](https://safety-research.github.io/petri/)  \n\n328. \u003ca href=\"https://github.com/facebookresearch/cwm\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/cwm\"\u003ecwm\u003c/a\u003e\u003c/b\u003e ⭐ 804    \n   Code World Model (CWM) is a 32-billion-parameter open-weights LLM, to advance research on code generation with world models.  \n\n329. \u003ca href=\"https://github.com/junruxiong/incarnamind\"\u003ejunruxiong/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/junruxiong/incarnamind\"\u003eIncarnaMind\u003c/a\u003e\u003c/b\u003e ⭐ 797    \n   Connect and chat with your multiple documents (pdf and txt) through GPT 3.5, GPT-4 Turbo, Claude and Local Open-Source LLMs  \n   🔗 [www.incarnamind.com](https://www.incarnamind.com)  \n\n330. \u003ca href=\"https://github.com/tag-research/tag-bench\"\u003etag-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tag-research/tag-bench\"\u003eTAG-Bench\u003c/a\u003e\u003c/b\u003e ⭐ 766    \n   Table-Augmented Generation (TAG) is a unified and general-purpose paradigm for answering natural language questions over databases  \n   🔗 [arxiv.org/pdf/2408.14717](https://arxiv.org/pdf/2408.14717)  \n\n331. \u003ca href=\"https://github.com/meta-llama/prompt-ops\"\u003emeta-llama/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meta-llama/prompt-ops\"\u003eprompt-ops\u003c/a\u003e\u003c/b\u003e ⭐ 752    \n   PDO (Prompt Duel Optimizer) - an efficient label-free prompt optimization method using dueling bandits and Thompson sampling  \n\n332. \u003ca href=\"https://github.com/developersdigest/llm-api-engine\"\u003edevelopersdigest/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/developersdigest/llm-api-engine\"\u003ellm-api-engine\u003c/a\u003e\u003c/b\u003e ⭐ 750    \n   Build and deploy AI-powered APIs in seconds. This project allows you to create custom APIs that extract structured data from websites using natural language descriptions, powered by LLMs and web scraping technology.  \n   🔗 [www.youtube.com/watch?v=8kuek1bo4mm](https://www.youtube.com/watch?v=8kUeK1Bo4mM)  \n\n333. \u003ca href=\"https://github.com/microsoft/sammo\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/sammo\"\u003esammo\u003c/a\u003e\u003c/b\u003e ⭐ 747    \n   A library for prompt engineering and optimization (SAMMO = Structure-aware Multi-Objective Metaprompt Optimization)  \n\n334. \u003ca href=\"https://github.com/metauto-ai/agent-as-a-judge\"\u003emetauto-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/metauto-ai/agent-as-a-judge\"\u003eagent-as-a-judge\u003c/a\u003e\u003c/b\u003e ⭐ 717    \n   👩‍⚖️ Agent-as-a-Judge: The Magic for Open-Endedness  \n   🔗 [arxiv.org/pdf/2410.10934](https://arxiv.org/pdf/2410.10934)  \n\n335. \u003ca href=\"https://github.com/microsoft/vptq\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/vptq\"\u003eVPTQ\u003c/a\u003e\u003c/b\u003e ⭐ 674    \n   Extreme Low-bit Vector Post-Training Quantization for Large Language Models  \n\n336. \u003ca href=\"https://github.com/modal-labs/llm-finetuning\"\u003emodal-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modal-labs/llm-finetuning\"\u003ellm-finetuning\u003c/a\u003e\u003c/b\u003e ⭐ 647    \n   Guide for fine-tuning Llama/Mistral/CodeLlama models and more  \n\n337. \u003ca href=\"https://github.com/qixucen/atom\"\u003eqixucen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qixucen/atom\"\u003eatom\u003c/a\u003e\u003c/b\u003e ⭐ 637    \n   Atom of Thoughts (AoT) is a new reasoning framework that represents the solution as a composition of atomic questions. This approach transforms the reasoning process into a Markov process with atomic states  \n\n338. \u003ca href=\"https://github.com/judahpaul16/gpt-home\"\u003ejudahpaul16/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/judahpaul16/gpt-home\"\u003egpt-home\u003c/a\u003e\u003c/b\u003e ⭐ 633    \n   ChatGPT at home! A better alternative to commercial smart home assistants, built on the Raspberry Pi using LiteLLM and LangGraph.  \n   🔗 [hub.docker.com/r/judahpaul/gpt-home](https://hub.docker.com/r/judahpaul/gpt-home)  \n\n339. \u003ca href=\"https://github.com/huggingface/text-clustering\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/text-clustering\"\u003etext-clustering\u003c/a\u003e\u003c/b\u003e ⭐ 592    \n   Easily embed, cluster and semantically label text datasets  \n\n340. \u003ca href=\"https://github.com/deepseek-ai/deepseek-prover-v1.5\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/deepseek-prover-v1.5\"\u003eDeepSeek-Prover-V1.5\u003c/a\u003e\u003c/b\u003e ⭐ 553    \n   DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search  \n\n341. \u003ca href=\"https://github.com/continuum-llms/chatgpt-memory\"\u003econtinuum-llms/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/continuum-llms/chatgpt-memory\"\u003echatgpt-memory\u003c/a\u003e\u003c/b\u003e ⭐ 535    \n   Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore.  \n\n342. \u003ca href=\"https://github.com/codelion/adaptive-classifier\"\u003ecodelion/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/codelion/adaptive-classifier\"\u003eadaptive-classifier\u003c/a\u003e\u003c/b\u003e ⭐ 526    \n   A flexible, adaptive classification system that allows for dynamic addition of new classes and continuous learning from examples. Built on top of transformers from HuggingFace, this library provides an easy-to-use interface for creating and updating text classifiers.  \n\n343. \u003ca href=\"https://github.com/xaviviro/python-toon\"\u003exaviviro/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xaviviro/python-toon\"\u003epython-toon\u003c/a\u003e\u003c/b\u003e ⭐ 320    \n   A compact data format optimized for transmitting structured information to Large Language Models (LLMs) with 30-60% fewer tokens than JSON.  \n\n344. \u003ca href=\"https://github.com/quotient-ai/judges\"\u003equotient-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quotient-ai/judges\"\u003ejudges\u003c/a\u003e\u003c/b\u003e ⭐ 316    \n   judges is a small library to use and create LLM-as-a-Judge evaluators. The purpose of judges is to have a curated set of LLM evaluators in a low-friction format across a variety of use cases  \n\n345. \u003ca href=\"https://github.com/stanford-oval/suql\"\u003estanford-oval/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stanford-oval/suql\"\u003esuql\u003c/a\u003e\u003c/b\u003e ⭐ 296    \n   SUQL: Conversational Search over Structured and Unstructured Data with LLMs  \n   🔗 [arxiv.org/abs/2311.09818](https://arxiv.org/abs/2311.09818)  \n\n346. \u003ca href=\"https://github.com/emissary-tech/legit-rag\"\u003eemissary-tech/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/emissary-tech/legit-rag\"\u003elegit-rag\u003c/a\u003e\u003c/b\u003e ⭐ 274    \n   A modular Retrieval-Augmented Generation (RAG) system built with FastAPI, Qdrant, and OpenAI.  \n\n347. \u003ca href=\"https://github.com/dottxt-ai/outlines-core\"\u003edottxt-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dottxt-ai/outlines-core\"\u003eoutlines-core\u003c/a\u003e\u003c/b\u003e ⭐ 273    \n   Core functionality for structured generation, formerly implemented in Outlines, with a focus on performance and portability.  \n   🔗 [docs.rs/outlines-core](https://docs.rs/outlines-core)  \n\n348. \u003ca href=\"https://github.com/jina-ai/llm-query-expansion\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/llm-query-expansion\"\u003ellm-query-expansion\u003c/a\u003e\u003c/b\u003e ⭐ 64    \n   Query Expension for Better Query Embedding using LLMs  \n\n## Math and Science\n\nMathematical, numerical and scientific libraries.  \n\n1. \u003ca href=\"https://github.com/numpy/numpy\"\u003enumpy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/numpy/numpy\"\u003enumpy\u003c/a\u003e\u003c/b\u003e ⭐ 31,307    \n   The fundamental package for scientific computing with Python.  \n   🔗 [numpy.org](https://numpy.org)  \n\n2. \u003ca href=\"https://github.com/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers\"\u003ecamdavidsonpilon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers\"\u003eProbabilistic-Programming-and-Bayesian-Methods-for-Hackers\u003c/a\u003e\u003c/b\u003e ⭐ 28,453    \n   aka \"Bayesian Methods for Hackers\": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)    \n   🔗 [camdavidsonpilon.github.io/probabilistic-programming-and-bayesian-methods-for-hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)  \n\n3. \u003ca href=\"https://github.com/taichi-dev/taichi\"\u003etaichi-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/taichi-dev/taichi\"\u003etaichi\u003c/a\u003e\u003c/b\u003e ⭐ 27,916    \n   Productive, portable, and performant GPU programming in Python: Taichi Lang is an open-source, imperative, parallel programming language for high-performance numerical computation.  \n   🔗 [taichi-lang.org](https://taichi-lang.org)  \n\n4. \u003ca href=\"https://github.com/experience-monks/math-as-code\"\u003eexperience-monks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/experience-monks/math-as-code\"\u003emath-as-code\u003c/a\u003e\u003c/b\u003e ⭐ 15,461    \n   This is a reference to ease developers into mathematical notation by showing comparisons with Python code  \n\n5. \u003ca href=\"https://github.com/scipy/scipy\"\u003escipy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/scipy/scipy\"\u003escipy\u003c/a\u003e\u003c/b\u003e ⭐ 14,389    \n   SciPy library main repository  \n   🔗 [scipy.org](https://scipy.org)  \n\n6. \u003ca href=\"https://github.com/sympy/sympy\"\u003esympy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sympy/sympy\"\u003esympy\u003c/a\u003e\u003c/b\u003e ⭐ 14,338    \n   A computer algebra system written in pure Python  \n   🔗 [sympy.org](https://sympy.org/)  \n\n7. \u003ca href=\"https://github.com/google/or-tools\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/or-tools\"\u003eor-tools\u003c/a\u003e\u003c/b\u003e ⭐ 13,016    \n   Google Optimization Tools (a.k.a., OR-Tools) is an open-source, fast and portable software suite for solving combinatorial optimization problems.  \n   🔗 [developers.google.com/optimization](https://developers.google.com/optimization/)  \n\n8. \u003ca href=\"https://github.com/z3prover/z3\"\u003ez3prover/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/z3prover/z3\"\u003ez3\u003c/a\u003e\u003c/b\u003e ⭐ 11,848    \n   Z3 is a theorem prover from Microsoft Research with a Python language binding.  \n\n9. \u003ca href=\"https://github.com/cupy/cupy\"\u003ecupy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cupy/cupy\"\u003ecupy\u003c/a\u003e\u003c/b\u003e ⭐ 10,736    \n   NumPy \u0026 SciPy for GPU  \n   🔗 [cupy.dev](https://cupy.dev)  \n\n10. \u003ca href=\"https://github.com/cvxpy/cvxpy\"\u003ecvxpy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cvxpy/cvxpy\"\u003ecvxpy\u003c/a\u003e\u003c/b\u003e ⭐ 6,096    \n   A Python-embedded modeling language for convex optimization problems.  \n   🔗 [www.cvxpy.org](https://www.cvxpy.org)  \n\n11. \u003ca href=\"https://github.com/google-deepmind/alphageometry\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/alphageometry\"\u003ealphageometry\u003c/a\u003e\u003c/b\u003e ⭐ 4,755    \n   Solving Olympiad Geometry without Human Demonstrations  \n\n12. \u003ca href=\"https://github.com/pim-book/programmers-introduction-to-mathematics\"\u003epim-book/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pim-book/programmers-introduction-to-mathematics\"\u003eprogrammers-introduction-to-mathematics\u003c/a\u003e\u003c/b\u003e ⭐ 3,635    \n   Code for A Programmer's Introduction to Mathematics  \n   🔗 [pimbook.org](https://pimbook.org)  \n\n13. \u003ca href=\"https://github.com/talalalrawajfeh/mathematics-roadmap\"\u003etalalalrawajfeh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/talalalrawajfeh/mathematics-roadmap\"\u003emathematics-roadmap\u003c/a\u003e\u003c/b\u003e ⭐ 3,319    \n   A Comprehensive Roadmap to Mathematics  \n\n14. \u003ca href=\"https://github.com/pyro-ppl/numpyro\"\u003epyro-ppl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyro-ppl/numpyro\"\u003enumpyro\u003c/a\u003e\u003c/b\u003e ⭐ 2,598    \n   Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.  \n   🔗 [num.pyro.ai](https://num.pyro.ai)  \n\n15. \u003ca href=\"https://github.com/mckinsey/causalnex\"\u003emckinsey/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mckinsey/causalnex\"\u003ecausalnex\u003c/a\u003e\u003c/b\u003e ⭐ 2,426    \n   A Python library that helps data scientists to infer causation rather than observing correlation.  \n   🔗 [causalnex.readthedocs.io](http://causalnex.readthedocs.io/)  \n\n16. \u003ca href=\"https://github.com/facebookresearch/theseus\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/theseus\"\u003etheseus\u003c/a\u003e\u003c/b\u003e ⭐ 1,992    \n   A library for differentiable nonlinear optimization  \n\n17. \u003ca href=\"https://github.com/pymc-labs/causalpy\"\u003epymc-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pymc-labs/causalpy\"\u003eCausalPy\u003c/a\u003e\u003c/b\u003e ⭐ 1,093    \n   A Python package for causal inference in quasi-experimental settings  \n   🔗 [causalpy.readthedocs.io](https://causalpy.readthedocs.io)  \n\n18. \u003ca href=\"https://github.com/extropic-ai/thrml\"\u003eextropic-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/extropic-ai/thrml\"\u003ethrml\u003c/a\u003e\u003c/b\u003e ⭐ 987    \n   A JAX library for building and sampling probabilistic graphical models, with a focus on efficient block Gibbs sampling and energy-based models  \n   🔗 [docs.thrml.ai](https://docs.thrml.ai/)  \n\n19. \u003ca href=\"https://github.com/brandondube/prysm\"\u003ebrandondube/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/brandondube/prysm\"\u003eprysm\u003c/a\u003e\u003c/b\u003e ⭐ 322    \n   Prysm is an open-source library for physical and first-order modeling of optical systems and analysis of related data: numerical and physical optics, integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing.  \n   🔗 [prysm.readthedocs.io/en/stable](https://prysm.readthedocs.io/en/stable/)  \n\n20. \u003ca href=\"https://github.com/lean-dojo/reprover\"\u003elean-dojo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lean-dojo/reprover\"\u003eReProver\u003c/a\u003e\u003c/b\u003e ⭐ 316    \n   Retrieval-Augmented Theorem Provers for Lean  \n   🔗 [leandojo.org/leandojo.html](https://leandojo.org/leandojo.html)  \n\n21. \u003ca href=\"https://github.com/albahnsen/pycircular\"\u003ealbahnsen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/albahnsen/pycircular\"\u003epycircular\u003c/a\u003e\u003c/b\u003e ⭐ 105    \n   pycircular is a Python module for circular data analysis  \n\n22. \u003ca href=\"https://github.com/gbillotey/fractalshades\"\u003egbillotey/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gbillotey/fractalshades\"\u003eFractalshades\u003c/a\u003e\u003c/b\u003e ⭐ 35    \n   Arbitrary-precision fractal explorer - Python package  \n\n## Machine Learning - General\n\nGeneral and classical machine learning libraries. See below for other sections covering specialised ML areas.  \n\n1. \u003ca href=\"https://github.com/openai/openai-cookbook\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/openai-cookbook\"\u003eopenai-cookbook\u003c/a\u003e\u003c/b\u003e ⭐ 71,101    \n   Examples and guides for using the OpenAI API  \n   🔗 [cookbook.openai.com](https://cookbook.openai.com)  \n\n2. \u003ca href=\"https://github.com/scikit-learn/scikit-learn\"\u003escikit-learn/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/scikit-learn/scikit-learn\"\u003escikit-learn\u003c/a\u003e\u003c/b\u003e ⭐ 64,753    \n   scikit-learn: machine learning in Python  \n   🔗 [scikit-learn.org](https://scikit-learn.org)  \n\n3. \u003ca href=\"https://github.com/suno-ai/bark\"\u003esuno-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/suno-ai/bark\"\u003ebark\u003c/a\u003e\u003c/b\u003e ⭐ 38,929    \n   🔊 Text-Prompted Generative Audio Model  \n\n4. \u003ca href=\"https://github.com/facebookresearch/faiss\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/faiss\"\u003efaiss\u003c/a\u003e\u003c/b\u003e ⭐ 38,864    \n   A library for efficient similarity search and clustering of dense vectors.  \n   🔗 [faiss.ai](https://faiss.ai)  \n\n5. \u003ca href=\"https://github.com/tencentarc/gfpgan\"\u003etencentarc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tencentarc/gfpgan\"\u003eGFPGAN\u003c/a\u003e\u003c/b\u003e ⭐ 37,353    \n   GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.  \n\n6. \u003ca href=\"https://github.com/google-research/google-research\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/google-research\"\u003egoogle-research\u003c/a\u003e\u003c/b\u003e ⭐ 37,132    \n   This repository contains code released by Google Research  \n   🔗 [research.google](https://research.google)  \n\n7. \u003ca href=\"https://github.com/roboflow/supervision\"\u003eroboflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/roboflow/supervision\"\u003esupervision\u003c/a\u003e\u003c/b\u003e ⭐ 36,378    \n   We write your reusable computer vision tools. 💜  \n   🔗 [supervision.roboflow.com](https://supervision.roboflow.com)  \n\n8. \u003ca href=\"https://github.com/google/jax\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/jax\"\u003ejax\u003c/a\u003e\u003c/b\u003e ⭐ 34,681    \n   Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more  \n   🔗 [docs.jax.dev](https://docs.jax.dev)  \n\n9. \u003ca href=\"https://github.com/google/mediapipe\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/mediapipe\"\u003emediapipe\u003c/a\u003e\u003c/b\u003e ⭐ 33,447    \n   Cross-platform, customizable ML solutions for live and streaming media.  \n   🔗 [ai.google.dev/edge/mediapipe](https://ai.google.dev/edge/mediapipe)  \n\n10. \u003ca href=\"https://github.com/open-mmlab/mmdetection\"\u003eopen-mmlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-mmlab/mmdetection\"\u003emmdetection\u003c/a\u003e\u003c/b\u003e ⭐ 32,319    \n   OpenMMLab Detection Toolbox and Benchmark  \n   🔗 [mmdetection.readthedocs.io](https://mmdetection.readthedocs.io)  \n\n11. \u003ca href=\"https://github.com/lutzroeder/netron\"\u003elutzroeder/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lutzroeder/netron\"\u003enetron\u003c/a\u003e\u003c/b\u003e ⭐ 32,250    \n   Visualizer for neural network, deep learning and machine learning models  \n   🔗 [netron.app](https://netron.app)  \n\n12. \u003ca href=\"https://github.com/ageron/handson-ml2\"\u003eageron/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ageron/handson-ml2\"\u003ehandson-ml2\u003c/a\u003e\u003c/b\u003e ⭐ 29,799    \n   A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.  \n\n13. \u003ca href=\"https://github.com/dmlc/xgboost\"\u003edmlc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dmlc/xgboost\"\u003exgboost\u003c/a\u003e\u003c/b\u003e ⭐ 27,898    \n   Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library,  for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow  \n   🔗 [xgboost.readthedocs.io](https://xgboost.readthedocs.io/)  \n\n14. \u003ca href=\"https://github.com/facebookresearch/fasttext\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/fasttext\"\u003efastText\u003c/a\u003e\u003c/b\u003e ⭐ 26,471    \n   A library for efficient learning of word representations and sentence classification.  \n   🔗 [fasttext.cc](https://fasttext.cc/)  \n\n15. \u003ca href=\"https://github.com/modular/max\"\u003emodular/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modular/max\"\u003emodular\u003c/a\u003e\u003c/b\u003e ⭐ 25,497    \n   The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows  \n   🔗 [docs.modular.com](https://docs.modular.com/)  \n\n16. \u003ca href=\"https://github.com/harisiqbal88/plotneuralnet\"\u003eharisiqbal88/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/harisiqbal88/plotneuralnet\"\u003ePlotNeuralNet\u003c/a\u003e\u003c/b\u003e ⭐ 24,366    \n   Latex code for making neural networks diagrams  \n\n17. \u003ca href=\"https://github.com/ml-explore/mlx\"\u003eml-explore/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ml-explore/mlx\"\u003emlx\u003c/a\u003e\u003c/b\u003e ⭐ 23,585    \n   MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.  \n   🔗 [ml-explore.github.io/mlx](https://ml-explore.github.io/mlx/)  \n\n18. \u003ca href=\"https://github.com/jina-ai/jina\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/jina\"\u003eserve\u003c/a\u003e\u003c/b\u003e ⭐ 21,828    \n   ☁️ Build multimodal AI applications with cloud-native stack  \n   🔗 [jina.ai/serve](https://jina.ai/serve)  \n\n19. \u003ca href=\"https://github.com/onnx/onnx\"\u003eonnx/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/onnx/onnx\"\u003eonnx\u003c/a\u003e\u003c/b\u003e ⭐ 20,211    \n   Open standard for machine learning interoperability  \n   🔗 [onnx.ai](https://onnx.ai/)  \n\n20. \u003ca href=\"https://github.com/huggingface/candle\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/candle\"\u003ecandle\u003c/a\u003e\u003c/b\u003e ⭐ 19,143    \n   Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use.  \n\n21. \u003ca href=\"https://github.com/microsoft/onnxruntime\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/onnxruntime\"\u003eonnxruntime\u003c/a\u003e\u003c/b\u003e ⭐ 19,069    \n   ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator  \n   🔗 [onnxruntime.ai](https://onnxruntime.ai)  \n\n22. \u003ca href=\"https://github.com/microsoft/lightgbm\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/lightgbm\"\u003eLightGBM\u003c/a\u003e\u003c/b\u003e ⭐ 18,027    \n   A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.  \n   🔗 [lightgbm.readthedocs.io/en/latest](https://lightgbm.readthedocs.io/en/latest/)  \n\n23. \u003ca href=\"https://github.com/tensorflow/tensor2tensor\"\u003etensorflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tensorflow/tensor2tensor\"\u003etensor2tensor\u003c/a\u003e\u003c/b\u003e ⭐ 16,935    \n   Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.  \n\n24. \u003ca href=\"https://github.com/google-gemini/cookbook\"\u003egoogle-gemini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-gemini/cookbook\"\u003ecookbook\u003c/a\u003e\u003c/b\u003e ⭐ 16,259    \n   A collection of guides and examples for the Gemini API, including quickstart tutorials for writing prompts.  \n   🔗 [ai.google.dev/gemini-api/docs](https://ai.google.dev/gemini-api/docs)  \n\n25. \u003ca href=\"https://github.com/ddbourgin/numpy-ml\"\u003eddbourgin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ddbourgin/numpy-ml\"\u003enumpy-ml\u003c/a\u003e\u003c/b\u003e ⭐ 16,241    \n   Machine learning, in numpy  \n   🔗 [numpy-ml.readthedocs.io](https://numpy-ml.readthedocs.io/)  \n\n26. \u003ca href=\"https://github.com/neonbjb/tortoise-tts\"\u003eneonbjb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neonbjb/tortoise-tts\"\u003etortoise-tts\u003c/a\u003e\u003c/b\u003e ⭐ 14,785    \n   A multi-voice TTS system trained with an emphasis on quality  \n\n27. \u003ca href=\"https://github.com/aleju/imgaug\"\u003ealeju/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aleju/imgaug\"\u003eimgaug\u003c/a\u003e\u003c/b\u003e ⭐ 14,726    \n   Image augmentation for machine learning experiments.  \n   🔗 [imgaug.readthedocs.io](http://imgaug.readthedocs.io)  \n\n28. \u003ca href=\"https://github.com/deepmind/deepmind-research\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/deepmind-research\"\u003edeepmind-research\u003c/a\u003e\u003c/b\u003e ⭐ 14,645    \n   This repository contains implementations and illustrative code to accompany DeepMind publications  \n\n29. \u003ca href=\"https://github.com/microsoft/nni\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/nni\"\u003enni\u003c/a\u003e\u003c/b\u003e ⭐ 14,338    \n   An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.  \n   🔗 [nni.readthedocs.io](https://nni.readthedocs.io)  \n\n30. \u003ca href=\"https://github.com/jindongwang/transferlearning\"\u003ejindongwang/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jindongwang/transferlearning\"\u003etransferlearning\u003c/a\u003e\u003c/b\u003e ⭐ 14,255    \n   Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习  \n   🔗 [transferlearning.xyz](http://transferlearning.xyz/)  \n\n31. \u003ca href=\"https://github.com/deepmind/alphafold\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/alphafold\"\u003ealphafold\u003c/a\u003e\u003c/b\u003e ⭐ 14,224    \n   Implementation of the inference pipeline of AlphaFold v2  \n\n32. \u003ca href=\"https://github.com/spotify/annoy\"\u003espotify/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spotify/annoy\"\u003eannoy\u003c/a\u003e\u003c/b\u003e ⭐ 14,135    \n   Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk  \n\n33. \u003ca href=\"https://github.com/ggerganov/ggml\"\u003eggerganov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ggerganov/ggml\"\u003eggml\u003c/a\u003e\u003c/b\u003e ⭐ 13,866    \n   Tensor library for machine learning  \n\n34. \u003ca href=\"https://github.com/optuna/optuna\"\u003eoptuna/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/optuna/optuna\"\u003eoptuna\u003c/a\u003e\u003c/b\u003e ⭐ 13,421    \n   A hyperparameter optimization framework  \n   🔗 [optuna.org](https://optuna.org)  \n\n35. \u003ca href=\"https://github.com/facebookresearch/animateddrawings\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/animateddrawings\"\u003eAnimatedDrawings\u003c/a\u003e\u003c/b\u003e ⭐ 12,761    \n   Code to accompany \"A Method for Animating Children's Drawings of the Human Figure\"  \n\n36. \u003ca href=\"https://github.com/thudm/cogvideo\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/cogvideo\"\u003eCogVideo\u003c/a\u003e\u003c/b\u003e ⭐ 12,361    \n   text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)  \n\n37. \u003ca href=\"https://github.com/cleanlab/cleanlab\"\u003ecleanlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cleanlab/cleanlab\"\u003ecleanlab\u003c/a\u003e\u003c/b\u003e ⭐ 11,281    \n   Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.  \n   🔗 [cleanlab.ai](https://cleanlab.ai)  \n\n38. \u003ca href=\"https://github.com/statsmodels/statsmodels\"\u003estatsmodels/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/statsmodels/statsmodels\"\u003estatsmodels\u003c/a\u003e\u003c/b\u003e ⭐ 11,210    \n   Statsmodels: statistical modeling and econometrics in Python  \n   🔗 [www.statsmodels.org/devel](http://www.statsmodels.org/devel/)  \n\n39. \u003ca href=\"https://github.com/wandb/client\"\u003ewandb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wandb/client\"\u003ewandb\u003c/a\u003e\u003c/b\u003e ⭐ 10,768    \n   The AI developer platform. Use Weights \u0026 Biases to train and fine-tune models, and manage models from experimentation to production.  \n   🔗 [wandb.ai](https://wandb.ai)  \n\n40. \u003ca href=\"https://github.com/twitter/the-algorithm-ml\"\u003etwitter/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/twitter/the-algorithm-ml\"\u003ethe-algorithm-ml\u003c/a\u003e\u003c/b\u003e ⭐ 10,510    \n   Source code for Twitter's Recommendation Algorithm  \n   🔗 [blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm)  \n\n41. \u003ca href=\"https://github.com/facebookresearch/xformers\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/xformers\"\u003exformers\u003c/a\u003e\u003c/b\u003e ⭐ 10,292    \n   Hackable and optimized Transformers building blocks, supporting a composable construction.  \n   🔗 [facebookresearch.github.io/xformers](https://facebookresearch.github.io/xformers/)  \n\n42. \u003ca href=\"https://github.com/megvii-basedetection/yolox\"\u003emegvii-basedetection/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/megvii-basedetection/yolox\"\u003eYOLOX\u003c/a\u003e\u003c/b\u003e ⭐ 10,291    \n   YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/  \n\n43. \u003ca href=\"https://github.com/epistasislab/tpot\"\u003eepistasislab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/epistasislab/tpot\"\u003etpot\u003c/a\u003e\u003c/b\u003e ⭐ 10,041    \n   A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.  \n   🔗 [epistasislab.github.io/tpot](http://epistasislab.github.io/tpot/)  \n\n44. \u003ca href=\"https://github.com/awslabs/autogluon\"\u003eawslabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/awslabs/autogluon\"\u003eautogluon\u003c/a\u003e\u003c/b\u003e ⭐ 9,828    \n   Fast and Accurate ML in 3 Lines of Code  \n   🔗 [auto.gluon.ai](https://auto.gluon.ai/)  \n\n45. \u003ca href=\"https://github.com/pycaret/pycaret\"\u003epycaret/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycaret/pycaret\"\u003epycaret\u003c/a\u003e\u003c/b\u003e ⭐ 9,677    \n   An open-source, low-code machine learning library in Python  \n   🔗 [www.pycaret.org](https://www.pycaret.org)  \n\n46. \u003ca href=\"https://github.com/open-mmlab/mmsegmentation\"\u003eopen-mmlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation\"\u003emmsegmentation\u003c/a\u003e\u003c/b\u003e ⭐ 9,586    \n   OpenMMLab Semantic Segmentation Toolbox and Benchmark.  \n   🔗 [mmsegmentation.readthedocs.io/en/main](https://mmsegmentation.readthedocs.io/en/main/)  \n\n47. \u003ca href=\"https://github.com/huggingface/accelerate\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/accelerate\"\u003eaccelerate\u003c/a\u003e\u003c/b\u003e ⭐ 9,461    \n   🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support  \n   🔗 [huggingface.co/docs/accelerate](https://huggingface.co/docs/accelerate)  \n\n48. \u003ca href=\"https://github.com/pymc-devs/pymc3\"\u003epymc-devs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pymc-devs/pymc3\"\u003epymc\u003c/a\u003e\u003c/b\u003e ⭐ 9,459    \n   Bayesian Modeling and Probabilistic Programming in Python  \n   🔗 [www.pymc.io](https://www.pymc.io)  \n\n49. \u003ca href=\"https://github.com/uberi/speech_recognition\"\u003euberi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/uberi/speech_recognition\"\u003espeech_recognition\u003c/a\u003e\u003c/b\u003e ⭐ 8,927    \n   Speech recognition module for Python, supporting several engines and APIs, online and offline.  \n   🔗 [pypi.python.org/pypi/speechrecognition](https://pypi.python.org/pypi/SpeechRecognition/)  \n\n50. \u003ca href=\"https://github.com/catboost/catboost\"\u003ecatboost/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/catboost/catboost\"\u003ecatboost\u003c/a\u003e\u003c/b\u003e ⭐ 8,768    \n   A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.  \n   🔗 [catboost.ai](https://catboost.ai)  \n\n51. \u003ca href=\"https://github.com/ml-explore/mlx-examples\"\u003eml-explore/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ml-explore/mlx-examples\"\u003emlx-examples\u003c/a\u003e\u003c/b\u003e ⭐ 8,168    \n   Examples in the MLX framework  \n\n52. \u003ca href=\"https://github.com/lmcinnes/umap\"\u003elmcinnes/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lmcinnes/umap\"\u003eumap\u003c/a\u003e\u003c/b\u003e ⭐ 8,072    \n   Uniform Manifold Approximation and Projection  \n   🔗 [umap-learn.readthedocs.io](https://umap-learn.readthedocs.io)  \n\n53. \u003ca href=\"https://github.com/automl/auto-sklearn\"\u003eautoml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/automl/auto-sklearn\"\u003eauto-sklearn\u003c/a\u003e\u003c/b\u003e ⭐ 8,042    \n   Automated Machine Learning with scikit-learn  \n   🔗 [automl.github.io/auto-sklearn](https://automl.github.io/auto-sklearn)  \n\n54. \u003ca href=\"https://github.com/py-why/dowhy\"\u003epy-why/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/py-why/dowhy\"\u003edowhy\u003c/a\u003e\u003c/b\u003e ⭐ 7,924    \n   DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.   \n   🔗 [www.pywhy.org/dowhy](https://www.pywhy.org/dowhy)  \n\n55. \u003ca href=\"https://github.com/project-monai/monai\"\u003eproject-monai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/project-monai/monai\"\u003eMONAI\u003c/a\u003e\u003c/b\u003e ⭐ 7,773    \n   AI Toolkit for Healthcare Imaging  \n   🔗 [project-monai.github.io](https://project-monai.github.io/)  \n\n56. \u003ca href=\"https://github.com/hyperopt/hyperopt\"\u003ehyperopt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hyperopt/hyperopt\"\u003ehyperopt\u003c/a\u003e\u003c/b\u003e ⭐ 7,608    \n   Distributed Asynchronous Hyperparameter Optimization in Python  \n   🔗 [hyperopt.github.io/hyperopt](http://hyperopt.github.io/hyperopt)  \n\n57. \u003ca href=\"https://github.com/featurelabs/featuretools\"\u003efeaturelabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/featurelabs/featuretools\"\u003efeaturetools\u003c/a\u003e\u003c/b\u003e ⭐ 7,598    \n   An open source python library for automated feature engineering  \n   🔗 [www.featuretools.com](https://www.featuretools.com)  \n\n58. \u003ca href=\"https://github.com/hips/autograd\"\u003ehips/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hips/autograd\"\u003eautograd\u003c/a\u003e\u003c/b\u003e ⭐ 7,446    \n   Efficiently computes derivatives of NumPy code.  \n\n59. \u003ca href=\"https://github.com/open-mmlab/mmediting\"\u003eopen-mmlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-mmlab/mmediting\"\u003emmagic\u003c/a\u003e\u003c/b\u003e ⭐ 7,369    \n   OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.  \n   🔗 [mmagic.readthedocs.io/en/latest](https://mmagic.readthedocs.io/en/latest/)  \n\n60. \u003ca href=\"https://github.com/scikit-learn-contrib/imbalanced-learn\"\u003escikit-learn-contrib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/scikit-learn-contrib/imbalanced-learn\"\u003eimbalanced-learn\u003c/a\u003e\u003c/b\u003e ⭐ 7,078    \n    A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning  \n   🔗 [imbalanced-learn.org](https://imbalanced-learn.org)  \n\n61. \u003ca href=\"https://github.com/yangchris11/samurai\"\u003eyangchris11/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yangchris11/samurai\"\u003esamurai\u003c/a\u003e\u003c/b\u003e ⭐ 7,037    \n   Official repository of \"SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory\"  \n   🔗 [yangchris11.github.io/samurai](https://yangchris11.github.io/samurai/)  \n\n62. \u003ca href=\"https://github.com/probml/pyprobml\"\u003eprobml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/probml/pyprobml\"\u003epyprobml\u003c/a\u003e\u003c/b\u003e ⭐ 6,995    \n   Python code for \"Probabilistic Machine learning\" book by Kevin Murphy  \n\n63. \u003ca href=\"https://github.com/nicolashug/surprise\"\u003enicolashug/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nicolashug/surprise\"\u003eSurprise\u003c/a\u003e\u003c/b\u003e ⭐ 6,757    \n   A Python scikit for building and analyzing recommender systems  \n   🔗 [surpriselib.com](http://surpriselib.com)  \n\n64. \u003ca href=\"https://github.com/google-deepmind/graphcast\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/graphcast\"\u003egraphcast\u003c/a\u003e\u003c/b\u003e ⭐ 6,488    \n   GraphCast: Learning skillful medium-range global weather forecasting  \n\n65. \u003ca href=\"https://github.com/google/automl\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/automl\"\u003eautoml\u003c/a\u003e\u003c/b\u003e ⭐ 6,452    \n   Google Brain AutoML  \n\n66. \u003ca href=\"https://github.com/cleverhans-lab/cleverhans\"\u003ecleverhans-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cleverhans-lab/cleverhans\"\u003ecleverhans\u003c/a\u003e\u003c/b\u003e ⭐ 6,401    \n   An adversarial example library for constructing attacks, building defenses, and benchmarking both  \n\n67. \u003ca href=\"https://github.com/open-mmlab/mmcv\"\u003eopen-mmlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-mmlab/mmcv\"\u003emmcv\u003c/a\u003e\u003c/b\u003e ⭐ 6,388    \n   OpenMMLab Computer Vision Foundation  \n   🔗 [mmcv.readthedocs.io/en/latest](https://mmcv.readthedocs.io/en/latest/)  \n\n68. \u003ca href=\"https://github.com/kevinmusgrave/pytorch-metric-learning\"\u003ekevinmusgrave/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kevinmusgrave/pytorch-metric-learning\"\u003epytorch-metric-learning\u003c/a\u003e\u003c/b\u003e ⭐ 6,298    \n   The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.  \n   🔗 [kevinmusgrave.github.io/pytorch-metric-learning](https://kevinmusgrave.github.io/pytorch-metric-learning/)  \n\n69. \u003ca href=\"https://github.com/uber/causalml\"\u003euber/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/uber/causalml\"\u003ecausalml\u003c/a\u003e\u003c/b\u003e ⭐ 5,704    \n   Uplift modeling and causal inference with machine learning algorithms  \n\n70. \u003ca href=\"https://github.com/online-ml/river\"\u003eonline-ml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/online-ml/river\"\u003eriver\u003c/a\u003e\u003c/b\u003e ⭐ 5,683    \n   🌊 Online machine learning in Python  \n   🔗 [riverml.xyz](https://riverml.xyz)  \n\n71. \u003ca href=\"https://github.com/priorlabs/tabpfn\"\u003epriorlabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/priorlabs/tabpfn\"\u003eTabPFN\u003c/a\u003e\u003c/b\u003e ⭐ 5,562    \n   The TabPFN is a neural network that learned to do tabular data prediction. This is the original CUDA-supporting pytorch impelementation.  \n   🔗 [priorlabs.ai](http://priorlabs.ai)  \n\n72. \u003ca href=\"https://github.com/google-deepmind/graph_nets\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/graph_nets\"\u003egraph_nets\u003c/a\u003e\u003c/b\u003e ⭐ 5,394    \n   Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet.  \n   🔗 [arxiv.org/abs/1806.01261](https://arxiv.org/abs/1806.01261)  \n\n73. \u003ca href=\"https://github.com/skvark/opencv-python\"\u003eskvark/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/skvark/opencv-python\"\u003eopencv-python\u003c/a\u003e\u003c/b\u003e ⭐ 5,165    \n   Automated CI toolchain to produce precompiled opencv-python, opencv-python-headless, opencv-contrib-python and opencv-contrib-python-headless packages.  \n   🔗 [pypi.org/project/opencv-python](https://pypi.org/project/opencv-python/)  \n\n74. \u003ca href=\"https://github.com/mdbloice/augmentor\"\u003emdbloice/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mdbloice/augmentor\"\u003eAugmentor\u003c/a\u003e\u003c/b\u003e ⭐ 5,148    \n   Image augmentation library in Python for machine learning.  \n   🔗 [augmentor.readthedocs.io/en/stable](https://augmentor.readthedocs.io/en/stable)  \n\n75. \u003ca href=\"https://github.com/apple/coremltools\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/coremltools\"\u003ecoremltools\u003c/a\u003e\u003c/b\u003e ⭐ 5,122    \n   Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.  \n   🔗 [coremltools.readme.io](https://coremltools.readme.io)  \n\n76. \u003ca href=\"https://github.com/rasbt/mlxtend\"\u003erasbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasbt/mlxtend\"\u003emlxtend\u003c/a\u003e\u003c/b\u003e ⭐ 5,092    \n   A library of extension and helper modules for Python's data analysis and machine learning libraries.  \n   🔗 [rasbt.github.io/mlxtend](https://rasbt.github.io/mlxtend/)  \n\n77. \u003ca href=\"https://github.com/nmslib/hnswlib\"\u003enmslib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nmslib/hnswlib\"\u003ehnswlib\u003c/a\u003e\u003c/b\u003e ⭐ 5,067    \n   Header-only C++/python library for fast approximate nearest neighbors  \n   🔗 [github.com/nmslib/hnswlib](https://github.com/nmslib/hnswlib)  \n\n78. \u003ca href=\"https://github.com/marqo-ai/marqo\"\u003emarqo-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marqo-ai/marqo\"\u003emarqo\u003c/a\u003e\u003c/b\u003e ⭐ 5,009    \n   Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai  \n   🔗 [www.marqo.ai](https://www.marqo.ai/)  \n\n79. \u003ca href=\"https://github.com/sanchit-gandhi/whisper-jax\"\u003esanchit-gandhi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sanchit-gandhi/whisper-jax\"\u003ewhisper-jax\u003c/a\u003e\u003c/b\u003e ⭐ 4,665    \n   JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.  \n\n80. \u003ca href=\"https://github.com/huggingface/autotrain-advanced\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/autotrain-advanced\"\u003eautotrain-advanced\u003c/a\u003e\u003c/b\u003e ⭐ 4,551    \n   AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models  \n   🔗 [huggingface.co/autotrain](https://huggingface.co/autotrain)  \n\n81. \u003ca href=\"https://github.com/py-why/econml\"\u003epy-why/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/py-why/econml\"\u003eEconML\u003c/a\u003e\u003c/b\u003e ⭐ 4,478    \n   ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its  goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to brin...  \n   🔗 [www.microsoft.com/en-us/research/project/alice](https://www.microsoft.com/en-us/research/project/alice/)  \n\n82. \u003ca href=\"https://github.com/huggingface/notebooks\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/notebooks\"\u003enotebooks\u003c/a\u003e\u003c/b\u003e ⭐ 4,438    \n   Notebooks using the Hugging Face libraries 🤗  \n\n83. \u003ca href=\"https://github.com/nv-tlabs/get3d\"\u003env-tlabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nv-tlabs/get3d\"\u003eGET3D\u003c/a\u003e\u003c/b\u003e ⭐ 4,425    \n   Generative Model of High Quality 3D Textured Shapes Learned from Images  \n\n84. \u003ca href=\"https://github.com/districtdatalabs/yellowbrick\"\u003edistrictdatalabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/districtdatalabs/yellowbrick\"\u003eyellowbrick\u003c/a\u003e\u003c/b\u003e ⭐ 4,395    \n   Visual analysis and diagnostic tools to facilitate machine learning model selection.  \n   🔗 [www.scikit-yb.org](http://www.scikit-yb.org/)  \n\n85. \u003ca href=\"https://github.com/lucidrains/deep-daze\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/deep-daze\"\u003edeep-daze\u003c/a\u003e\u003c/b\u003e ⭐ 4,332    \n   Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun  \n\n86. \u003ca href=\"https://github.com/zjunlp/deepke\"\u003ezjunlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zjunlp/deepke\"\u003eDeepKE\u003c/a\u003e\u003c/b\u003e ⭐ 4,304    \n   [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction  \n   🔗 [deepke.zjukg.cn](http://deepke.zjukg.cn/)  \n\n87. \u003ca href=\"https://github.com/microsoft/flaml\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/flaml\"\u003eFLAML\u003c/a\u003e\u003c/b\u003e ⭐ 4,283    \n   A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.  \n   🔗 [microsoft.github.io/flaml](https://microsoft.github.io/FLAML/)  \n\n88. \u003ca href=\"https://github.com/huggingface/speech-to-speech\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/speech-to-speech\"\u003espeech-to-speech\u003c/a\u003e\u003c/b\u003e ⭐ 4,274    \n   Speech To Speech: an effort for an open-sourced and modular GPT4-o  \n\n89. \u003ca href=\"https://github.com/cmusphinx/pocketsphinx\"\u003ecmusphinx/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cmusphinx/pocketsphinx\"\u003epocketsphinx\u003c/a\u003e\u003c/b\u003e ⭐ 4,261    \n   A small speech recognizer  \n\n90. \u003ca href=\"https://github.com/rucaibox/recbole\"\u003erucaibox/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rucaibox/recbole\"\u003eRecBole\u003c/a\u003e\u003c/b\u003e ⭐ 4,235    \n   A unified, comprehensive and efficient recommendation library  \n   🔗 [recbole.io](https://recbole.io/)  \n\n91. \u003ca href=\"https://github.com/ourownstory/neural_prophet\"\u003eourownstory/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ourownstory/neural_prophet\"\u003eneural_prophet\u003c/a\u003e\u003c/b\u003e ⭐ 4,233    \n   NeuralProphet: A simple forecasting package  \n   🔗 [neuralprophet.com](https://neuralprophet.com)  \n\n92. \u003ca href=\"https://github.com/facebookresearch/flow_matching\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/flow_matching\"\u003eflow_matching\u003c/a\u003e\u003c/b\u003e ⭐ 4,032    \n   Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures  \n   🔗 [facebookresearch.github.io/flow_matching](http://facebookresearch.github.io/flow_matching)  \n\n93. \u003ca href=\"https://github.com/cornellius-gp/gpytorch\"\u003ecornellius-gp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cornellius-gp/gpytorch\"\u003egpytorch\u003c/a\u003e\u003c/b\u003e ⭐ 3,819    \n   GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease.  \n\n94. \u003ca href=\"https://github.com/lightly-ai/lightly\"\u003elightly-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightly-ai/lightly\"\u003elightly\u003c/a\u003e\u003c/b\u003e ⭐ 3,666    \n   A python library for self-supervised learning on images.  \n   🔗 [docs.lightly.ai/self-supervised-learning](https://docs.lightly.ai/self-supervised-learning/)  \n\n95. \u003ca href=\"https://github.com/huggingface/safetensors\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/safetensors\"\u003esafetensors\u003c/a\u003e\u003c/b\u003e ⭐ 3,597    \n   Implements a new simple format for storing tensors safely (as opposed to pickle) and that is still fast (zero-copy).  \n   🔗 [huggingface.co/docs/safetensors](https://huggingface.co/docs/safetensors)  \n\n96. \u003ca href=\"https://github.com/yoheinakajima/instagraph\"\u003eyoheinakajima/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yoheinakajima/instagraph\"\u003einstagraph\u003c/a\u003e\u003c/b\u003e ⭐ 3,540    \n   Converts text input or URL into knowledge graph and displays  \n\n97. \u003ca href=\"https://github.com/petarv-/gat\"\u003epetarv-/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/petarv-/gat\"\u003eGAT\u003c/a\u003e\u003c/b\u003e ⭐ 3,492    \n   Implementation of a Graph Attention Network (GAT) layer in TensorFlow  \n   🔗 [petar-v.com/gat](https://petar-v.com/GAT/)  \n\n98. \u003ca href=\"https://github.com/neuraloperator/neuraloperator\"\u003eneuraloperator/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neuraloperator/neuraloperator\"\u003eneuraloperator\u003c/a\u003e\u003c/b\u003e ⭐ 3,337    \n   Comprehensive library for learning neural operators in PyTorch. It is the official implementation for Fourier Neural Operators and Tensorized Neural Operators.  \n   🔗 [neuraloperator.github.io/dev/index.html](https://neuraloperator.github.io/dev/index.html)  \n\n99. \u003ca href=\"https://github.com/pytorch/glow\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/glow\"\u003eglow\u003c/a\u003e\u003c/b\u003e ⭐ 3,328    \n   Compiler for Neural Network hardware accelerators  \n\n100. \u003ca href=\"https://github.com/hrnet/hrnet-semantic-segmentation\"\u003ehrnet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hrnet/hrnet-semantic-segmentation\"\u003eHRNet-Semantic-Segmentation\u003c/a\u003e\u003c/b\u003e ⭐ 3,311    \n   The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919  \n\n101. \u003ca href=\"https://github.com/facebookresearch/vissl\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/vissl\"\u003evissl\u003c/a\u003e\u003c/b\u003e ⭐ 3,294    \n   VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.  \n   🔗 [vissl.ai](https://vissl.ai)  \n\n102. \u003ca href=\"https://github.com/lucidrains/musiclm-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/musiclm-pytorch\"\u003emusiclm-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 3,293    \n   Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch  \n\n103. \u003ca href=\"https://github.com/shankarpandala/lazypredict\"\u003eshankarpandala/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shankarpandala/lazypredict\"\u003elazypredict\u003c/a\u003e\u003c/b\u003e ⭐ 3,285    \n   Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning  \n\n104. \u003ca href=\"https://github.com/huggingface/huggingface_hub\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/huggingface_hub\"\u003ehuggingface_hub\u003c/a\u003e\u003c/b\u003e ⭐ 3,277    \n   The official Python client for the Hugging Face Hub.  \n   🔗 [huggingface.co/docs/huggingface_hub](https://huggingface.co/docs/huggingface_hub)  \n\n105. \u003ca href=\"https://github.com/huggingface/optimum\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/optimum\"\u003eoptimum\u003c/a\u003e\u003c/b\u003e ⭐ 3,268    \n   🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools  \n   🔗 [huggingface.co/docs/optimum/main](https://huggingface.co/docs/optimum/main/)  \n\n106. \u003ca href=\"https://github.com/mljar/mljar-supervised\"\u003emljar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mljar/mljar-supervised\"\u003emljar-supervised\u003c/a\u003e\u003c/b\u003e ⭐ 3,234    \n   Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation  \n   🔗 [mljar.com](https://mljar.com)  \n\n107. \u003ca href=\"https://github.com/nvidia/cuda-python\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/cuda-python\"\u003ecuda-python\u003c/a\u003e\u003c/b\u003e ⭐ 3,145    \n   CUDA Python: Performance meets Productivity  \n   🔗 [nvidia.github.io/cuda-python](https://nvidia.github.io/cuda-python/)  \n\n108. \u003ca href=\"https://github.com/google-research/t5x\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/t5x\"\u003et5x\u003c/a\u003e\u003c/b\u003e ⭐ 2,938    \n   T5X is a modular, composable, research-friendly framework for high-performance, configurable, self-service training, evaluation, and inference of sequence models (starting with language) at many scales.  \n\n109. \u003ca href=\"https://github.com/eric-mitchell/direct-preference-optimization\"\u003eeric-mitchell/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eric-mitchell/direct-preference-optimization\"\u003edirect-preference-optimization\u003c/a\u003e\u003c/b\u003e ⭐ 2,834    \n   Reference implementation for DPO (Direct Preference Optimization)  \n\n110. \u003ca href=\"https://github.com/rom1504/clip-retrieval\"\u003erom1504/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rom1504/clip-retrieval\"\u003eclip-retrieval\u003c/a\u003e\u003c/b\u003e ⭐ 2,716    \n   Easily compute clip embeddings and build a clip retrieval system with them  \n   🔗 [rom1504.github.io/clip-retrieval](https://rom1504.github.io/clip-retrieval/)  \n\n111. \u003ca href=\"https://github.com/freedmand/semantra\"\u003efreedmand/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/freedmand/semantra\"\u003esemantra\u003c/a\u003e\u003c/b\u003e ⭐ 2,686    \n   Semantra is a multipurpose tool for semantically searching documents. Query by meaning rather than just by matching text.  \n\n112. \u003ca href=\"https://github.com/apple/ml-ane-transformers\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/ml-ane-transformers\"\u003eml-ane-transformers\u003c/a\u003e\u003c/b\u003e ⭐ 2,672    \n   Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)  \n\n113. \u003ca href=\"https://github.com/qdrant/fastembed\"\u003eqdrant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qdrant/fastembed\"\u003efastembed\u003c/a\u003e\u003c/b\u003e ⭐ 2,651    \n   Fast, Accurate, Lightweight Python library to make State of the Art Embedding  \n   🔗 [qdrant.github.io/fastembed](https://qdrant.github.io/fastembed/)  \n\n114. \u003ca href=\"https://github.com/huggingface/evaluate\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/evaluate\"\u003eevaluate\u003c/a\u003e\u003c/b\u003e ⭐ 2,404    \n   🤗 Evaluate: A library for easily evaluating machine learning models and datasets.  \n   🔗 [huggingface.co/docs/evaluate](https://huggingface.co/docs/evaluate)  \n\n115. \u003ca href=\"https://github.com/benedekrozemberczki/karateclub\"\u003ebenedekrozemberczki/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/benedekrozemberczki/karateclub\"\u003ekarateclub\u003c/a\u003e\u003c/b\u003e ⭐ 2,273    \n   Karate Club is an unsupervised machine learning extension library for NetworkX.  \n   🔗 [karateclub.readthedocs.io](https://karateclub.readthedocs.io)  \n\n116. \u003ca href=\"https://github.com/microsoft/olive\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/olive\"\u003eOlive\u003c/a\u003e\u003c/b\u003e ⭐ 2,240    \n   Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs.  \n   🔗 [microsoft.github.io/olive](https://microsoft.github.io/Olive/)  \n\n117. \u003ca href=\"https://github.com/castorini/pyserini\"\u003ecastorini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/castorini/pyserini\"\u003epyserini\u003c/a\u003e\u003c/b\u003e ⭐ 2,008    \n   Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.  \n   🔗 [pyserini.io](http://pyserini.io/)  \n\n118. \u003ca href=\"https://github.com/linkedin/greykite\"\u003elinkedin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/linkedin/greykite\"\u003egreykite\u003c/a\u003e\u003c/b\u003e ⭐ 1,854    \n   A flexible, intuitive and fast forecasting library  \n\n119. \u003ca href=\"https://github.com/rentruewang/koila\"\u003erentruewang/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rentruewang/koila\"\u003ekoila\u003c/a\u003e\u003c/b\u003e ⭐ 1,832    \n   Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code.  \n   🔗 [koila.rentruewang.com](https://koila.rentruewang.com)  \n\n120. \u003ca href=\"https://github.com/laekov/fastmoe\"\u003elaekov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/laekov/fastmoe\"\u003efastmoe\u003c/a\u003e\u003c/b\u003e ⭐ 1,829    \n   A fast MoE impl for PyTorch  \n   🔗 [fastmoe.ai](https://fastmoe.ai)  \n\n121. \u003ca href=\"https://github.com/visual-layer/fastdup\"\u003evisual-layer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/visual-layer/fastdup\"\u003efastdup\u003c/a\u003e\u003c/b\u003e ⭐ 1,817    \n   fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.  \n   🔗 [docs.visual-layer.com/fastdup_docs_old/first%20steps/getting-started](https://docs.visual-layer.com/fastdup_docs_old/First%20Steps/getting-started)  \n\n122. \u003ca href=\"https://github.com/microsoft/i-code\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/i-code\"\u003ei-Code\u003c/a\u003e\u003c/b\u003e ⭐ 1,707    \n   The ambition of the i-Code project is to build integrative and composable multimodal AI. The \"i\" stands for integrative multimodal learning.  \n\n123. \u003ca href=\"https://github.com/google/vizier\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/vizier\"\u003evizier\u003c/a\u003e\u003c/b\u003e ⭐ 1,623    \n   Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.  \n   🔗 [oss-vizier.readthedocs.io](https://oss-vizier.readthedocs.io)  \n\n124. \u003ca href=\"https://github.com/microsoft/semi-supervised-learning\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/semi-supervised-learning\"\u003eSemi-supervised-learning\u003c/a\u003e\u003c/b\u003e ⭐ 1,559    \n   A Unified Semi-Supervised Learning Codebase (NeurIPS'22)  \n   🔗 [usb.readthedocs.io](https://usb.readthedocs.io)  \n\n125. \u003ca href=\"https://github.com/spotify/voyager\"\u003espotify/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spotify/voyager\"\u003evoyager\u003c/a\u003e\u003c/b\u003e ⭐ 1,535    \n   🛰️ An approximate nearest-neighbor search library for Python and Java with a focus on ease of use, simplicity, and deployability.  \n   🔗 [spotify.github.io/voyager](https://spotify.github.io/voyager/)  \n\n126. \u003ca href=\"https://github.com/jina-ai/finetuner\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/finetuner\"\u003efinetuner\u003c/a\u003e\u003c/b\u003e ⭐ 1,508    \n   :dart: Task-oriented embedding tuning for BERT, CLIP, etc.  \n   🔗 [finetuner.jina.ai](https://finetuner.jina.ai)  \n\n127. \u003ca href=\"https://github.com/patchy631/machine-learning\"\u003epatchy631/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/patchy631/machine-learning\"\u003emachine-learning\u003c/a\u003e\u003c/b\u003e ⭐ 1,505    \n   Machine Learning Tutorials Repository  \n\n128. \u003ca href=\"https://github.com/lightning-ai/lightning-thunder\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/lightning-thunder\"\u003elightning-thunder\u003c/a\u003e\u003c/b\u003e ⭐ 1,437    \n   Thunder is a source-to-source compiler for PyTorch. It makes PyTorch programs faster by combining and using different hardware executors at once  \n\n129. \u003ca href=\"https://github.com/gradio-app/trackio\"\u003egradio-app/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gradio-app/trackio\"\u003etrackio\u003c/a\u003e\u003c/b\u003e ⭐ 1,230    \n   A lightweight, local-first, and 🆓 experiment tracking library from Hugging Face 🤗  \n\n130. \u003ca href=\"https://github.com/huggingface/quanto\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/quanto\"\u003eoptimum-quanto\u003c/a\u003e\u003c/b\u003e ⭐ 1,020    \n   A pytorch quantization backend for optimum  \n\n131. \u003ca href=\"https://github.com/criteo/autofaiss\"\u003ecriteo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/criteo/autofaiss\"\u003eautofaiss\u003c/a\u003e\u003c/b\u003e ⭐ 893    \n   Automatically create Faiss knn indices with the most optimal similarity search parameters.  \n   🔗 [criteo.github.io/autofaiss](https://criteo.github.io/autofaiss/)  \n\n132. \u003ca href=\"https://github.com/trent-b/iterative-stratification\"\u003etrent-b/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/trent-b/iterative-stratification\"\u003eiterative-stratification\u003c/a\u003e\u003c/b\u003e ⭐ 882    \n   Provides scikit-learn compatible cross validators with stratification for multilabel data.  \n\n133. \u003ca href=\"https://github.com/minishlab/semhash\"\u003eminishlab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minishlab/semhash\"\u003esemhash\u003c/a\u003e\u003c/b\u003e ⭐ 878    \n   SemHash is a lightweight and flexible tool for deduplicating datasets using semantic similarity. It combines fast embedding generation from Model2Vec with efficient ANN-based similarity search through Vicinity  \n   🔗 [minish.ai/packages/semhash](https://minish.ai/packages/semhash)  \n\n134. \u003ca href=\"https://github.com/nomic-ai/contrastors\"\u003enomic-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nomic-ai/contrastors\"\u003econtrastors\u003c/a\u003e\u003c/b\u003e ⭐ 773    \n   Contrastive learning toolkit that enables researchers and engineers to train and evaluate contrastive models efficiently.  \n\n135. \u003ca href=\"https://github.com/intel/intel-npu-acceleration-library\"\u003eintel/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/intel/intel-npu-acceleration-library\"\u003eintel-npu-acceleration-library\u003c/a\u003e\u003c/b\u003e ⭐ 701    \n   The Intel NPU Acceleration Library is a Python library designed to boost the efficiency of your applications by leveraging the power of the Intel Neural Processing Unit (NPU) to perform high-speed computations on compatible hardware.  \n\n136. \u003ca href=\"https://github.com/nicolas-hbt/pygraft\"\u003enicolas-hbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nicolas-hbt/pygraft\"\u003epygraft\u003c/a\u003e\u003c/b\u003e ⭐ 696    \n   Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips  \n   🔗 [pygraft.readthedocs.io/en/latest](https://pygraft.readthedocs.io/en/latest/)  \n\n137. \u003ca href=\"https://github.com/eleutherai/sae\"\u003eeleutherai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eleutherai/sae\"\u003esparsify\u003c/a\u003e\u003c/b\u003e ⭐ 685    \n   This library trains k-sparse autoencoders (SAEs) on the residual stream activations of HuggingFace language models, roughly following the recipe detailed in Scaling and evaluating sparse autoencoders (Gao et al. 2024)  \n\n138. \u003ca href=\"https://github.com/hkust-knowcomp/autoschemakg\"\u003ehkust-knowcomp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hkust-knowcomp/autoschemakg\"\u003eAutoSchemaKG\u003c/a\u003e\u003c/b\u003e ⭐ 669    \n   A Knowledge Graph Construction Framework with Schema Generation and Knowledge Graph Completion  \n   🔗 [hkust-knowcomp.github.io/autoschemakg](https://hkust-knowcomp.github.io/AutoSchemaKG/)  \n\n139. \u003ca href=\"https://github.com/google-deepmind/limit\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/limit\"\u003elimit\u003c/a\u003e\u003c/b\u003e ⭐ 619    \n   On the Theoretical Limitations of Embedding-Based Retrieval  \n   🔗 [arxiv.org/abs/2508.21038](https://arxiv.org/abs/2508.21038)  \n\n140. \u003ca href=\"https://github.com/apple/ml-l3m\"\u003eapple/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apple/ml-l3m\"\u003eml-l3m\u003c/a\u003e\u003c/b\u003e ⭐ 229    \n   A flexible library for training any type of large model, regardless of modality. Instead of more traditional approaches, we opt for a config-heavy approach  \n\n141. \u003ca href=\"https://github.com/dylanhogg/gptauthor\"\u003edylanhogg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dylanhogg/gptauthor\"\u003egptauthor\u003c/a\u003e\u003c/b\u003e ⭐ 97    \n   GPTAuthor is an AI tool for writing long form, multi-chapter stories given a story prompt.  \n\n142. \u003ca href=\"https://github.com/awslabs/stickler\"\u003eawslabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/awslabs/stickler\"\u003estickler\u003c/a\u003e\u003c/b\u003e ⭐ 23    \n   A library for evaluating structured data and AI outputs with weighted field comparison and custom comparators  \n   🔗 [awslabs.github.io/stickler](https://awslabs.github.io/stickler/)  \n\n143. \u003ca href=\"https://github.com/wjbmattingly/gliner-finetune\"\u003ewjbmattingly/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wjbmattingly/gliner-finetune\"\u003egliner-finetune\u003c/a\u003e\u003c/b\u003e ⭐ 14    \n   A library to generate synthetic data using LLM models, process this data, and then use it to train a GLiNER model. GLiNER is a Named Entity Recognition (NER) framework.  \n\n## Machine Learning - Deep Learning\n\nMachine learning libraries that cross over with deep learning in some way.  \n\n1. \u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003etensorflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tensorflow/tensorflow\"\u003etensorflow\u003c/a\u003e\u003c/b\u003e ⭐ 193,464    \n   An Open Source Machine Learning Framework for Everyone  \n   🔗 [tensorflow.org](https://tensorflow.org)  \n\n2. \u003ca href=\"https://github.com/pytorch/pytorch\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/pytorch\"\u003epytorch\u003c/a\u003e\u003c/b\u003e ⭐ 96,869    \n   Tensors and Dynamic neural networks in Python with strong GPU acceleration  \n   🔗 [pytorch.org](https://pytorch.org)  \n\n3. \u003ca href=\"https://github.com/openai/whisper\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/whisper\"\u003ewhisper\u003c/a\u003e\u003c/b\u003e ⭐ 93,624    \n   Robust Speech Recognition via Large-Scale Weak Supervision  \n\n4. \u003ca href=\"https://github.com/keras-team/keras\"\u003ekeras-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/keras-team/keras\"\u003ekeras\u003c/a\u003e\u003c/b\u003e ⭐ 63,738    \n   Deep Learning for humans  \n   🔗 [keras.io](http://keras.io/)  \n\n5. \u003ca href=\"https://github.com/deepfakes/faceswap\"\u003edeepfakes/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepfakes/faceswap\"\u003efaceswap\u003c/a\u003e\u003c/b\u003e ⭐ 54,915    \n   Deepfakes Software For All  \n   🔗 [www.faceswap.dev](https://www.faceswap.dev)  \n\n6. \u003ca href=\"https://github.com/facebookresearch/segment-anything\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/segment-anything\"\u003esegment-anything\u003c/a\u003e\u003c/b\u003e ⭐ 53,249    \n   The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.  \n\n7. \u003ca href=\"https://github.com/microsoft/deepspeed\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/deepspeed\"\u003eDeepSpeed\u003c/a\u003e\u003c/b\u003e ⭐ 41,382    \n   DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.  \n   🔗 [www.deepspeed.ai](https://www.deepspeed.ai/)  \n\n8. \u003ca href=\"https://github.com/rwightman/pytorch-image-models\"\u003erwightman/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rwightman/pytorch-image-models\"\u003epytorch-image-models\u003c/a\u003e\u003c/b\u003e ⭐ 36,256    \n   The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 \u0026 V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more  \n   🔗 [huggingface.co/docs/timm](https://huggingface.co/docs/timm)  \n\n9. \u003ca href=\"https://github.com/xinntao/real-esrgan\"\u003exinntao/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xinntao/real-esrgan\"\u003eReal-ESRGAN\u003c/a\u003e\u003c/b\u003e ⭐ 34,015    \n   Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.  \n\n10. \u003ca href=\"https://github.com/facebookresearch/detectron2\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/detectron2\"\u003edetectron2\u003c/a\u003e\u003c/b\u003e ⭐ 33,986    \n   Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.  \n   🔗 [detectron2.readthedocs.io/en/latest](https://detectron2.readthedocs.io/en/latest/)  \n\n11. \u003ca href=\"https://github.com/openai/clip\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/clip\"\u003eCLIP\u003c/a\u003e\u003c/b\u003e ⭐ 32,375    \n   CLIP (Contrastive Language-Image Pretraining),  Predict the most relevant text snippet given an image  \n\n12. \u003ca href=\"https://github.com/lightning-ai/pytorch-lightning\"\u003elightning-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightning-ai/pytorch-lightning\"\u003epytorch-lightning\u003c/a\u003e\u003c/b\u003e ⭐ 30,771    \n   The deep learning framework to pretrain, finetune and deploy AI models. PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.  \n   🔗 [lightning.ai/pytorch-lightning/?utm_source=ptl_readme\u0026utm_medium=referral\u0026utm_campaign=ptl_readme](https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme\u0026utm_medium=referral\u0026utm_campaign=ptl_readme)  \n\n13. \u003ca href=\"https://github.com/google-research/tuning_playbook\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/tuning_playbook\"\u003etuning_playbook\u003c/a\u003e\u003c/b\u003e ⭐ 29,721    \n   A playbook for systematically maximizing the performance of deep learning models.  \n\n14. \u003ca href=\"https://github.com/facebookresearch/detectron\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/detectron\"\u003eDetectron\u003c/a\u003e\u003c/b\u003e ⭐ 26,409    \n   FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.  \n\n15. \u003ca href=\"https://github.com/matterport/mask_rcnn\"\u003ematterport/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/matterport/mask_rcnn\"\u003eMask_RCNN\u003c/a\u003e\u003c/b\u003e ⭐ 25,497    \n   Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow  \n\n16. \u003ca href=\"https://github.com/lucidrains/vit-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/vit-pytorch\"\u003evit-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 24,921    \n   Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch  \n\n17. \u003ca href=\"https://github.com/paddlepaddle/paddle\"\u003epaddlepaddle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/paddlepaddle/paddle\"\u003ePaddle\u003c/a\u003e\u003c/b\u003e ⭐ 23,586    \n   PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习\u0026机器学习高性能单机、分布式训练和跨平台部署）  \n   🔗 [www.paddlepaddle.org](http://www.paddlepaddle.org/)  \n\n18. \u003ca href=\"https://github.com/pyg-team/pytorch_geometric\"\u003epyg-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyg-team/pytorch_geometric\"\u003epytorch_geometric\u003c/a\u003e\u003c/b\u003e ⭐ 23,414    \n   Graph Neural Network Library for PyTorch  \n   🔗 [pyg.org](https://pyg.org)  \n\n19. \u003ca href=\"https://github.com/sanster/lama-cleaner\"\u003esanster/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sanster/lama-cleaner\"\u003eIOPaint\u003c/a\u003e\u003c/b\u003e ⭐ 22,642    \n   Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.  \n   🔗 [www.iopaint.com](https://www.iopaint.com/)  \n\n20. \u003ca href=\"https://github.com/danielgatis/rembg\"\u003edanielgatis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/danielgatis/rembg\"\u003erembg\u003c/a\u003e\u003c/b\u003e ⭐ 21,624    \n   Rembg is a tool to remove images background  \n\n21. \u003ca href=\"https://github.com/apache/incubator-mxnet\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/incubator-mxnet\"\u003emxnet\u003c/a\u003e\u003c/b\u003e ⭐ 20,840    \n   Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more  \n   🔗 [mxnet.apache.org](https://mxnet.apache.org)  \n\n22. \u003ca href=\"https://github.com/rasbt/deeplearning-models\"\u003erasbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasbt/deeplearning-models\"\u003edeeplearning-models\u003c/a\u003e\u003c/b\u003e ⭐ 17,369    \n   A collection of various deep learning architectures, models, and tips  \n\n23. \u003ca href=\"https://github.com/microsoft/swin-transformer\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/swin-transformer\"\u003eSwin-Transformer\u003c/a\u003e\u003c/b\u003e ⭐ 15,667    \n   This is an official implementation for \"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows\".  \n   🔗 [arxiv.org/abs/2103.14030](https://arxiv.org/abs/2103.14030)  \n\n24. \u003ca href=\"https://github.com/albumentations-team/albumentations\"\u003ealbumentations-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/albumentations-team/albumentations\"\u003ealbumentations\u003c/a\u003e\u003c/b\u003e ⭐ 15,266    \n   Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125  \n   🔗 [albumentations.ai](https://albumentations.ai)  \n\n25. \u003ca href=\"https://github.com/facebookresearch/detr\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/detr\"\u003edetr\u003c/a\u003e\u003c/b\u003e ⭐ 15,071    \n   End-to-End Object Detection with Transformers  \n\n26. \u003ca href=\"https://github.com/nvidia/deeplearningexamples\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/deeplearningexamples\"\u003eDeepLearningExamples\u003c/a\u003e\u003c/b\u003e ⭐ 14,697    \n   State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.  \n\n27. \u003ca href=\"https://github.com/dmlc/dgl\"\u003edmlc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dmlc/dgl\"\u003edgl\u003c/a\u003e\u003c/b\u003e ⭐ 14,228    \n   Python package built to ease deep learning on graph, on top of existing DL frameworks.  \n   🔗 [dgl.ai](http://dgl.ai)  \n\n28. \u003ca href=\"https://github.com/mlfoundations/open_clip\"\u003emlfoundations/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlfoundations/open_clip\"\u003eopen_clip\u003c/a\u003e\u003c/b\u003e ⭐ 13,289    \n   Open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training).  \n\n29. \u003ca href=\"https://github.com/tencent-hunyuan/hunyuanvideo\"\u003etencent-hunyuan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tencent-hunyuan/hunyuanvideo\"\u003eHunyuanVideo\u003c/a\u003e\u003c/b\u003e ⭐ 11,635    \n   HunyuanVideo: A Systematic Framework For Large Video Generation Model  \n   🔗 [aivideo.hunyuan.tencent.com](https://aivideo.hunyuan.tencent.com)  \n\n30. \u003ca href=\"https://github.com/kornia/kornia\"\u003ekornia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kornia/kornia\"\u003ekornia\u003c/a\u003e\u003c/b\u003e ⭐ 11,031    \n   🐍 Geometric Computer Vision Library for Spatial AI  \n   🔗 [kornia.readthedocs.io](https://kornia.readthedocs.io)  \n\n31. \u003ca href=\"https://github.com/facebookresearch/pytorch3d\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/pytorch3d\"\u003epytorch3d\u003c/a\u003e\u003c/b\u003e ⭐ 9,761    \n   PyTorch3D is FAIR's library of reusable components for deep learning with 3D data  \n   🔗 [pytorch3d.org](https://pytorch3d.org/)  \n\n32. \u003ca href=\"https://github.com/modelscope/facechain\"\u003emodelscope/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modelscope/facechain\"\u003efacechain\u003c/a\u003e\u003c/b\u003e ⭐ 9,497    \n   FaceChain is a deep-learning toolchain for generating your Digital-Twin.  \n\n33. \u003ca href=\"https://github.com/arogozhnikov/einops\"\u003earogozhnikov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arogozhnikov/einops\"\u003eeinops\u003c/a\u003e\u003c/b\u003e ⭐ 9,360    \n   Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)  \n   🔗 [einops.rocks](https://einops.rocks)  \n\n34. \u003ca href=\"https://github.com/keras-team/autokeras\"\u003ekeras-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/keras-team/autokeras\"\u003eautokeras\u003c/a\u003e\u003c/b\u003e ⭐ 9,290    \n   AutoML library for deep learning  \n   🔗 [autokeras.com](http://autokeras.com/)  \n\n35. \u003ca href=\"https://github.com/bytedance/monolith\"\u003ebytedance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedance/monolith\"\u003emonolith\u003c/a\u003e\u003c/b\u003e ⭐ 9,259    \n   A deep learning framework for large scale recommendation modeling with collisionless embedding and real time training captures.  \n\n36. \u003ca href=\"https://github.com/pyro-ppl/pyro\"\u003epyro-ppl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyro-ppl/pyro\"\u003epyro\u003c/a\u003e\u003c/b\u003e ⭐ 8,959    \n   Deep universal probabilistic programming with Python and PyTorch  \n   🔗 [pyro.ai](http://pyro.ai)  \n\n37. \u003ca href=\"https://github.com/facebookresearch/imagebind\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/imagebind\"\u003eImageBind\u003c/a\u003e\u003c/b\u003e ⭐ 8,955    \n   ImageBind One Embedding Space to Bind Them All  \n\n38. \u003ca href=\"https://github.com/nvidia/apex\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/apex\"\u003eapex\u003c/a\u003e\u003c/b\u003e ⭐ 8,899    \n   A PyTorch Extension:  Tools for easy mixed precision and distributed training in Pytorch  \n\n39. \u003ca href=\"https://github.com/lucidrains/imagen-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/imagen-pytorch\"\u003eimagen-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 8,409    \n   Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch  \n\n40. \u003ca href=\"https://github.com/google/trax\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/trax\"\u003etrax\u003c/a\u003e\u003c/b\u003e ⭐ 8,302    \n   Trax — Deep Learning with Clear Code and Speed  \n\n41. \u003ca href=\"https://github.com/xpixelgroup/basicsr\"\u003expixelgroup/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xpixelgroup/basicsr\"\u003eBasicSR\u003c/a\u003e\u003c/b\u003e ⭐ 8,062    \n   Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.  \n   🔗 [basicsr.readthedocs.io/en/latest](https://basicsr.readthedocs.io/en/latest/)  \n\n42. \u003ca href=\"https://github.com/google/flax\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/flax\"\u003eflax\u003c/a\u003e\u003c/b\u003e ⭐ 7,046    \n   Flax is a neural network library for JAX that is designed for flexibility.  \n   🔗 [flax.readthedocs.io](https://flax.readthedocs.io)  \n\n43. \u003ca href=\"https://github.com/skorch-dev/skorch\"\u003eskorch-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/skorch-dev/skorch\"\u003eskorch\u003c/a\u003e\u003c/b\u003e ⭐ 6,149    \n   A scikit-learn compatible neural network library that wraps PyTorch  \n\n44. \u003ca href=\"https://github.com/facebookresearch/mmf\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/mmf\"\u003emmf\u003c/a\u003e\u003c/b\u003e ⭐ 5,616    \n   A modular framework for vision \u0026 language multimodal research from Facebook AI Research (FAIR)  \n   🔗 [mmf.sh](https://mmf.sh/)  \n\n45. \u003ca href=\"https://github.com/mosaicml/composer\"\u003emosaicml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mosaicml/composer\"\u003ecomposer\u003c/a\u003e\u003c/b\u003e ⭐ 5,458    \n   Supercharge Your Model Training  \n   🔗 [docs.mosaicml.com](http://docs.mosaicml.com)  \n\n46. \u003ca href=\"https://github.com/nvidiagameworks/kaolin\"\u003envidiagameworks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidiagameworks/kaolin\"\u003ekaolin\u003c/a\u003e\u003c/b\u003e ⭐ 5,021    \n   A PyTorch Library for Accelerating 3D Deep Learning Research  \n\n47. \u003ca href=\"https://github.com/deci-ai/super-gradients\"\u003edeci-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deci-ai/super-gradients\"\u003esuper-gradients\u003c/a\u003e\u003c/b\u003e ⭐ 4,997    \n   Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.  \n   🔗 [www.supergradients.com](https://www.supergradients.com)  \n\n48. \u003ca href=\"https://github.com/pytorch/ignite\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/ignite\"\u003eignite\u003c/a\u003e\u003c/b\u003e ⭐ 4,727    \n   High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.  \n   🔗 [pytorch-ignite.ai](https://pytorch-ignite.ai)  \n\n49. \u003ca href=\"https://github.com/facebookincubator/aitemplate\"\u003efacebookincubator/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookincubator/aitemplate\"\u003eAITemplate\u003c/a\u003e\u003c/b\u003e ⭐ 4,701    \n   AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.  \n\n50. \u003ca href=\"https://github.com/cvg/lightglue\"\u003ecvg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cvg/lightglue\"\u003eLightGlue\u003c/a\u003e\u003c/b\u003e ⭐ 4,322    \n   LightGlue: Local Feature Matching at Light Speed (ICCV 2023)  \n\n51. \u003ca href=\"https://github.com/modelscope/clearervoice-studio\"\u003emodelscope/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modelscope/clearervoice-studio\"\u003eClearerVoice-Studio\u003c/a\u003e\u003c/b\u003e ⭐ 3,856    \n   An AI-Powered Speech Processing Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Enhancement, Separation, and Target Speaker Extraction, etc.  \n\n52. \u003ca href=\"https://github.com/google-research/scenic\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/scenic\"\u003escenic\u003c/a\u003e\u003c/b\u003e ⭐ 3,757    \n   Scenic: A Jax Library for Computer Vision Research and Beyond  \n\n53. \u003ca href=\"https://github.com/williamyang1991/vtoonify\"\u003ewilliamyang1991/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/williamyang1991/vtoonify\"\u003eVToonify\u003c/a\u003e\u003c/b\u003e ⭐ 3,598    \n   [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer  \n\n54. \u003ca href=\"https://github.com/pytorch/botorch\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/botorch\"\u003ebotorch\u003c/a\u003e\u003c/b\u003e ⭐ 3,452    \n   Bayesian optimization in PyTorch  \n   🔗 [botorch.org](https://botorch.org/)  \n\n55. \u003ca href=\"https://github.com/facebookresearch/pytorch-biggraph\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/pytorch-biggraph\"\u003ePyTorch-BigGraph\u003c/a\u003e\u003c/b\u003e ⭐ 3,419    \n   Generate embeddings from large-scale graph-structured data.  \n   🔗 [torchbiggraph.readthedocs.io](https://torchbiggraph.readthedocs.io/)  \n\n56. \u003ca href=\"https://github.com/alpa-projects/alpa\"\u003ealpa-projects/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alpa-projects/alpa\"\u003ealpa\u003c/a\u003e\u003c/b\u003e ⭐ 3,175    \n   Training and serving large-scale neural networks with auto parallelization.  \n   🔗 [alpa.ai](https://alpa.ai)  \n\n57. \u003ca href=\"https://github.com/deepmind/dm-haiku\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/dm-haiku\"\u003edm-haiku\u003c/a\u003e\u003c/b\u003e ⭐ 3,172    \n   JAX-based neural network library  \n   🔗 [dm-haiku.readthedocs.io](https://dm-haiku.readthedocs.io)  \n\n58. \u003ca href=\"https://github.com/nerdyrodent/vqgan-clip\"\u003enerdyrodent/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nerdyrodent/vqgan-clip\"\u003eVQGAN-CLIP\u003c/a\u003e\u003c/b\u003e ⭐ 2,661    \n   Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.  \n\n59. \u003ca href=\"https://github.com/pytorch/torchrec\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/torchrec\"\u003etorchrec\u003c/a\u003e\u003c/b\u003e ⭐ 2,462    \n   Pytorch domain library for recommendation systems  \n   🔗 [pytorch.org/torchrec](https://pytorch.org/torchrec/)  \n\n60. \u003ca href=\"https://github.com/danielegrattarola/spektral\"\u003edanielegrattarola/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/danielegrattarola/spektral\"\u003espektral\u003c/a\u003e\u003c/b\u003e ⭐ 2,393    \n   Graph Neural Networks with Keras and Tensorflow 2.  \n   🔗 [graphneural.network](https://graphneural.network)  \n\n61. \u003ca href=\"https://github.com/google-research/electra\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/electra\"\u003eelectra\u003c/a\u003e\u003c/b\u003e ⭐ 2,369    \n   ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators  \n\n62. \u003ca href=\"https://github.com/fepegar/torchio\"\u003efepegar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fepegar/torchio\"\u003etorchio\u003c/a\u003e\u003c/b\u003e ⭐ 2,346    \n   Medical imaging processing for AI applications.  \n   🔗 [docs.torchio.org](https://docs.torchio.org/)  \n\n63. \u003ca href=\"https://github.com/neuralmagic/sparseml\"\u003eneuralmagic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neuralmagic/sparseml\"\u003esparseml\u003c/a\u003e\u003c/b\u003e ⭐ 2,143    \n   Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models  \n\n64. \u003ca href=\"https://github.com/jeshraghian/snntorch\"\u003ejeshraghian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jeshraghian/snntorch\"\u003esnntorch\u003c/a\u003e\u003c/b\u003e ⭐ 1,855    \n   Deep and online learning with spiking neural networks in Python  \n   🔗 [snntorch.readthedocs.io/en/latest](https://snntorch.readthedocs.io/en/latest/)  \n\n65. \u003ca href=\"https://github.com/sakanaai/continuous-thought-machines\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/continuous-thought-machines\"\u003econtinuous-thought-machines\u003c/a\u003e\u003c/b\u003e ⭐ 1,728    \n   Continuous Thought Machine (CTM), a model designed to unfold and then leverage neural activity as the underlying mechanism for observation and action  \n   🔗 [pub.sakana.ai/ctm](https://pub.sakana.ai/ctm/)  \n\n66. \u003ca href=\"https://github.com/xl0/lovely-tensors\"\u003exl0/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xl0/lovely-tensors\"\u003elovely-tensors\u003c/a\u003e\u003c/b\u003e ⭐ 1,353    \n   Tensors, for human consumption  \n   🔗 [xl0.github.io/lovely-tensors](https://xl0.github.io/lovely-tensors)  \n\n67. \u003ca href=\"https://github.com/allenai/reward-bench\"\u003eallenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/allenai/reward-bench\"\u003ereward-bench\u003c/a\u003e\u003c/b\u003e ⭐ 683    \n   RewardBench is a benchmark designed to evaluate the capabilities and safety of reward models (including those trained with Direct Preference Optimization, DPO)  \n   🔗 [huggingface.co/spaces/allenai/reward-bench](https://huggingface.co/spaces/allenai/reward-bench)  \n\n## Machine Learning - Interpretability\n\nMachine learning interpretability libraries. Covers explainability, prediction explainations, dashboards, understanding knowledge development in training.  \n\n1. \u003ca href=\"https://github.com/slundberg/shap\"\u003eslundberg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/slundberg/shap\"\u003eshap\u003c/a\u003e\u003c/b\u003e ⭐ 24,945    \n   A game theoretic approach to explain the output of any machine learning model.  \n   🔗 [shap.readthedocs.io](https://shap.readthedocs.io)  \n\n2. \u003ca href=\"https://github.com/marcotcr/lime\"\u003emarcotcr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marcotcr/lime\"\u003elime\u003c/a\u003e\u003c/b\u003e ⭐ 12,090    \n   Lime: Explaining the predictions of any machine learning classifier  \n\n3. \u003ca href=\"https://github.com/arize-ai/phoenix\"\u003earize-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arize-ai/phoenix\"\u003ephoenix\u003c/a\u003e\u003c/b\u003e ⭐ 8,344    \n   AI Observability \u0026 Evaluation  \n   🔗 [arize.com/docs/phoenix](https://arize.com/docs/phoenix)  \n\n4. \u003ca href=\"https://github.com/interpretml/interpret\"\u003einterpretml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/interpretml/interpret\"\u003einterpret\u003c/a\u003e\u003c/b\u003e ⭐ 6,768    \n   Fit interpretable models. Explain blackbox machine learning.   \n   🔗 [interpret.ml/docs](https://interpret.ml/docs)  \n\n5. \u003ca href=\"https://github.com/pytorch/captum\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/captum\"\u003ecaptum\u003c/a\u003e\u003c/b\u003e ⭐ 5,537    \n   Model interpretability and understanding for PyTorch  \n   🔗 [captum.ai](https://captum.ai)  \n\n6. \u003ca href=\"https://github.com/tensorflow/lucid\"\u003etensorflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tensorflow/lucid\"\u003elucid\u003c/a\u003e\u003c/b\u003e ⭐ 4,705    \n   A collection of infrastructure and tools for research in neural network interpretability.  \n\n7. \u003ca href=\"https://github.com/pair-code/lit\"\u003epair-code/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pair-code/lit\"\u003elit\u003c/a\u003e\u003c/b\u003e ⭐ 3,628    \n   The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.  \n   🔗 [pair-code.github.io/lit](https://pair-code.github.io/lit)  \n\n8. \u003ca href=\"https://github.com/maif/shapash\"\u003emaif/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/maif/shapash\"\u003eshapash\u003c/a\u003e\u003c/b\u003e ⭐ 3,119    \n   🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models  \n   🔗 [maif.github.io/shapash](https://maif.github.io/shapash/)  \n\n9. \u003ca href=\"https://github.com/transformerlensorg/transformerlens\"\u003etransformerlensorg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/transformerlensorg/transformerlens\"\u003eTransformerLens\u003c/a\u003e\u003c/b\u003e ⭐ 3,012    \n   A library for mechanistic interpretability of GPT-style language models  \n   🔗 [transformerlensorg.github.io/transformerlens](https://transformerlensorg.github.io/TransformerLens/)  \n\n10. \u003ca href=\"https://github.com/eleutherai/pythia\"\u003eeleutherai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eleutherai/pythia\"\u003epythia\u003c/a\u003e\u003c/b\u003e ⭐ 2,715    \n   Interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers  \n\n11. \u003ca href=\"https://github.com/seldonio/alibi\"\u003eseldonio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/seldonio/alibi\"\u003ealibi\u003c/a\u003e\u003c/b\u003e ⭐ 2,607    \n   Algorithms for explaining machine learning models  \n   🔗 [docs.seldon.io/projects/alibi/en/stable](https://docs.seldon.io/projects/alibi/en/stable/)  \n\n12. \u003ca href=\"https://github.com/oegedijk/explainerdashboard\"\u003eoegedijk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/oegedijk/explainerdashboard\"\u003eexplainerdashboard\u003c/a\u003e\u003c/b\u003e ⭐ 2,469    \n   Quickly build Explainable AI dashboards that show the inner workings of so-called \"blackbox\" machine learning models.  \n   🔗 [explainerdashboard.readthedocs.io](http://explainerdashboard.readthedocs.io)  \n\n13. \u003ca href=\"https://github.com/jalammar/ecco\"\u003ejalammar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jalammar/ecco\"\u003eecco\u003c/a\u003e\u003c/b\u003e ⭐ 2,073    \n   Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).  \n   🔗 [ecco.readthedocs.io](https://ecco.readthedocs.io)  \n\n14. \u003ca href=\"https://github.com/google-deepmind/penzai\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/penzai\"\u003epenzai\u003c/a\u003e\u003c/b\u003e ⭐ 1,851    \n   A JAX library for writing models as legible, functional pytree data structures, along with tools for visualizing, modifying, and analyzing them. Penzai focuses on making it easy to do stuff with models after they have been trained  \n   🔗 [penzai.readthedocs.io](https://penzai.readthedocs.io/)  \n\n15. \u003ca href=\"https://github.com/stanfordnlp/pyreft\"\u003estanfordnlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stanfordnlp/pyreft\"\u003epyreft\u003c/a\u003e\u003c/b\u003e ⭐ 1,554    \n   Stanford NLP Python library for Representation Finetuning (ReFT)  \n   🔗 [arxiv.org/abs/2404.03592](https://arxiv.org/abs/2404.03592)  \n\n16. \u003ca href=\"https://github.com/selfexplainml/piml-toolbox\"\u003eselfexplainml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/selfexplainml/piml-toolbox\"\u003ePiML-Toolbox\u003c/a\u003e\u003c/b\u003e ⭐ 1,285    \n   PiML (Python Interpretable Machine Learning) toolbox for model development \u0026 diagnostics  \n   🔗 [selfexplainml.github.io/piml-toolbox](https://selfexplainml.github.io/PiML-Toolbox)  \n\n17. \u003ca href=\"https://github.com/ethicalml/xai\"\u003eethicalml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ethicalml/xai\"\u003exai\u003c/a\u003e\u003c/b\u003e ⭐ 1,221    \n   XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models  \n   🔗 [ethical.institute/principles.html#commitment-3](https://ethical.institute/principles.html#commitment-3)  \n\n18. \u003ca href=\"https://github.com/jbloomaus/saelens\"\u003ejbloomaus/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jbloomaus/saelens\"\u003eSAELens\u003c/a\u003e\u003c/b\u003e ⭐ 1,174    \n   Training Sparse Autoencoders on LLms. Analyse sparse autoencoders and neural network internals.  \n   🔗 [decoderesearch.github.io/saelens](https://decoderesearch.github.io/SAELens/)  \n\n19. \u003ca href=\"https://github.com/andyzoujm/representation-engineering\"\u003eandyzoujm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/andyzoujm/representation-engineering\"\u003erepresentation-engineering\u003c/a\u003e\u003c/b\u003e ⭐ 942    \n   Representation Engineering: A Top-Down Approach to AI Transparency  \n   🔗 [www.ai-transparency.org](https://www.ai-transparency.org/)  \n\n20. \u003ca href=\"https://github.com/ndif-team/nnsight\"\u003endif-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ndif-team/nnsight\"\u003ennsight\u003c/a\u003e\u003c/b\u003e ⭐ 782    \n   The nnsight package enables interpreting and manipulating the internals of deep learned models.  \n   🔗 [nnsight.net](https://nnsight.net/)  \n\n21. \u003ca href=\"https://github.com/labmlai/inspectus\"\u003elabmlai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/labmlai/inspectus\"\u003einspectus\u003c/a\u003e\u003c/b\u003e ⭐ 705    \n   Inspectus provides visualization tools for attention mechanisms in deep learning models. It provides a set of comprehensive views, making it easier to understand how these models work.  \n\n## Machine Learning - Ops\n\nMLOps tools, frameworks and libraries: intersection of machine learning, data engineering and DevOps; deployment, health, diagnostics and governance of ML models.  \n\n1. \u003ca href=\"https://github.com/apache/airflow\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/airflow\"\u003eairflow\u003c/a\u003e\u003c/b\u003e ⭐ 43,992    \n   Apache Airflow - A platform to programmatically author, schedule, and monitor workflows  \n   🔗 [airflow.apache.org](https://airflow.apache.org/)  \n\n2. \u003ca href=\"https://github.com/ray-project/ray\"\u003eray-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ray-project/ray\"\u003eray\u003c/a\u003e\u003c/b\u003e ⭐ 40,957    \n   Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.  \n   🔗 [ray.io](https://ray.io)  \n\n3. \u003ca href=\"https://github.com/kestra-io/kestra\"\u003ekestra-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kestra-io/kestra\"\u003ekestra\u003c/a\u003e\u003c/b\u003e ⭐ 26,269    \n   Event Driven Orchestration \u0026 Scheduling Platform for Mission Critical Applications  \n   🔗 [kestra.io](https://kestra.io)  \n\n4. \u003ca href=\"https://github.com/mlflow/mlflow\"\u003emlflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlflow/mlflow\"\u003emlflow\u003c/a\u003e\u003c/b\u003e ⭐ 23,795    \n   The open source developer platform to build AI agents and models with confidence. Enhance your AI applications with end-to-end tracking, observability, and evaluations, all in one integrated platform.  \n   🔗 [mlflow.org](https://mlflow.org)  \n\n5. \u003ca href=\"https://github.com/jlowin/fastmcp\"\u003ejlowin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jlowin/fastmcp\"\u003efastmcp\u003c/a\u003e\u003c/b\u003e ⭐ 22,279    \n   FastMCP is the standard framework for building MCP servers and clients. FastMCP 1.0 was incorporated into the official MCP Python SDK.  \n   🔗 [gofastmcp.com](https://gofastmcp.com)  \n\n6. \u003ca href=\"https://github.com/prefecthq/prefect\"\u003eprefecthq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prefecthq/prefect\"\u003eprefect\u003c/a\u003e\u003c/b\u003e ⭐ 21,412    \n   Prefect is a workflow orchestration framework for building resilient data pipelines in Python.  \n   🔗 [prefect.io](https://prefect.io)  \n\n7. \u003ca href=\"https://github.com/langfuse/langfuse\"\u003elangfuse/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/langfuse/langfuse\"\u003elangfuse\u003c/a\u003e\u003c/b\u003e ⭐ 21,017    \n   🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23   \n   🔗 [langfuse.com/docs](https://langfuse.com/docs)  \n\n8. \u003ca href=\"https://github.com/spotify/luigi\"\u003espotify/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spotify/luigi\"\u003eluigi\u003c/a\u003e\u003c/b\u003e ⭐ 18,628    \n   Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.   \n\n9. \u003ca href=\"https://github.com/iterative/dvc\"\u003eiterative/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/iterative/dvc\"\u003edvc\u003c/a\u003e\u003c/b\u003e ⭐ 15,302    \n   🦉 Data Versioning and ML Experiments  \n   🔗 [dvc.org](https://dvc.org)  \n\n10. \u003ca href=\"https://github.com/dagster-io/dagster\"\u003edagster-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dagster-io/dagster\"\u003edagster\u003c/a\u003e\u003c/b\u003e ⭐ 14,798    \n   An orchestration platform for the development, production, and observation of data assets.  \n   🔗 [dagster.io](https://dagster.io)  \n\n11. \u003ca href=\"https://github.com/horovod/horovod\"\u003ehorovod/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/horovod/horovod\"\u003ehorovod\u003c/a\u003e\u003c/b\u003e ⭐ 14,655    \n   Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.  \n   🔗 [horovod.ai](http://horovod.ai)  \n\n12. \u003ca href=\"https://github.com/dbt-labs/dbt-core\"\u003edbt-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dbt-labs/dbt-core\"\u003edbt-core\u003c/a\u003e\u003c/b\u003e ⭐ 12,132    \n   dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.  \n   🔗 [getdbt.com](https://getdbt.com)  \n\n13. \u003ca href=\"https://github.com/bentoml/openllm\"\u003ebentoml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bentoml/openllm\"\u003eOpenLLM\u003c/a\u003e\u003c/b\u003e ⭐ 12,063    \n   Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.  \n   🔗 [bentoml.com](https://bentoml.com)  \n\n14. \u003ca href=\"https://github.com/ludwig-ai/ludwig\"\u003eludwig-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ludwig-ai/ludwig\"\u003eludwig\u003c/a\u003e\u003c/b\u003e ⭐ 11,644    \n   Low-code framework for building custom LLMs, neural networks, and other AI models  \n   🔗 [ludwig.ai](http://ludwig.ai)  \n\n15. \u003ca href=\"https://github.com/great-expectations/great_expectations\"\u003egreat-expectations/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/great-expectations/great_expectations\"\u003egreat_expectations\u003c/a\u003e\u003c/b\u003e ⭐ 11,095    \n   Always know what to expect from your data.  \n   🔗 [docs.greatexpectations.io](https://docs.greatexpectations.io/)  \n\n16. \u003ca href=\"https://github.com/huggingface/text-generation-inference\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/text-generation-inference\"\u003etext-generation-inference\u003c/a\u003e\u003c/b\u003e ⭐ 10,739    \n   A Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power Hugging Chat, the Inference API and Inference Endpoint.  \n   🔗 [hf.co/docs/text-generation-inference](http://hf.co/docs/text-generation-inference)  \n\n17. \u003ca href=\"https://github.com/kedro-org/kedro\"\u003ekedro-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kedro-org/kedro\"\u003ekedro\u003c/a\u003e\u003c/b\u003e ⭐ 10,718    \n   Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.  \n   🔗 [kedro.org](https://kedro.org)  \n\n18. \u003ca href=\"https://github.com/netflix/metaflow\"\u003enetflix/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/netflix/metaflow\"\u003emetaflow\u003c/a\u003e\u003c/b\u003e ⭐ 9,728    \n   Build, Manage and Deploy AI/ML Systems  \n   🔗 [metaflow.org](https://metaflow.org)  \n\n19. \u003ca href=\"https://github.com/activeloopai/deeplake\"\u003eactiveloopai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/activeloopai/deeplake\"\u003edeeplake\u003c/a\u003e\u003c/b\u003e ⭐ 8,982    \n   Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, \u0026 visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai  \n   🔗 [activeloop.ai](https://activeloop.ai)  \n\n20. \u003ca href=\"https://github.com/mage-ai/mage-ai\"\u003emage-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mage-ai/mage-ai\"\u003emage-ai\u003c/a\u003e\u003c/b\u003e ⭐ 8,622    \n   🧙 Build, run, and manage data pipelines for integrating and transforming data.  \n   🔗 [www.mage.ai](https://www.mage.ai)  \n\n21. \u003ca href=\"https://github.com/bentoml/bentoml\"\u003ebentoml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bentoml/bentoml\"\u003eBentoML\u003c/a\u003e\u003c/b\u003e ⭐ 8,384    \n   The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!  \n   🔗 [bentoml.com](https://bentoml.com)  \n\n22. \u003ca href=\"https://github.com/internlm/lmdeploy\"\u003einternlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/internlm/lmdeploy\"\u003elmdeploy\u003c/a\u003e\u003c/b\u003e ⭐ 7,553    \n   LMDeploy is a toolkit for compressing, deploying, and serving LLMs.  \n   🔗 [lmdeploy.readthedocs.io/en/latest](https://lmdeploy.readthedocs.io/en/latest)  \n\n23. \u003ca href=\"https://github.com/evidentlyai/evidently\"\u003eevidentlyai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/evidentlyai/evidently\"\u003eevidently\u003c/a\u003e\u003c/b\u003e ⭐ 7,041    \n   Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.  \n   🔗 [discord.gg/xzjkranp8b](https://discord.gg/xZjKRaNp8b)  \n\n24. \u003ca href=\"https://github.com/flyteorg/flyte\"\u003eflyteorg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/flyteorg/flyte\"\u003eflyte\u003c/a\u003e\u003c/b\u003e ⭐ 6,694    \n   Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.  \n   🔗 [flyte.org](https://flyte.org)  \n\n25. \u003ca href=\"https://github.com/feast-dev/feast\"\u003efeast-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/feast-dev/feast\"\u003efeast\u003c/a\u003e\u003c/b\u003e ⭐ 6,645    \n   The Open Source Feature Store for AI/ML  \n   🔗 [feast.dev](https://feast.dev)  \n\n26. \u003ca href=\"https://github.com/adap/flower\"\u003eadap/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/adap/flower\"\u003eflower\u003c/a\u003e\u003c/b\u003e ⭐ 6,598    \n   Flower: A Friendly Federated AI Framework  \n   🔗 [flower.ai](https://flower.ai)  \n\n27. \u003ca href=\"https://github.com/allegroai/clearml\"\u003eallegroai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/allegroai/clearml\"\u003eclearml\u003c/a\u003e\u003c/b\u003e ⭐ 6,468    \n   ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling \u0026 Serving in one MLOps/LLMOps solution  \n   🔗 [clear.ml/docs](https://clear.ml/docs)  \n\n28. \u003ca href=\"https://github.com/aimhubio/aim\"\u003eaimhubio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aimhubio/aim\"\u003eaim\u003c/a\u003e\u003c/b\u003e ⭐ 5,966    \n   Aim 💫 — An easy-to-use \u0026 supercharged open-source experiment tracker.  \n   🔗 [aimstack.io](https://aimstack.io)  \n\n29. \u003ca href=\"https://github.com/zenml-io/zenml\"\u003ezenml-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zenml-io/zenml\"\u003ezenml\u003c/a\u003e\u003c/b\u003e ⭐ 5,166    \n   ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.  \n   🔗 [zenml.io](https://zenml.io)  \n\n30. \u003ca href=\"https://github.com/internlm/xtuner\"\u003einternlm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/internlm/xtuner\"\u003extuner\u003c/a\u003e\u003c/b\u003e ⭐ 5,061    \n   A Next-Generation Training Engine Built for Ultra-Large MoE Models  \n   🔗 [xtuner.readthedocs.io/zh-cn/latest](https://xtuner.readthedocs.io/zh-cn/latest/)  \n\n31. \u003ca href=\"https://github.com/orchest/orchest\"\u003eorchest/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/orchest/orchest\"\u003eorchest\u003c/a\u003e\u003c/b\u003e ⭐ 4,144    \n   Build data pipelines, the easy way 🛠️  \n   🔗 [orchest.readthedocs.io/en/stable](https://orchest.readthedocs.io/en/stable/)  \n\n32. \u003ca href=\"https://github.com/kubeflow/pipelines\"\u003ekubeflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kubeflow/pipelines\"\u003epipelines\u003c/a\u003e\u003c/b\u003e ⭐ 4,062    \n   Machine Learning Pipelines for Kubeflow  \n   🔗 [www.kubeflow.org/docs/components/pipelines](https://www.kubeflow.org/docs/components/pipelines/)  \n\n33. \u003ca href=\"https://github.com/polyaxon/polyaxon\"\u003epolyaxon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/polyaxon/polyaxon\"\u003epolyaxon\u003c/a\u003e\u003c/b\u003e ⭐ 3,692    \n   MLOps Tools For Managing \u0026 Orchestrating The Machine Learning LifeCycle  \n   🔗 [polyaxon.com](https://polyaxon.com)  \n\n34. \u003ca href=\"https://github.com/ploomber/ploomber\"\u003eploomber/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ploomber/ploomber\"\u003eploomber\u003c/a\u003e\u003c/b\u003e ⭐ 3,622    \n   The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️  \n   🔗 [docs.ploomber.io](https://docs.ploomber.io)  \n\n35. \u003ca href=\"https://github.com/towhee-io/towhee\"\u003etowhee-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/towhee-io/towhee\"\u003etowhee\u003c/a\u003e\u003c/b\u003e ⭐ 3,449    \n   Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.  \n   🔗 [towhee.io](https://towhee.io)  \n\n36. \u003ca href=\"https://github.com/azure/pyrit\"\u003eazure/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/azure/pyrit\"\u003ePyRIT\u003c/a\u003e\u003c/b\u003e ⭐ 3,342    \n   The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and ML engineers to red team foundation models and their applications.  \n   🔗 [azure.github.io/pyrit](https://azure.github.io/PyRIT/)  \n\n37. \u003ca href=\"https://github.com/determined-ai/determined\"\u003edetermined-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/determined-ai/determined\"\u003edetermined\u003c/a\u003e\u003c/b\u003e ⭐ 3,211    \n   Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.  \n   🔗 [determined.ai](https://determined.ai)  \n\n38. \u003ca href=\"https://github.com/leptonai/leptonai\"\u003eleptonai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/leptonai/leptonai\"\u003eleptonai\u003c/a\u003e\u003c/b\u003e ⭐ 2,804    \n   A Pythonic framework to simplify AI service building  \n   🔗 [lepton.ai](https://lepton.ai/)  \n\n39. \u003ca href=\"https://github.com/michaelfeil/infinity\"\u003emichaelfeil/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/michaelfeil/infinity\"\u003einfinity\u003c/a\u003e\u003c/b\u003e ⭐ 2,635    \n   Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models, clip, clap and colpali  \n   🔗 [michaelfeil.github.io/infinity](https://michaelfeil.github.io/infinity/)  \n\n40. \u003ca href=\"https://github.com/apache/hamilton\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/hamilton\"\u003ehamilton\u003c/a\u003e\u003c/b\u003e ⭐ 2,374    \n   Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.  \n   🔗 [hamilton.apache.org](https://hamilton.apache.org/)  \n\n41. \u003ca href=\"https://github.com/meltano/meltano\"\u003emeltano/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/meltano/meltano\"\u003emeltano\u003c/a\u003e\u003c/b\u003e ⭐ 2,326    \n   Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.  \n   🔗 [meltano.com](https://meltano.com/)  \n\n42. \u003ca href=\"https://github.com/labmlai/labml\"\u003elabmlai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/labmlai/labml\"\u003elabml\u003c/a\u003e\u003c/b\u003e ⭐ 2,293    \n   🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱  \n   🔗 [labml.ai](https://labml.ai)  \n\n43. \u003ca href=\"https://github.com/vllm-project/production-stack\"\u003evllm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vllm-project/production-stack\"\u003eproduction-stack\u003c/a\u003e\u003c/b\u003e ⭐ 2,122    \n   vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization  \n   🔗 [docs.vllm.ai/projects/production-stack](https://docs.vllm.ai/projects/production-stack)  \n\n44. \u003ca href=\"https://github.com/dstackai/dstack\"\u003edstackai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dstackai/dstack\"\u003edstack\u003c/a\u003e\u003c/b\u003e ⭐ 2,018    \n   dstack is an open-source control plane for running development, training, and inference jobs on GPUs—across hyperscalers, neoclouds, or on-prem.  \n   🔗 [dstack.ai/docs](https://dstack.ai/docs)  \n\n45. \u003ca href=\"https://github.com/dagworks-inc/burr\"\u003edagworks-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dagworks-inc/burr\"\u003eburr\u003c/a\u003e\u003c/b\u003e ⭐ 1,891    \n   Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, trace, persist, and execute on your own infrastructure.  \n   🔗 [burr.apache.org](https://burr.apache.org/)  \n\n46. \u003ca href=\"https://github.com/substratusai/kubeai\"\u003esubstratusai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/substratusai/kubeai\"\u003ekubeai\u003c/a\u003e\u003c/b\u003e ⭐ 1,133    \n   AI Inference Operator for Kubernetes. The easiest way to serve ML models in production. Supports VLMs, LLMs, embeddings, and speech-to-text.  \n   🔗 [www.kubeai.org](https://www.kubeai.org)  \n\n47. \u003ca href=\"https://github.com/arize-ai/openinference\"\u003earize-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arize-ai/openinference\"\u003eopeninference\u003c/a\u003e\u003c/b\u003e ⭐ 833    \n   OpenInference is a set of conventions and plugins that is complimentary to OpenTelemetry to enable tracing of AI applications.  \n   🔗 [arize-ai.github.io/openinference](https://arize-ai.github.io/openinference/)  \n\n48. \u003ca href=\"https://github.com/lightonai/pylate\"\u003elightonai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lightonai/pylate\"\u003epylate\u003c/a\u003e\u003c/b\u003e ⭐ 690    \n   Built on Sentence Transformers, designed to simplify fine-tuning, inference, and retrieval with state-of-the-art ColBERT models  \n   🔗 [lightonai.github.io/pylate](https://lightonai.github.io/pylate/)  \n\n## Machine Learning - Reinforcement\n\nMachine learning libraries and toolkits that cross over with reinforcement learning in some way: agent reinforcement learning, agent environemnts, RLHF  \n\n1. \u003ca href=\"https://github.com/openai/gym\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/gym\"\u003egym\u003c/a\u003e\u003c/b\u003e ⭐ 36,974    \n   A toolkit for developing and comparing reinforcement learning algorithms.  \n   🔗 [www.gymlibrary.dev](https://www.gymlibrary.dev)  \n\n2. \u003ca href=\"https://github.com/lvwerra/trl\"\u003elvwerra/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lvwerra/trl\"\u003etrl\u003c/a\u003e\u003c/b\u003e ⭐ 17,119    \n   Train transformer language models with reinforcement learning.  \n   🔗 [hf.co/docs/trl](http://hf.co/docs/trl)  \n\n3. \u003ca href=\"https://github.com/openai/baselines\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/baselines\"\u003ebaselines\u003c/a\u003e\u003c/b\u003e ⭐ 16,627    \n   OpenAI Baselines: high-quality implementations of reinforcement learning algorithms  \n\n4. \u003ca href=\"https://github.com/farama-foundation/gymnasium\"\u003efarama-foundation/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/farama-foundation/gymnasium\"\u003eGymnasium\u003c/a\u003e\u003c/b\u003e ⭐ 11,179    \n   An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)  \n   🔗 [gymnasium.farama.org](https://gymnasium.farama.org)  \n\n5. \u003ca href=\"https://github.com/google/dopamine\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/dopamine\"\u003edopamine\u003c/a\u003e\u003c/b\u003e ⭐ 10,842    \n   Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.   \n   🔗 [github.com/google/dopamine](https://github.com/google/dopamine)  \n\n6. \u003ca href=\"https://github.com/thu-ml/tianshou\"\u003ethu-ml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thu-ml/tianshou\"\u003etianshou\u003c/a\u003e\u003c/b\u003e ⭐ 9,822    \n   An elegant PyTorch deep reinforcement learning library.  \n   🔗 [tianshou.org](https://tianshou.org)  \n\n7. \u003ca href=\"https://github.com/deepmind/pysc2\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/pysc2\"\u003epysc2\u003c/a\u003e\u003c/b\u003e ⭐ 8,247    \n   StarCraft II Learning Environment  \n\n8. \u003ca href=\"https://github.com/lucidrains/palm-rlhf-pytorch\"\u003elucidrains/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lucidrains/palm-rlhf-pytorch\"\u003ePaLM-rlhf-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 7,878    \n   Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM  \n\n9. \u003ca href=\"https://github.com/tensorlayer/tensorlayer\"\u003etensorlayer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tensorlayer/tensorlayer\"\u003eTensorLayer\u003c/a\u003e\u003c/b\u003e ⭐ 7,387    \n   Deep Learning and Reinforcement Learning Library for Scientists and Engineers   \n   🔗 [tensorlayerx.com](http://tensorlayerx.com)  \n\n10. \u003ca href=\"https://github.com/keras-rl/keras-rl\"\u003ekeras-rl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/keras-rl/keras-rl\"\u003ekeras-rl\u003c/a\u003e\u003c/b\u003e ⭐ 5,558    \n   Deep Reinforcement Learning for Keras.  \n   🔗 [keras-rl.readthedocs.io](http://keras-rl.readthedocs.io/)  \n\n11. \u003ca href=\"https://github.com/deepmind/dm_control\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/dm_control\"\u003edm_control\u003c/a\u003e\u003c/b\u003e ⭐ 4,424    \n   Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.  \n\n12. \u003ca href=\"https://github.com/ai4finance-foundation/elegantrl\"\u003eai4finance-foundation/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ai4finance-foundation/elegantrl\"\u003eElegantRL\u003c/a\u003e\u003c/b\u003e ⭐ 4,279    \n   Massively Parallel Deep Reinforcement Learning. 🔥  \n   🔗 [ai4finance.org](https://ai4finance.org)  \n\n13. \u003ca href=\"https://github.com/deepmind/acme\"\u003edeepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepmind/acme\"\u003eacme\u003c/a\u003e\u003c/b\u003e ⭐ 3,904    \n   A library of reinforcement learning components and agents  \n\n14. \u003ca href=\"https://github.com/facebookresearch/reagent\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/reagent\"\u003eReAgent\u003c/a\u003e\u003c/b\u003e ⭐ 3,684    \n   A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)  \n   🔗 [reagent.ai](https://reagent.ai)  \n\n15. \u003ca href=\"https://github.com/opendilab/di-engine\"\u003eopendilab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/opendilab/di-engine\"\u003eDI-engine\u003c/a\u003e\u003c/b\u003e ⭐ 3,582    \n   DI-engine is a generalized decision intelligence engine for PyTorch and JAX. It provides python-first and asynchronous-native task and middleware abstractions  \n   🔗 [di-engine-docs.readthedocs.io](https://di-engine-docs.readthedocs.io)  \n\n16. \u003ca href=\"https://github.com/pettingzoo-team/pettingzoo\"\u003epettingzoo-team/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pettingzoo-team/pettingzoo\"\u003ePettingZoo\u003c/a\u003e\u003c/b\u003e ⭐ 3,286    \n   An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities  \n   🔗 [pettingzoo.farama.org](https://pettingzoo.farama.org)  \n\n17. \u003ca href=\"https://github.com/pytorch/rl\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/rl\"\u003erl\u003c/a\u003e\u003c/b\u003e ⭐ 3,263    \n   A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.  \n   🔗 [pytorch.org/rl](https://pytorch.org/rl)  \n\n18. \u003ca href=\"https://github.com/eureka-research/eureka\"\u003eeureka-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eureka-research/eureka\"\u003eEureka\u003c/a\u003e\u003c/b\u003e ⭐ 3,107    \n   Official Repository for \"Eureka: Human-Level Reward Design via Coding Large Language Models\" (ICLR 2024)  \n   🔗 [eureka-research.github.io](https://eureka-research.github.io/)  \n\n19. \u003ca href=\"https://github.com/kzl/decision-transformer\"\u003ekzl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kzl/decision-transformer\"\u003edecision-transformer\u003c/a\u003e\u003c/b\u003e ⭐ 2,755    \n   Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling.  \n\n20. \u003ca href=\"https://github.com/anthropics/hh-rlhf\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/hh-rlhf\"\u003ehh-rlhf\u003c/a\u003e\u003c/b\u003e ⭐ 1,808    \n   Human preference data for \"Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback\"  \n   🔗 [arxiv.org/abs/2204.05862](https://arxiv.org/abs/2204.05862)  \n\n21. \u003ca href=\"https://github.com/google-deepmind/meltingpot\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/meltingpot\"\u003emeltingpot\u003c/a\u003e\u003c/b\u003e ⭐ 781    \n   A suite of test scenarios for multi-agent reinforcement learning.  \n\n22. \u003ca href=\"https://github.com/open-tinker/opentinker\"\u003eopen-tinker/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-tinker/opentinker\"\u003eOpenTinker\u003c/a\u003e\u003c/b\u003e ⭐ 598    \n   OpenTinker is an RL-as-a-Service infrastructure for foundation models, providing a flexible environment design framework that supports diverse training scenarios over data and interaction modes.  \n\n## Natural Language Processing\n\nNatural language processing libraries and toolkits: text processing, topic modelling, tokenisers, chatbots. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#llms-and-chatgpt\"\u003eLLMs and ChatGPT\u003c/a\u003e category for crossover.  \n\n1. \u003ca href=\"https://github.com/huggingface/transformers\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/transformers\"\u003etransformers\u003c/a\u003e\u003c/b\u003e ⭐ 155,622    \n   🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.   \n   🔗 [huggingface.co/transformers](https://huggingface.co/transformers)  \n\n2. \u003ca href=\"https://github.com/myshell-ai/openvoice\"\u003emyshell-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/myshell-ai/openvoice\"\u003eOpenVoice\u003c/a\u003e\u003c/b\u003e ⭐ 35,840    \n   Instant voice cloning by MIT and MyShell. Audio foundation model.  \n   🔗 [research.myshell.ai/open-voice](https://research.myshell.ai/open-voice)  \n\n3. \u003ca href=\"https://github.com/explosion/spacy\"\u003eexplosion/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/explosion/spacy\"\u003espaCy\u003c/a\u003e\u003c/b\u003e ⭐ 33,097    \n   💫 Industrial-strength Natural Language Processing (NLP) in Python  \n   🔗 [spacy.io](https://spacy.io)  \n\n4. \u003ca href=\"https://github.com/pytorch/fairseq\"\u003epytorch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytorch/fairseq\"\u003efairseq\u003c/a\u003e\u003c/b\u003e ⭐ 32,110    \n   Facebook AI Research Sequence-to-Sequence Toolkit written in Python.  \n\n5. \u003ca href=\"https://github.com/vikparuchuri/marker\"\u003evikparuchuri/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vikparuchuri/marker\"\u003emarker\u003c/a\u003e\u003c/b\u003e ⭐ 31,151    \n   Marker converts PDF, EPUB, and MOBI to markdown. It's 10x faster than nougat, more accurate on most documents, and has low hallucination risk.  \n   🔗 [www.datalab.to](https://www.datalab.to)  \n\n6. \u003ca href=\"https://github.com/microsoft/unilm\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/unilm\"\u003eunilm\u003c/a\u003e\u003c/b\u003e ⭐ 21,979    \n   Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities  \n   🔗 [aka.ms/generalai](https://aka.ms/GeneralAI)  \n\n7. \u003ca href=\"https://github.com/huggingface/datasets\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/datasets\"\u003edatasets\u003c/a\u003e\u003c/b\u003e ⭐ 21,122    \n   🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools  \n   🔗 [huggingface.co/docs/datasets](https://huggingface.co/docs/datasets)  \n\n8. \u003ca href=\"https://github.com/m-bain/whisperx\"\u003em-bain/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/m-bain/whisperx\"\u003ewhisperX\u003c/a\u003e\u003c/b\u003e ⭐ 19,783    \n   WhisperX:  Automatic Speech Recognition with Word-level Timestamps (\u0026 Diarization)  \n\n9. \u003ca href=\"https://github.com/vikparuchuri/surya\"\u003evikparuchuri/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vikparuchuri/surya\"\u003esurya\u003c/a\u003e\u003c/b\u003e ⭐ 19,159    \n   OCR, layout analysis, reading order, table recognition in 90+ languages  \n   🔗 [www.datalab.to](https://www.datalab.to)  \n\n10. \u003ca href=\"https://github.com/ukplab/sentence-transformers\"\u003eukplab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ukplab/sentence-transformers\"\u003esentence-transformers\u003c/a\u003e\u003c/b\u003e ⭐ 18,144    \n   State-of-the-Art Text Embeddings  \n   🔗 [www.sbert.net](https://www.sbert.net)  \n\n11. \u003ca href=\"https://github.com/openai/tiktoken\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/tiktoken\"\u003etiktoken\u003c/a\u003e\u003c/b\u003e ⭐ 17,074    \n   tiktoken is a fast BPE tokeniser for use with OpenAI's models.  \n\n12. \u003ca href=\"https://github.com/nvidia/nemo\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/nemo\"\u003eNeMo\u003c/a\u003e\u003c/b\u003e ⭐ 16,608    \n   A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)  \n   🔗 [docs.nvidia.com/nemo-framework/user-guide/latest/overview.html](https://docs.nvidia.com/nemo-framework/user-guide/latest/overview.html)  \n\n13. \u003ca href=\"https://github.com/rare-technologies/gensim\"\u003erare-technologies/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rare-technologies/gensim\"\u003egensim\u003c/a\u003e\u003c/b\u003e ⭐ 16,333    \n   Topic Modelling for Humans  \n   🔗 [radimrehurek.com/gensim](https://radimrehurek.com/gensim)  \n\n14. \u003ca href=\"https://github.com/gunthercox/chatterbot\"\u003egunthercox/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gunthercox/chatterbot\"\u003eChatterBot\u003c/a\u003e\u003c/b\u003e ⭐ 14,478    \n   ChatterBot is a machine learning, conversational dialog engine for creating chat bots  \n   🔗 [docs.chatterbot.us](http://docs.chatterbot.us/)  \n\n15. \u003ca href=\"https://github.com/nltk/nltk\"\u003enltk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nltk/nltk\"\u003enltk\u003c/a\u003e\u003c/b\u003e ⭐ 14,468    \n   NLTK Source  \n   🔗 [www.nltk.org](https://www.nltk.org)  \n\n16. \u003ca href=\"https://github.com/flairnlp/flair\"\u003eflairnlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/flairnlp/flair\"\u003eflair\u003c/a\u003e\u003c/b\u003e ⭐ 14,343    \n   A very simple framework for state-of-the-art Natural Language Processing (NLP)  \n   🔗 [flairnlp.github.io/flair](https://flairnlp.github.io/flair/)  \n\n17. \u003ca href=\"https://github.com/jina-ai/clip-as-service\"\u003ejina-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jina-ai/clip-as-service\"\u003eclip-as-service\u003c/a\u003e\u003c/b\u003e ⭐ 12,812    \n   🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP  \n   🔗 [clip-as-service.jina.ai](https://clip-as-service.jina.ai)  \n\n18. \u003ca href=\"https://github.com/neuml/txtai\"\u003eneuml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neuml/txtai\"\u003etxtai\u003c/a\u003e\u003c/b\u003e ⭐ 12,049    \n   💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows  \n   🔗 [neuml.github.io/txtai](https://neuml.github.io/txtai)  \n\n19. \u003ca href=\"https://github.com/allenai/allennlp\"\u003eallenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/allenai/allennlp\"\u003eallennlp\u003c/a\u003e\u003c/b\u003e ⭐ 11,888    \n   An open-source NLP research library, built on PyTorch.  \n   🔗 [www.allennlp.org](http://www.allennlp.org)  \n\n20. \u003ca href=\"https://github.com/facebookresearch/seamless_communication\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/seamless_communication\"\u003eseamless_communication\u003c/a\u003e\u003c/b\u003e ⭐ 11,738    \n   Foundational Models for State-of-the-Art Speech and Text Translation  \n\n21. \u003ca href=\"https://github.com/google/sentencepiece\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/sentencepiece\"\u003esentencepiece\u003c/a\u003e\u003c/b\u003e ⭐ 11,599    \n   Unsupervised text tokenizer for Neural Network-based text generation.  \n\n22. \u003ca href=\"https://github.com/speechbrain/speechbrain\"\u003espeechbrain/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/speechbrain/speechbrain\"\u003espeechbrain\u003c/a\u003e\u003c/b\u003e ⭐ 11,084    \n   A PyTorch-based Speech Toolkit  \n   🔗 [speechbrain.github.io](http://speechbrain.github.io)  \n\n23. \u003ca href=\"https://github.com/facebookresearch/parlai\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/parlai\"\u003eParlAI\u003c/a\u003e\u003c/b\u003e ⭐ 10,628    \n   A framework for training and evaluating AI models on a variety of openly available dialogue datasets.  \n   🔗 [parl.ai](https://parl.ai)  \n\n24. \u003ca href=\"https://github.com/doccano/doccano\"\u003edoccano/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/doccano/doccano\"\u003edoccano\u003c/a\u003e\u003c/b\u003e ⭐ 10,501    \n   Open source annotation tool for machine learning practitioners.  \n\n25. \u003ca href=\"https://github.com/facebookresearch/nougat\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/nougat\"\u003enougat\u003c/a\u003e\u003c/b\u003e ⭐ 9,809    \n   Implementation of Nougat Neural Optical Understanding for Academic Documents  \n   🔗 [facebookresearch.github.io/nougat](https://facebookresearch.github.io/nougat/)  \n\n26. \u003ca href=\"https://github.com/espnet/espnet\"\u003eespnet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/espnet/espnet\"\u003eespnet\u003c/a\u003e\u003c/b\u003e ⭐ 9,700    \n   End-to-End Speech Processing Toolkit  \n   🔗 [espnet.github.io/espnet](https://espnet.github.io/espnet/)  \n\n27. \u003ca href=\"https://github.com/sloria/textblob\"\u003esloria/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sloria/textblob\"\u003eTextBlob\u003c/a\u003e\u003c/b\u003e ⭐ 9,486    \n   Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.  \n   🔗 [textblob.readthedocs.io](https://textblob.readthedocs.io/)  \n\n28. \u003ca href=\"https://github.com/togethercomputer/openchatkit\"\u003etogethercomputer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/togethercomputer/openchatkit\"\u003eOpenChatKit\u003c/a\u003e\u003c/b\u003e ⭐ 9,015    \n   OpenChatKit provides a powerful, open-source base to create both specialized and general purpose chatbots  \n\n29. \u003ca href=\"https://github.com/clips/pattern\"\u003eclips/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/clips/pattern\"\u003epattern\u003c/a\u003e\u003c/b\u003e ⭐ 8,854    \n   Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.  \n   🔗 [github.com/clips/pattern/wiki](https://github.com/clips/pattern/wiki)  \n\n30. \u003ca href=\"https://github.com/maartengr/bertopic\"\u003emaartengr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/maartengr/bertopic\"\u003eBERTopic\u003c/a\u003e\u003c/b\u003e ⭐ 7,346    \n   Leveraging BERT and c-TF-IDF to create easily interpretable topics.   \n   🔗 [maartengr.github.io/bertopic](https://maartengr.github.io/BERTopic/)  \n\n31. \u003ca href=\"https://github.com/quivrhq/megaparse\"\u003equivrhq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quivrhq/megaparse\"\u003eMegaParse\u003c/a\u003e\u003c/b\u003e ⭐ 7,267    \n   File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.   \n   🔗 [megaparse.com](https://megaparse.com)  \n\n32. \u003ca href=\"https://github.com/deeppavlov/deeppavlov\"\u003edeeppavlov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deeppavlov/deeppavlov\"\u003eDeepPavlov\u003c/a\u003e\u003c/b\u003e ⭐ 6,963    \n   An open source library for deep learning end-to-end dialog systems and chatbots.  \n   🔗 [deeppavlov.ai](https://deeppavlov.ai)  \n\n33. \u003ca href=\"https://github.com/facebookresearch/metaseq\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/metaseq\"\u003emetaseq\u003c/a\u003e\u003c/b\u003e ⭐ 6,542    \n   A codebase for working with Open Pre-trained Transformers, originally forked from fairseq.  \n\n34. \u003ca href=\"https://github.com/kingoflolz/mesh-transformer-jax\"\u003ekingoflolz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kingoflolz/mesh-transformer-jax\"\u003emesh-transformer-jax\u003c/a\u003e\u003c/b\u003e ⭐ 6,363    \n   Model parallel transformers in JAX and Haiku  \n\n35. \u003ca href=\"https://github.com/aiwaves-cn/agents\"\u003eaiwaves-cn/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aiwaves-cn/agents\"\u003eagents\u003c/a\u003e\u003c/b\u003e ⭐ 5,858    \n   An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents  \n\n36. \u003ca href=\"https://github.com/layout-parser/layout-parser\"\u003elayout-parser/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/layout-parser/layout-parser\"\u003elayout-parser\u003c/a\u003e\u003c/b\u003e ⭐ 5,642    \n   A Unified Toolkit for Deep Learning Based Document Image Analysis  \n   🔗 [layout-parser.github.io](https://layout-parser.github.io/)  \n\n37. \u003ca href=\"https://github.com/salesforce/codegen\"\u003esalesforce/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforce/codegen\"\u003eCodeGen\u003c/a\u003e\u003c/b\u003e ⭐ 5,172    \n   CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.  \n\n38. \u003ca href=\"https://github.com/minimaxir/textgenrnn\"\u003eminimaxir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/minimaxir/textgenrnn\"\u003etextgenrnn\u003c/a\u003e\u003c/b\u003e ⭐ 4,932    \n   Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.  \n\n39. \u003ca href=\"https://github.com/argilla-io/argilla\"\u003eargilla-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/argilla-io/argilla\"\u003eargilla\u003c/a\u003e\u003c/b\u003e ⭐ 4,820    \n   Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets  \n   🔗 [argilla-io.github.io/argilla/latest](https://argilla-io.github.io/argilla/latest/)  \n\n40. \u003ca href=\"https://github.com/makcedward/nlpaug\"\u003emakcedward/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/makcedward/nlpaug\"\u003enlpaug\u003c/a\u003e\u003c/b\u003e ⭐ 4,641    \n   Data augmentation for NLP   \n   🔗 [makcedward.github.io](https://makcedward.github.io/)  \n\n41. \u003ca href=\"https://github.com/promptslab/promptify\"\u003epromptslab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/promptslab/promptify\"\u003ePromptify\u003c/a\u003e\u003c/b\u003e ⭐ 4,536    \n   Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research  \n   🔗 [discord.gg/m88xfymbk6](https://discord.gg/m88xfYMbK6)  \n\n42. \u003ca href=\"https://github.com/facebookresearch/drqa\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/drqa\"\u003eDrQA\u003c/a\u003e\u003c/b\u003e ⭐ 4,480    \n   Reading Wikipedia to Answer Open-Domain Questions  \n\n43. \u003ca href=\"https://github.com/thilinarajapakse/simpletransformers\"\u003ethilinarajapakse/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thilinarajapakse/simpletransformers\"\u003esimpletransformers\u003c/a\u003e\u003c/b\u003e ⭐ 4,227    \n   Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI  \n   🔗 [simpletransformers.ai](https://simpletransformers.ai/)  \n\n44. \u003ca href=\"https://github.com/maartengr/keybert\"\u003emaartengr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/maartengr/keybert\"\u003eKeyBERT\u003c/a\u003e\u003c/b\u003e ⭐ 4,086    \n   A minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.  \n   🔗 [maartengr.github.io/keybert](https://MaartenGr.github.io/KeyBERT/)  \n\n45. \u003ca href=\"https://github.com/rapidfuzz/rapidfuzz\"\u003erapidfuzz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rapidfuzz/rapidfuzz\"\u003eRapidFuzz\u003c/a\u003e\u003c/b\u003e ⭐ 3,717    \n   Rapid fuzzy string matching in Python using various string metrics  \n   🔗 [rapidfuzz.github.io/rapidfuzz](https://rapidfuzz.github.io/RapidFuzz/)  \n\n46. \u003ca href=\"https://github.com/chonkie-inc/chonkie\"\u003echonkie-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chonkie-inc/chonkie\"\u003echonkie\u003c/a\u003e\u003c/b\u003e ⭐ 3,635    \n   🦛 CHONK docs with Chonkie ✨ — The lightweight ingestion library for fast, efficient and robust RAG pipelines  \n   🔗 [docs.chonkie.ai](https://docs.chonkie.ai)  \n\n47. \u003ca href=\"https://github.com/life4/textdistance\"\u003elife4/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/life4/textdistance\"\u003etextdistance\u003c/a\u003e\u003c/b\u003e ⭐ 3,512    \n   📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.  \n\n48. \u003ca href=\"https://github.com/bytedance/lightseq\"\u003ebytedance/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bytedance/lightseq\"\u003elightseq\u003c/a\u003e\u003c/b\u003e ⭐ 3,304    \n   LightSeq: A High Performance Library for Sequence Processing and Generation  \n\n49. \u003ca href=\"https://github.com/neuralmagic/deepsparse\"\u003eneuralmagic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neuralmagic/deepsparse\"\u003edeepsparse\u003c/a\u003e\u003c/b\u003e ⭐ 3,159    \n   Sparsity-aware deep learning inference runtime for CPUs  \n   🔗 [neuralmagic.com/deepsparse](https://neuralmagic.com/deepsparse/)  \n\n50. \u003ca href=\"https://github.com/huawei-noah/pretrained-language-model\"\u003ehuawei-noah/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huawei-noah/pretrained-language-model\"\u003ePretrained-Language-Model\u003c/a\u003e\u003c/b\u003e ⭐ 3,155    \n   Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.  \n\n51. \u003ca href=\"https://github.com/ddangelov/top2vec\"\u003eddangelov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ddangelov/top2vec\"\u003eTop2Vec\u003c/a\u003e\u003c/b\u003e ⭐ 3,108    \n   Top2Vec learns jointly embedded topic, document and word vectors.  \n\n52. \u003ca href=\"https://github.com/salesforce/codet5\"\u003esalesforce/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforce/codet5\"\u003eCodeT5\u003c/a\u003e\u003c/b\u003e ⭐ 3,096    \n   Home of CodeT5: Open Code LLMs for Code Understanding and Generation  \n   🔗 [arxiv.org/abs/2305.07922](https://arxiv.org/abs/2305.07922)  \n\n53. \u003ca href=\"https://github.com/bigscience-workshop/promptsource\"\u003ebigscience-workshop/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bigscience-workshop/promptsource\"\u003epromptsource\u003c/a\u003e\u003c/b\u003e ⭐ 2,994    \n   Toolkit for creating, sharing and using natural language prompts.  \n\n54. \u003ca href=\"https://github.com/jbesomi/texthero\"\u003ejbesomi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jbesomi/texthero\"\u003etexthero\u003c/a\u003e\u003c/b\u003e ⭐ 2,917    \n   Text preprocessing, representation and visualization from zero to hero.  \n   🔗 [texthero.org](https://texthero.org)  \n\n55. \u003ca href=\"https://github.com/huggingface/neuralcoref\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/neuralcoref\"\u003eneuralcoref\u003c/a\u003e\u003c/b\u003e ⭐ 2,890    \n   ✨Fast Coreference Resolution in spaCy with Neural Networks  \n   🔗 [huggingface.co/coref](https://huggingface.co/coref/)  \n\n56. \u003ca href=\"https://github.com/nvidia/nv-ingest\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/nv-ingest\"\u003env-ingest\u003c/a\u003e\u003c/b\u003e ⭐ 2,817    \n   NVIDIA-Ingest is a scalable, performance-oriented document content and metadata extraction microservice.  \n   🔗 [docs.nvidia.com/nemo/retriever/latest/extraction/overview](https://docs.nvidia.com/nemo/retriever/latest/extraction/overview/)  \n\n57. \u003ca href=\"https://github.com/urchade/gliner\"\u003eurchade/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/urchade/gliner\"\u003eGLiNER\u003c/a\u003e\u003c/b\u003e ⭐ 2,726    \n   Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 2024  \n   🔗 [urchade.github.io/gliner](https://urchade.github.io/GLiNER)  \n\n58. \u003ca href=\"https://github.com/huggingface/setfit\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/setfit\"\u003esetfit\u003c/a\u003e\u003c/b\u003e ⭐ 2,673    \n   SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers.  \n   🔗 [hf.co/docs/setfit](https://hf.co/docs/setfit)  \n\n59. \u003ca href=\"https://github.com/alibaba/easynlp\"\u003ealibaba/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alibaba/easynlp\"\u003eEasyNLP\u003c/a\u003e\u003c/b\u003e ⭐ 2,182    \n   EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit  \n\n60. \u003ca href=\"https://github.com/thudm/p-tuning-v2\"\u003ethudm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thudm/p-tuning-v2\"\u003eP-tuning-v2\u003c/a\u003e\u003c/b\u003e ⭐ 2,075    \n   An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks  \n\n61. \u003ca href=\"https://github.com/featureform/embeddinghub\"\u003efeatureform/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/featureform/embeddinghub\"\u003efeatureform\u003c/a\u003e\u003c/b\u003e ⭐ 1,961    \n   The Virtual Feature Store. Turn your existing data infrastructure into a feature store.  \n   🔗 [www.featureform.com](https://www.featureform.com)  \n\n62. \u003ca href=\"https://github.com/marella/ctransformers\"\u003emarella/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marella/ctransformers\"\u003ectransformers\u003c/a\u003e\u003c/b\u003e ⭐ 1,878    \n   Python bindings for the Transformer models implemented in C/C++ using GGML library.  \n\n63. \u003ca href=\"https://github.com/nomic-ai/nomic\"\u003enomic-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nomic-ai/nomic\"\u003enomic\u003c/a\u003e\u003c/b\u003e ⭐ 1,859    \n   Nomic Developer API SDK  \n   🔗 [atlas.nomic.ai](https://atlas.nomic.ai)  \n\n64. \u003ca href=\"https://github.com/intellabs/fastrag\"\u003eintellabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/intellabs/fastrag\"\u003efastRAG\u003c/a\u003e\u003c/b\u003e ⭐ 1,760    \n   Efficient Retrieval Augmentation and Generation Framework  \n\n65. \u003ca href=\"https://github.com/pemistahl/lingua-py\"\u003epemistahl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pemistahl/lingua-py\"\u003elingua-py\u003c/a\u003e\u003c/b\u003e ⭐ 1,625    \n   The most accurate natural language detection library for Python, suitable for short text and mixed-language text  \n\n66. \u003ca href=\"https://github.com/answerdotai/modernbert\"\u003eanswerdotai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/answerdotai/modernbert\"\u003eModernBERT\u003c/a\u003e\u003c/b\u003e ⭐ 1,620    \n   Bringing BERT into modernity via both architecture changes and scaling  \n   🔗 [arxiv.org/abs/2412.13663](https://arxiv.org/abs/2412.13663)  \n\n67. \u003ca href=\"https://github.com/xhluca/bm25s\"\u003exhluca/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xhluca/bm25s\"\u003ebm25s\u003c/a\u003e\u003c/b\u003e ⭐ 1,464    \n   Fast lexical search implementing BM25 in Python using Numpy, Numba and Scipy  \n   🔗 [bm25s.github.io](https://bm25s.github.io)  \n\n68. \u003ca href=\"https://github.com/openai/grade-school-math\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/grade-school-math\"\u003egrade-school-math\u003c/a\u003e\u003c/b\u003e ⭐ 1,384    \n   GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems  \n\n69. \u003ca href=\"https://github.com/jonasgeiping/cramming\"\u003ejonasgeiping/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jonasgeiping/cramming\"\u003ecramming\u003c/a\u003e\u003c/b\u003e ⭐ 1,361    \n   Cramming the training of a (BERT-type) language model into limited compute.  \n\n70. \u003ca href=\"https://github.com/abertsch72/unlimiformer\"\u003eabertsch72/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/abertsch72/unlimiformer\"\u003eunlimiformer\u003c/a\u003e\u003c/b\u003e ⭐ 1,066    \n   Public repo for the NeurIPS 2023 paper \"Unlimiformer: Long-Range Transformers with Unlimited Length Input\"  \n\n71. \u003ca href=\"https://github.com/webis-de/small-text\"\u003ewebis-de/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/webis-de/small-text\"\u003esmall-text\u003c/a\u003e\u003c/b\u003e ⭐ 635    \n   Small-Text provides state-of-the-art Active Learning for Text Classification. Several pre-implemented Query Strategies, Initialization Strategies, and Stopping Critera are provided, which can be easily mixed and matched to build active learning experiments or applications.  \n   🔗 [small-text.readthedocs.io](https://small-text.readthedocs.io/)  \n\n72. \u003ca href=\"https://github.com/fastino-ai/gliner2\"\u003efastino-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fastino-ai/gliner2\"\u003eGLiNER2\u003c/a\u003e\u003c/b\u003e ⭐ 539    \n   GLiNER2 unifies Named Entity Recognition, Text Classification, and Structured Data Extraction into a single 205M parameter model. It provides efficient CPU-based inference without requiring complex pipelines or external API dependencies.  \n\n## Packaging\n\nPython packaging, dependency management and bundling.  \n\n1. \u003ca href=\"https://github.com/astral-sh/uv\"\u003eastral-sh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/astral-sh/uv\"\u003euv\u003c/a\u003e\u003c/b\u003e ⭐ 77,640    \n   An extremely fast Python package installer and resolver, written in Rust. Designed as a drop-in replacement for pip and pip-compile.  \n   🔗 [docs.astral.sh/uv](https://docs.astral.sh/uv)  \n\n2. \u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyenv/pyenv\"\u003epyenv\u003c/a\u003e\u003c/b\u003e ⭐ 44,127    \n   pyenv lets you easily switch between multiple versions of Python.  \n\n3. \u003ca href=\"https://github.com/python-poetry/poetry\"\u003epython-poetry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-poetry/poetry\"\u003epoetry\u003c/a\u003e\u003c/b\u003e ⭐ 34,157    \n   Python packaging and dependency management made easy  \n   🔗 [python-poetry.org](https://python-poetry.org)  \n\n4. \u003ca href=\"https://github.com/pypa/pipenv\"\u003epypa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypa/pipenv\"\u003epipenv\u003c/a\u003e\u003c/b\u003e ⭐ 25,106    \n   A virtualenv management tool that supports a multitude of systems and nicely bridges the gaps between pip, python and virtualenv.  \n   🔗 [pipenv.pypa.io](https://pipenv.pypa.io)  \n\n5. \u003ca href=\"https://github.com/mitsuhiko/rye\"\u003emitsuhiko/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mitsuhiko/rye\"\u003erye\u003c/a\u003e\u003c/b\u003e ⭐ 14,304    \n   a Hassle-Free Python Experience  \n   🔗 [rye.astral.sh](https://rye.astral.sh)  \n\n6. \u003ca href=\"https://github.com/pyinstaller/pyinstaller\"\u003epyinstaller/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyinstaller/pyinstaller\"\u003epyinstaller\u003c/a\u003e\u003c/b\u003e ⭐ 12,850    \n   Freeze (package) Python programs into stand-alone executables  \n   🔗 [www.pyinstaller.org](http://www.pyinstaller.org)  \n\n7. \u003ca href=\"https://github.com/pypa/pipx\"\u003epypa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypa/pipx\"\u003epipx\u003c/a\u003e\u003c/b\u003e ⭐ 12,457    \n   Install and Run Python Applications in Isolated Environments  \n   🔗 [pipx.pypa.io](https://pipx.pypa.io)  \n\n8. \u003ca href=\"https://github.com/conda-forge/miniforge\"\u003econda-forge/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/conda-forge/miniforge\"\u003eminiforge\u003c/a\u003e\u003c/b\u003e ⭐ 9,199    \n   A conda-forge distribution.  \n   🔗 [conda-forge.org/download](https://conda-forge.org/download)  \n\n9. \u003ca href=\"https://github.com/pdm-project/pdm\"\u003epdm-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pdm-project/pdm\"\u003epdm\u003c/a\u003e\u003c/b\u003e ⭐ 8,541    \n   A modern Python package and dependency manager supporting the latest PEP standards  \n   🔗 [pdm-project.org](https://pdm-project.org)  \n\n10. \u003ca href=\"https://github.com/jazzband/pip-tools\"\u003ejazzband/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jazzband/pip-tools\"\u003epip-tools\u003c/a\u003e\u003c/b\u003e ⭐ 7,990    \n   A set of tools to keep your pinned Python dependencies fresh (pip-compile + pip-sync)  \n   🔗 [pip-tools.rtfd.io](https://pip-tools.rtfd.io)  \n\n11. \u003ca href=\"https://github.com/mamba-org/mamba\"\u003emamba-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mamba-org/mamba\"\u003emamba\u003c/a\u003e\u003c/b\u003e ⭐ 7,877    \n   The Fast Cross-Platform Package Manager: mamba is a reimplementation of the conda package manager in C++  \n   🔗 [mamba.readthedocs.io](https://mamba.readthedocs.io)  \n\n12. \u003ca href=\"https://github.com/conda/conda\"\u003econda/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/conda/conda\"\u003econda\u003c/a\u003e\u003c/b\u003e ⭐ 7,283    \n   A system-level, binary package and environment manager running on all major operating systems and platforms.  \n   🔗 [docs.conda.io/projects/conda](https://docs.conda.io/projects/conda/)  \n\n13. \u003ca href=\"https://github.com/pypa/hatch\"\u003epypa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypa/hatch\"\u003ehatch\u003c/a\u003e\u003c/b\u003e ⭐ 7,109    \n   Modern, extensible Python project management  \n   🔗 [hatch.pypa.io/latest](https://hatch.pypa.io/latest/)  \n\n14. \u003ca href=\"https://github.com/prefix-dev/pixi\"\u003eprefix-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prefix-dev/pixi\"\u003epixi\u003c/a\u003e\u003c/b\u003e ⭐ 6,197    \n   pixi is a cross-platform, multi-language package manager and workflow tool built on the foundation of the conda ecosystem.  \n   🔗 [pixi.sh](https://pixi.sh)  \n\n15. \u003ca href=\"https://github.com/indygreg/pyoxidizer\"\u003eindygreg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/indygreg/pyoxidizer\"\u003ePyOxidizer\u003c/a\u003e\u003c/b\u003e ⭐ 6,060    \n   A modern Python application packaging and distribution tool  \n\n16. \u003ca href=\"https://github.com/pypa/virtualenv\"\u003epypa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypa/virtualenv\"\u003evirtualenv\u003c/a\u003e\u003c/b\u003e ⭐ 5,007    \n   A tool to create isolated Python environments. Since Python 3.3, a subset of it has been integrated into the standard lib venv module.  \n   🔗 [virtualenv.pypa.io](https://virtualenv.pypa.io)  \n\n17. \u003ca href=\"https://github.com/spack/spack\"\u003espack/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spack/spack\"\u003espack\u003c/a\u003e\u003c/b\u003e ⭐ 4,931    \n   A flexible package manager that supports multiple versions, configurations, platforms, and compilers.  \n   🔗 [spack.io](https://spack.io)  \n\n18. \u003ca href=\"https://github.com/pantsbuild/pex\"\u003epantsbuild/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pantsbuild/pex\"\u003epex\u003c/a\u003e\u003c/b\u003e ⭐ 4,170    \n   A tool for generating .pex (Python EXecutable) files, lock files and venvs.  \n   🔗 [docs.pex-tool.org](https://docs.pex-tool.org/)  \n\n19. \u003ca href=\"https://github.com/pypa/flit\"\u003epypa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypa/flit\"\u003eflit\u003c/a\u003e\u003c/b\u003e ⭐ 2,241    \n   Simplified packaging of Python modules  \n   🔗 [flit.pypa.io](https://flit.pypa.io/)  \n\n20. \u003ca href=\"https://github.com/ofek/pyapp\"\u003eofek/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ofek/pyapp\"\u003epyapp\u003c/a\u003e\u003c/b\u003e ⭐ 1,913    \n   Runtime installer for Python applications  \n   🔗 [ofek.dev/pyapp](https://ofek.dev/pyapp/)  \n\n21. \u003ca href=\"https://github.com/python-poetry/install.python-poetry.org\"\u003epython-poetry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-poetry/install.python-poetry.org\"\u003einstall.python-poetry.org\u003c/a\u003e\u003c/b\u003e ⭐ 247    \n   The official Poetry installation script  \n   🔗 [install.python-poetry.org](https://install.python-poetry.org)  \n\n## Pandas\n\nPandas and dataframe libraries: data analysis, statistical reporting, pandas GUIs, pandas performance optimisations.  \n\n1. \u003ca href=\"https://github.com/pandas-dev/pandas\"\u003epandas-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pandas-dev/pandas\"\u003epandas\u003c/a\u003e\u003c/b\u003e ⭐ 47,680    \n   Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more  \n   🔗 [pandas.pydata.org](https://pandas.pydata.org)  \n\n2. \u003ca href=\"https://github.com/pola-rs/polars\"\u003epola-rs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pola-rs/polars\"\u003epolars\u003c/a\u003e\u003c/b\u003e ⭐ 37,116    \n   Extremely fast Query Engine for DataFrames, written in Rust  \n   🔗 [docs.pola.rs](https://docs.pola.rs)  \n\n3. \u003ca href=\"https://github.com/duckdb/duckdb\"\u003educkdb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/duckdb/duckdb\"\u003educkdb\u003c/a\u003e\u003c/b\u003e ⭐ 35,624    \n   DuckDB is an analytical in-process SQL database management system  \n   🔗 [www.duckdb.org](http://www.duckdb.org)  \n\n4. \u003ca href=\"https://github.com/gventuri/pandas-ai\"\u003egventuri/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gventuri/pandas-ai\"\u003epandas-ai\u003c/a\u003e\u003c/b\u003e ⭐ 23,060    \n   Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.  \n   🔗 [pandas-ai.com](https://pandas-ai.com)  \n\n5. \u003ca href=\"https://github.com/kanaries/pygwalker\"\u003ekanaries/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kanaries/pygwalker\"\u003epygwalker\u003c/a\u003e\u003c/b\u003e ⭐ 15,593    \n   PyGWalker: Turn your dataframe into an interactive UI for visual analysis  \n   🔗 [kanaries.net/pygwalker](https://kanaries.net/pygwalker)  \n\n6. \u003ca href=\"https://github.com/ydataai/ydata-profiling\"\u003eydataai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ydataai/ydata-profiling\"\u003eydata-profiling\u003c/a\u003e\u003c/b\u003e ⭐ 13,343    \n   1 Line of code data quality profiling \u0026 exploratory data analysis for Pandas and Spark DataFrames.   \n   🔗 [docs.sdk.ydata.ai](https://docs.sdk.ydata.ai)  \n\n7. \u003ca href=\"https://github.com/rapidsai/cudf\"\u003erapidsai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rapidsai/cudf\"\u003ecudf\u003c/a\u003e\u003c/b\u003e ⭐ 9,468    \n   cuDF is a GPU DataFrame library for loading joining, aggregating, filtering, and otherwise manipulating data  \n   🔗 [docs.rapids.ai/api/cudf/stable](https://docs.rapids.ai/api/cudf/stable/)  \n\n8. \u003ca href=\"https://github.com/eventual-inc/daft\"\u003eeventual-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eventual-inc/daft\"\u003eDaft\u003c/a\u003e\u003c/b\u003e ⭐ 5,144    \n   High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale  \n   🔗 [daft.ai](https://daft.ai)  \n\n9. \u003ca href=\"https://github.com/deepseek-ai/smallpond\"\u003edeepseek-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/deepseek-ai/smallpond\"\u003esmallpond\u003c/a\u003e\u003c/b\u003e ⭐ 4,905    \n   A lightweight data processing framework built on DuckDB and 3FS.  \n\n10. \u003ca href=\"https://github.com/unionai-oss/pandera\"\u003eunionai-oss/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/unionai-oss/pandera\"\u003epandera\u003c/a\u003e\u003c/b\u003e ⭐ 4,180    \n   A light-weight, flexible, and expressive statistical data testing library  \n   🔗 [www.union.ai/pandera](https://www.union.ai/pandera)  \n\n11. \u003ca href=\"https://github.com/aws/aws-sdk-pandas\"\u003eaws/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aws/aws-sdk-pandas\"\u003eaws-sdk-pandas\u003c/a\u003e\u003c/b\u003e ⭐ 4,093    \n   pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).  \n   🔗 [aws-sdk-pandas.readthedocs.io](https://aws-sdk-pandas.readthedocs.io)  \n\n12. \u003ca href=\"https://github.com/nalepae/pandarallel\"\u003enalepae/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nalepae/pandarallel\"\u003epandarallel\u003c/a\u003e\u003c/b\u003e ⭐ 3,806    \n   A simple and efficient tool to parallelize Pandas operations on all available CPUs  \n   🔗 [nalepae.github.io/pandarallel](https://nalepae.github.io/pandarallel)  \n\n13. \u003ca href=\"https://github.com/adamerose/pandasgui\"\u003eadamerose/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/adamerose/pandasgui\"\u003ePandasGUI\u003c/a\u003e\u003c/b\u003e ⭐ 3,265    \n   A GUI for Pandas DataFrames  \n\n14. \u003ca href=\"https://github.com/delta-io/delta-rs\"\u003edelta-io/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/delta-io/delta-rs\"\u003edelta-rs\u003c/a\u003e\u003c/b\u003e ⭐ 3,114    \n   A native Rust library for Delta Lake, with bindings into Python  \n   🔗 [delta-io.github.io/delta-rs](https://delta-io.github.io/delta-rs/)  \n\n15. \u003ca href=\"https://github.com/jmcarpenter2/swifter\"\u003ejmcarpenter2/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jmcarpenter2/swifter\"\u003eswifter\u003c/a\u003e\u003c/b\u003e ⭐ 2,641    \n   A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner  \n\n16. \u003ca href=\"https://github.com/fugue-project/fugue\"\u003efugue-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fugue-project/fugue\"\u003efugue\u003c/a\u003e\u003c/b\u003e ⭐ 2,136    \n   A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.  \n   🔗 [fugue-tutorials.readthedocs.io](https://fugue-tutorials.readthedocs.io/)  \n\n17. \u003ca href=\"https://github.com/pyjanitor-devs/pyjanitor\"\u003epyjanitor-devs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyjanitor-devs/pyjanitor\"\u003epyjanitor\u003c/a\u003e\u003c/b\u003e ⭐ 1,477    \n   Clean APIs for data cleaning. Python implementation of R package Janitor  \n   🔗 [pyjanitor-devs.github.io/pyjanitor](https://pyjanitor-devs.github.io/pyjanitor)  \n\n18. \u003ca href=\"https://github.com/renumics/spotlight\"\u003erenumics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/renumics/spotlight\"\u003espotlight\u003c/a\u003e\u003c/b\u003e ⭐ 1,245    \n   Interactively explore unstructured datasets from your dataframe.  \n   🔗 [renumics.com](https://renumics.com)  \n\n## Performance\n\nPerformance, parallelisation and low level libraries.  \n\n1. \u003ca href=\"https://github.com/celery/celery\"\u003ecelery/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/celery/celery\"\u003ecelery\u003c/a\u003e\u003c/b\u003e ⭐ 27,904    \n   Distributed Task Queue (development branch)  \n   🔗 [docs.celeryq.dev](https://docs.celeryq.dev)  \n\n2. \u003ca href=\"https://github.com/google/flatbuffers\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/flatbuffers\"\u003eflatbuffers\u003c/a\u003e\u003c/b\u003e ⭐ 25,451    \n   FlatBuffers: Memory Efficient Serialization Library  \n   🔗 [flatbuffers.dev](https://flatbuffers.dev/)  \n\n3. \u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pybind/pybind11\"\u003epybind11\u003c/a\u003e\u003c/b\u003e ⭐ 17,658    \n   Seamless operability between C++11 and Python  \n   🔗 [pybind11.readthedocs.io](https://pybind11.readthedocs.io/)  \n\n4. \u003ca href=\"https://github.com/exaloop/codon\"\u003eexaloop/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/exaloop/codon\"\u003ecodon\u003c/a\u003e\u003c/b\u003e ⭐ 16,566    \n   A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support  \n   🔗 [docs.exaloop.io](https://docs.exaloop.io)  \n\n5. \u003ca href=\"https://github.com/dask/dask\"\u003edask/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dask/dask\"\u003edask\u003c/a\u003e\u003c/b\u003e ⭐ 13,728    \n   Parallel computing with task scheduling  \n   🔗 [dask.org](https://dask.org)  \n\n6. \u003ca href=\"https://github.com/numba/numba\"\u003enumba/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/numba/numba\"\u003enumba\u003c/a\u003e\u003c/b\u003e ⭐ 10,865    \n   NumPy aware dynamic Python compiler using LLVM  \n   🔗 [numba.pydata.org](https://numba.pydata.org/)  \n\n7. \u003ca href=\"https://github.com/modin-project/modin\"\u003emodin-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modin-project/modin\"\u003emodin\u003c/a\u003e\u003c/b\u003e ⭐ 10,350    \n   Modin: Scale your Pandas workflows by changing a single line of code  \n   🔗 [modin.readthedocs.io](http://modin.readthedocs.io)  \n\n8. \u003ca href=\"https://github.com/vaexio/vaex\"\u003evaexio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vaexio/vaex\"\u003evaex\u003c/a\u003e\u003c/b\u003e ⭐ 8,469    \n   Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀  \n   🔗 [vaex.io](https://vaex.io)  \n\n9. \u003ca href=\"https://github.com/nebuly-ai/nebullvm\"\u003enebuly-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nebuly-ai/nebullvm\"\u003eoptimate\u003c/a\u003e\u003c/b\u003e ⭐ 8,353    \n   A collection of libraries to optimise AI model performances  \n   🔗 [www.nebuly.com](https://www.nebuly.com/)  \n\n10. \u003ca href=\"https://github.com/python-trio/trio\"\u003epython-trio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-trio/trio\"\u003etrio\u003c/a\u003e\u003c/b\u003e ⭐ 7,110    \n   Trio – a friendly Python library for async concurrency and I/O  \n   🔗 [trio.readthedocs.io](https://trio.readthedocs.io)  \n\n11. \u003ca href=\"https://github.com/mher/flower\"\u003emher/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mher/flower\"\u003eflower\u003c/a\u003e\u003c/b\u003e ⭐ 7,096    \n   Real-time monitor and web admin for Celery distributed task queue  \n   🔗 [flower.readthedocs.io](https://flower.readthedocs.io)  \n\n12. \u003ca href=\"https://github.com/airtai/faststream\"\u003eairtai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/airtai/faststream\"\u003efaststream\u003c/a\u003e\u003c/b\u003e ⭐ 4,901    \n   FastStream is a powerful and easy-to-use asynchronous Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.  \n   🔗 [faststream.ag2.ai/latest](http://faststream.ag2.ai/latest/)  \n\n13. \u003ca href=\"https://github.com/tlkh/asitop\"\u003etlkh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tlkh/asitop\"\u003easitop\u003c/a\u003e\u003c/b\u003e ⭐ 4,419    \n   Perf monitoring CLI tool for Apple Silicon  \n   🔗 [tlkh.github.io/asitop](https://tlkh.github.io/asitop/)  \n\n14. \u003ca href=\"https://github.com/facebookincubator/cinder\"\u003efacebookincubator/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookincubator/cinder\"\u003ecinder\u003c/a\u003e\u003c/b\u003e ⭐ 3,757    \n   This is Meta's fork of the CPython runtime.  The name \"cinder\" here is historical, see https://github.com/facebookincubator/cinderx for the Python extension / JIT compiler.  \n\n15. \u003ca href=\"https://github.com/agronholm/anyio\"\u003eagronholm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agronholm/anyio\"\u003eanyio\u003c/a\u003e\u003c/b\u003e ⭐ 2,367    \n   High level asynchronous concurrency and networking framework that works on top of either Trio or asyncio  \n\n16. \u003ca href=\"https://github.com/tiangolo/asyncer\"\u003etiangolo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tiangolo/asyncer\"\u003easyncer\u003c/a\u003e\u003c/b\u003e ⭐ 2,343    \n   Asyncer, async and await, focused on developer experience.  \n   🔗 [asyncer.tiangolo.com](https://asyncer.tiangolo.com/)  \n\n17. \u003ca href=\"https://github.com/intel/intel-extension-for-transformers\"\u003eintel/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/intel/intel-extension-for-transformers\"\u003eintel-extension-for-transformers\u003c/a\u003e\u003c/b\u003e ⭐ 2,173    \n   ⚡ Build your chatbot within minutes on your favorite device; offer SOTA compression techniques for LLMs; run LLMs efficiently on Intel Platforms⚡  \n\n18. \u003ca href=\"https://github.com/intel/intel-extension-for-pytorch\"\u003eintel/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/intel/intel-extension-for-pytorch\"\u003eintel-extension-for-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 2,005    \n   A Python package for extending the official PyTorch that can easily obtain performance on Intel platform  \n\n19. \u003ca href=\"https://github.com/faster-cpython/ideas\"\u003efaster-cpython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/faster-cpython/ideas\"\u003eideas\u003c/a\u003e\u003c/b\u003e ⭐ 1,727    \n   Discussion and work tracker for Faster CPython project.  \n\n## Profiling\n\nMemory and CPU/GPU profiling tools and libraries.  \n\n1. \u003ca href=\"https://github.com/benfred/py-spy\"\u003ebenfred/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/benfred/py-spy\"\u003epy-spy\u003c/a\u003e\u003c/b\u003e ⭐ 14,865    \n   Sampling profiler for Python programs  \n\n2. \u003ca href=\"https://github.com/bloomberg/memray\"\u003ebloomberg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bloomberg/memray\"\u003ememray\u003c/a\u003e\u003c/b\u003e ⭐ 14,792    \n   Memray is a memory profiler for Python  \n   🔗 [bloomberg.github.io/memray](https://bloomberg.github.io/memray/)  \n\n3. \u003ca href=\"https://github.com/plasma-umass/scalene\"\u003eplasma-umass/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plasma-umass/scalene\"\u003escalene\u003c/a\u003e\u003c/b\u003e ⭐ 13,243    \n   Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals  \n\n4. \u003ca href=\"https://github.com/joerick/pyinstrument\"\u003ejoerick/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/joerick/pyinstrument\"\u003epyinstrument\u003c/a\u003e\u003c/b\u003e ⭐ 7,596    \n   🚴 Call stack profiler for Python. Shows you why your code is slow!  \n   🔗 [pyinstrument.readthedocs.io](https://pyinstrument.readthedocs.io/)  \n\n5. \u003ca href=\"https://github.com/gaogaotiantian/viztracer\"\u003egaogaotiantian/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gaogaotiantian/viztracer\"\u003eviztracer\u003c/a\u003e\u003c/b\u003e ⭐ 7,523    \n   A debugging and profiling tool that can trace and visualize python code execution  \n   🔗 [viztracer.readthedocs.io](https://viztracer.readthedocs.io/)  \n\n6. \u003ca href=\"https://github.com/pythonprofilers/memory_profiler\"\u003epythonprofilers/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pythonprofilers/memory_profiler\"\u003ememory_profiler\u003c/a\u003e\u003c/b\u003e ⭐ 4,549    \n   Monitor Memory usage of Python code  \n   🔗 [pypi.python.org/pypi/memory_profiler](http://pypi.python.org/pypi/memory_profiler)  \n\n7. \u003ca href=\"https://github.com/pyutils/line_profiler\"\u003epyutils/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyutils/line_profiler\"\u003eline_profiler\u003c/a\u003e\u003c/b\u003e ⭐ 3,188    \n   Line-by-line profiling for Python  \n\n8. \u003ca href=\"https://github.com/reloadware/reloadium\"\u003ereloadware/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/reloadware/reloadium\"\u003ereloadium\u003c/a\u003e\u003c/b\u003e ⭐ 2,996    \n   Hot Reloading and Profiling for Python  \n\n## Security\n\nSecurity related libraries: vulnerability discovery, SQL injection, environment auditing.  \n\n1. \u003ca href=\"https://github.com/swisskyrepo/payloadsallthethings\"\u003eswisskyrepo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/swisskyrepo/payloadsallthethings\"\u003ePayloadsAllTheThings\u003c/a\u003e\u003c/b\u003e ⭐ 74,615    \n   A list of useful payloads and bypass for Web Application Security and Pentest/CTF  \n   🔗 [swisskyrepo.github.io/payloadsallthethings](https://swisskyrepo.github.io/PayloadsAllTheThings/)  \n\n2. \u003ca href=\"https://github.com/sqlmapproject/sqlmap\"\u003esqlmapproject/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sqlmapproject/sqlmap\"\u003esqlmap\u003c/a\u003e\u003c/b\u003e ⭐ 36,374    \n   Automatic SQL injection and database takeover tool  \n   🔗 [sqlmap.org](http://sqlmap.org)  \n\n3. \u003ca href=\"https://github.com/certbot/certbot\"\u003ecertbot/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/certbot/certbot\"\u003ecertbot\u003c/a\u003e\u003c/b\u003e ⭐ 32,781    \n   Certbot is EFF's tool to obtain certs from Let's Encrypt and (optionally) auto-enable HTTPS on your server.  It can also act as a client for any other CA that uses the ACME protocol.  \n\n4. \u003ca href=\"https://github.com/aquasecurity/trivy\"\u003eaquasecurity/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aquasecurity/trivy\"\u003etrivy\u003c/a\u003e\u003c/b\u003e ⭐ 31,095    \n   Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more  \n   🔗 [trivy.dev](https://trivy.dev)  \n\n5. \u003ca href=\"https://github.com/bridgecrewio/checkov\"\u003ebridgecrewio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bridgecrewio/checkov\"\u003echeckov\u003c/a\u003e\u003c/b\u003e ⭐ 8,423    \n   Checkov is a static code analysis tool for infrastructure as code (IaC) and also a software composition analysis (SCA) tool for images and open source packages.  \n   🔗 [www.checkov.io](https://www.checkov.io/)  \n\n6. \u003ca href=\"https://github.com/stamparm/maltrail\"\u003estamparm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/stamparm/maltrail\"\u003emaltrail\u003c/a\u003e\u003c/b\u003e ⭐ 8,167    \n   Malicious traffic detection system  \n\n7. \u003ca href=\"https://github.com/pycqa/bandit\"\u003epycqa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycqa/bandit\"\u003ebandit\u003c/a\u003e\u003c/b\u003e ⭐ 7,688    \n   Bandit is a tool designed to find common security issues in Python code.  \n   🔗 [bandit.readthedocs.io](https://bandit.readthedocs.io)  \n\n8. \u003ca href=\"https://github.com/nccgroup/scoutsuite\"\u003enccgroup/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nccgroup/scoutsuite\"\u003eScoutSuite\u003c/a\u003e\u003c/b\u003e ⭐ 7,519    \n   Multi-Cloud Security Auditing Tool  \n\n9. \u003ca href=\"https://github.com/microsoft/presidio\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/presidio\"\u003epresidio\u003c/a\u003e\u003c/b\u003e ⭐ 6,716    \n   Context aware, pluggable and customizable PII de-identification service for text and images  \n   🔗 [microsoft.github.io/presidio](https://microsoft.github.io/presidio)  \n\n10. \u003ca href=\"https://github.com/rhinosecuritylabs/pacu\"\u003erhinosecuritylabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rhinosecuritylabs/pacu\"\u003epacu\u003c/a\u003e\u003c/b\u003e ⭐ 5,040    \n   The AWS exploitation framework, designed for testing the security of Amazon Web Services environments.  \n   🔗 [rhinosecuritylabs.com/aws/pacu-open-source-aws-exploitation-framework](https://rhinosecuritylabs.com/aws/pacu-open-source-aws-exploitation-framework/)  \n\n11. \u003ca href=\"https://github.com/dashingsoft/pyarmor\"\u003edashingsoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dashingsoft/pyarmor\"\u003epyarmor\u003c/a\u003e\u003c/b\u003e ⭐ 4,913    \n   A tool used to obfuscate python scripts, bind obfuscated scripts to fixed machine or expire obfuscated scripts.  \n   🔗 [pyarmor.dashingsoft.com](http://pyarmor.dashingsoft.com)  \n\n12. \u003ca href=\"https://github.com/fadi002/de4py\"\u003efadi002/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fadi002/de4py\"\u003ede4py\u003c/a\u003e\u003c/b\u003e ⭐ 950    \n   toolkit for python reverse engineering  \n\n## Simulation\n\nSimulation libraries: robotics, economic, agent-based, traffic, physics, astronomy, chemistry, quantum simulation. Also see the \u003ca href=\"https://github.com/dylanhogg/awesome-python#math-and-science\"\u003eMaths and Science\u003c/a\u003e category for crossover.  \n\n1. \u003ca href=\"https://github.com/atsushisakai/pythonrobotics\"\u003eatsushisakai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/atsushisakai/pythonrobotics\"\u003ePythonRobotics\u003c/a\u003e\u003c/b\u003e ⭐ 28,351    \n   Python sample codes and textbook for robotics algorithms.  \n   🔗 [atsushisakai.github.io/pythonrobotics](https://atsushisakai.github.io/PythonRobotics/)  \n\n2. \u003ca href=\"https://github.com/genesis-embodied-ai/genesis\"\u003egenesis-embodied-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/genesis-embodied-ai/genesis\"\u003eGenesis\u003c/a\u003e\u003c/b\u003e ⭐ 28,015    \n   Genesis is a physics platform, and generative data engine, designed for general purpose Robotics/Embodied AI/Physical AI applications  \n   🔗 [genesis-world.readthedocs.io](https://genesis-world.readthedocs.io)  \n\n3. \u003ca href=\"https://github.com/bulletphysics/bullet3\"\u003ebulletphysics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bulletphysics/bullet3\"\u003ebullet3\u003c/a\u003e\u003c/b\u003e ⭐ 14,183    \n   Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.  \n   🔗 [bulletphysics.org](http://bulletphysics.org)  \n\n4. \u003ca href=\"https://github.com/isl-org/open3d\"\u003eisl-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/isl-org/open3d\"\u003eOpen3D\u003c/a\u003e\u003c/b\u003e ⭐ 13,256    \n   Open3D: A Modern Library for 3D Data Processing  \n   🔗 [www.open3d.org](http://www.open3d.org)  \n\n5. \u003ca href=\"https://github.com/dlr-rm/stable-baselines3\"\u003edlr-rm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dlr-rm/stable-baselines3\"\u003estable-baselines3\u003c/a\u003e\u003c/b\u003e ⭐ 12,596    \n   Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch  \n   🔗 [stable-baselines3.readthedocs.io](https://stable-baselines3.readthedocs.io)  \n\n6. \u003ca href=\"https://github.com/nvidia/cosmos\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/cosmos\"\u003eCosmos\u003c/a\u003e\u003c/b\u003e ⭐ 8,085    \n   NVIDIA Cosmos is a developer-first world foundation model platform designed to help Physical AI developers build their Physical AI systems better and faster.  \n   🔗 [github.com/nvidia-cosmos](https://github.com/nvidia-cosmos)  \n\n7. \u003ca href=\"https://github.com/qiskit/qiskit\"\u003eqiskit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/qiskit/qiskit\"\u003eqiskit\u003c/a\u003e\u003c/b\u003e ⭐ 6,956    \n   Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.  \n   🔗 [www.ibm.com/quantum/qiskit](https://www.ibm.com/quantum/qiskit)  \n\n8. \u003ca href=\"https://github.com/nvidia/warp\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/warp\"\u003ewarp\u003c/a\u003e\u003c/b\u003e ⭐ 6,138    \n   A Python framework for accelerated simulation, data generation and spatial computing.  \n   🔗 [nvidia.github.io/warp](https://nvidia.github.io/warp/)  \n\n9. \u003ca href=\"https://github.com/nvidia-omniverse/orbit\"\u003envidia-omniverse/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia-omniverse/orbit\"\u003eIsaacLab\u003c/a\u003e\u003c/b\u003e ⭐ 6,136    \n   Unified framework for robot learning built on NVIDIA Isaac Sim  \n   🔗 [isaac-sim.github.io/isaaclab](https://isaac-sim.github.io/IsaacLab)  \n\n10. \u003ca href=\"https://github.com/astropy/astropy\"\u003eastropy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/astropy/astropy\"\u003eastropy\u003c/a\u003e\u003c/b\u003e ⭐ 5,012    \n   Astronomy and astrophysics core library  \n   🔗 [www.astropy.org](https://www.astropy.org)  \n\n11. \u003ca href=\"https://github.com/quantumlib/cirq\"\u003equantumlib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/quantumlib/cirq\"\u003eCirq\u003c/a\u003e\u003c/b\u003e ⭐ 4,851    \n   Python framework for creating, editing, and running Noisy Intermediate-Scale Quantum (NISQ) circuits.  \n   🔗 [quantumai.google/cirq](https://quantumai.google/cirq)  \n\n12. \u003ca href=\"https://github.com/chakazul/lenia\"\u003echakazul/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chakazul/lenia\"\u003eLenia\u003c/a\u003e\u003c/b\u003e ⭐ 3,728    \n   Lenia is a 2D cellular automata with continuous space, time and states. It produces a huge variety of interesting methematical life forms  \n   🔗 [chakazul.github.io/lenia/javascript/lenia.html](https://chakazul.github.io/Lenia/JavaScript/Lenia.html)  \n\n13. \u003ca href=\"https://github.com/openai/mujoco-py\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/mujoco-py\"\u003emujoco-py\u003c/a\u003e\u003c/b\u003e ⭐ 3,105    \n   MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.  \n\n14. \u003ca href=\"https://github.com/pennylaneai/pennylane\"\u003epennylaneai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pennylaneai/pennylane\"\u003epennylane\u003c/a\u003e\u003c/b\u003e ⭐ 3,037    \n   PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research.   \n   🔗 [pennylane.ai](https://pennylane.ai)  \n\n15. \u003ca href=\"https://github.com/google/brax\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/brax\"\u003ebrax\u003c/a\u003e\u003c/b\u003e ⭐ 3,033    \n   Massively parallel rigidbody physics simulation on accelerator hardware.  \n\n16. \u003ca href=\"https://github.com/nvidia-omniverse/isaacgymenvs\"\u003envidia-omniverse/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia-omniverse/isaacgymenvs\"\u003eIsaacGymEnvs\u003c/a\u003e\u003c/b\u003e ⭐ 2,817    \n   Example RL environments for the NVIDIA Isaac Gym high performance environments  \n\n17. \u003ca href=\"https://github.com/facebookresearch/habitat-lab\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/habitat-lab\"\u003ehabitat-lab\u003c/a\u003e\u003c/b\u003e ⭐ 2,807    \n   A modular high-level library to train embodied AI agents across a variety of tasks and environments.  \n   🔗 [aihabitat.org](https://aihabitat.org/)  \n\n18. \u003ca href=\"https://github.com/tencent-hunyuan/hunyuan3d-2.1\"\u003etencent-hunyuan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tencent-hunyuan/hunyuan3d-2.1\"\u003eHunyuan3D-2.1\u003c/a\u003e\u003c/b\u003e ⭐ 2,788    \n   Tencent Hunyuan3D-2.1 is a scalable 3D asset creation system that advances state-of-the-art 3D generation  \n   🔗 [3d.hunyuan.tencent.com](https://3d.hunyuan.tencent.com/)  \n\n19. \u003ca href=\"https://github.com/taichi-dev/difftaichi\"\u003etaichi-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/taichi-dev/difftaichi\"\u003edifftaichi\u003c/a\u003e\u003c/b\u003e ⭐ 2,707    \n   10 differentiable physical simulators built with Taichi differentiable programming (DiffTaichi, ICLR 2020)  \n\n20. \u003ca href=\"https://github.com/dlr-rm/rl-baselines3-zoo\"\u003edlr-rm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dlr-rm/rl-baselines3-zoo\"\u003erl-baselines3-zoo\u003c/a\u003e\u003c/b\u003e ⭐ 2,691    \n   A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.  \n   🔗 [rl-baselines3-zoo.readthedocs.io](https://rl-baselines3-zoo.readthedocs.io)  \n\n21. \u003ca href=\"https://github.com/isaac-sim/isaacsim\"\u003eisaac-sim/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/isaac-sim/isaacsim\"\u003eIsaacSim\u003c/a\u003e\u003c/b\u003e ⭐ 2,407    \n   NVIDIA Isaac Sim is a simulation platform built on NVIDIA Omniverse, designed to develop, test, train, and deploy AI-powered robots in realistic virtual environments.  \n   🔗 [developer.nvidia.com/isaac/sim](https://developer.nvidia.com/isaac/sim)  \n\n22. \u003ca href=\"https://github.com/microsoft/promptcraft-robotics\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/promptcraft-robotics\"\u003ePromptCraft-Robotics\u003c/a\u003e\u003c/b\u003e ⭐ 2,084    \n   Community for applying LLMs to robotics and a robot simulator with ChatGPT integration  \n   🔗 [aka.ms/chatgpt-robotics](https://aka.ms/ChatGPT-Robotics)  \n\n23. \u003ca href=\"https://github.com/eloialonso/diamond\"\u003eeloialonso/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eloialonso/diamond\"\u003ediamond\u003c/a\u003e\u003c/b\u003e ⭐ 1,952    \n   DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model  \n   🔗 [diamond-wm.github.io](https://diamond-wm.github.io)  \n\n24. \u003ca href=\"https://github.com/polymathicai/the_well\"\u003epolymathicai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/polymathicai/the_well\"\u003ethe_well\u003c/a\u003e\u003c/b\u003e ⭐ 1,655    \n   15TB of Physics Simulations: collection of machine learning datasets containing numerical simulations of a wide variety of spatiotemporal physical systems.  \n   🔗 [polymathic-ai.org/the_well](https://polymathic-ai.org/the_well/)  \n\n25. \u003ca href=\"https://github.com/bowang-lab/scgpt\"\u003ebowang-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bowang-lab/scgpt\"\u003escGPT\u003c/a\u003e\u003c/b\u003e ⭐ 1,446    \n   scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI  \n   🔗 [scgpt.readthedocs.io/en/latest](https://scgpt.readthedocs.io/en/latest/)  \n\n26. \u003ca href=\"https://github.com/altera-al/project-sid\"\u003ealtera-al/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/altera-al/project-sid\"\u003eproject-sid\u003c/a\u003e\u003c/b\u003e ⭐ 1,168    \n   Project Sid: Many-agent simulations toward AI civilization technical report  \n\n27. \u003ca href=\"https://github.com/google-deepmind/materials_discovery\"\u003egoogle-deepmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-deepmind/materials_discovery\"\u003ematerials_discovery\u003c/a\u003e\u003c/b\u003e ⭐ 1,114    \n   Graph Networks for Materials Science (GNoME) is a project centered around scaling machine learning methods to tackle materials science.  \n\n28. \u003ca href=\"https://github.com/viblo/pymunk\"\u003eviblo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/viblo/pymunk\"\u003epymunk\u003c/a\u003e\u003c/b\u003e ⭐ 1,038    \n   Pymunk is a easy-to-use pythonic 2d physics library that can be used whenever  you need 2d rigid body physics from Python  \n   🔗 [www.pymunk.org](http://www.pymunk.org)  \n\n29. \u003ca href=\"https://github.com/eureka-research/dreureka\"\u003eeureka-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eureka-research/dreureka\"\u003eDrEureka\u003c/a\u003e\u003c/b\u003e ⭐ 915    \n   Official Repository for \"DrEureka: Language Model Guided Sim-To-Real Transfer\" (RSS 2024)  \n   🔗 [eureka-research.github.io/dr-eureka](https://eureka-research.github.io/dr-eureka/)  \n\n30. \u003ca href=\"https://github.com/ur-whitelab/chemcrow-public\"\u003eur-whitelab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ur-whitelab/chemcrow-public\"\u003echemcrow-public\u003c/a\u003e\u003c/b\u003e ⭐ 868    \n   Chemcrow  \n\n31. \u003ca href=\"https://github.com/vandijklab/cell2sentence\"\u003evandijklab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vandijklab/cell2sentence\"\u003ecell2sentence\u003c/a\u003e\u003c/b\u003e ⭐ 801    \n   Cell2Sentence (C2S-Scale) framework for applying Large Language Models (LLMs) to single-cell transcriptomics.  \n\n32. \u003ca href=\"https://github.com/sakanaai/shinkaevolve\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/shinkaevolve\"\u003eShinkaEvolve\u003c/a\u003e\u003c/b\u003e ⭐ 799    \n   A framework that combines LLMs with evolutionary algorithms to drive scientific discovery. Leveraging creative capabilities of LLMs and the optimization power of evolutionary search, enables automated exploration and improvement of scientific code.  \n\n33. \u003ca href=\"https://github.com/sakanaai/asal\"\u003esakanaai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sakanaai/asal\"\u003easal\u003c/a\u003e\u003c/b\u003e ⭐ 449    \n   Automating the Search for Artificial Life with Foundation Models!  \n   🔗 [pub.sakana.ai/asal](https://pub.sakana.ai/asal/)  \n\n34. \u003ca href=\"https://github.com/arshka/physix\"\u003earshka/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arshka/physix\"\u003ePhysiX\u003c/a\u003e\u003c/b\u003e ⭐ 110    \n   A Foundation Model for physics simulations  \n\n35. \u003ca href=\"https://github.com/ur-whitelab/chemcrow-runs\"\u003eur-whitelab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ur-whitelab/chemcrow-runs\"\u003echemcrow-runs\u003c/a\u003e\u003c/b\u003e ⭐ 94    \n   ur-whitelab/chemcrow-runs  \n\n## Study\n\nMiscellaneous study resources: algorithms, general resources, system design, code repos for textbooks, best practices, tutorials.  \n\n1. \u003ca href=\"https://github.com/thealgorithms/python\"\u003ethealgorithms/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/thealgorithms/python\"\u003ePython\u003c/a\u003e\u003c/b\u003e ⭐ 217,119    \n   All Algorithms implemented in Python  \n   🔗 [thealgorithms.github.io/python](https://thealgorithms.github.io/Python/)  \n\n2. \u003ca href=\"https://github.com/microsoft/generative-ai-for-beginners\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/generative-ai-for-beginners\"\u003egenerative-ai-for-beginners\u003c/a\u003e\u003c/b\u003e ⭐ 105,570    \n   Learn the fundamentals of building Generative AI applications with our 21-lesson comprehensive course by Microsoft Cloud Advocates.  \n\n3. \u003ca href=\"https://github.com/rasbt/llms-from-scratch\"\u003erasbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasbt/llms-from-scratch\"\u003eLLMs-from-scratch\u003c/a\u003e\u003c/b\u003e ⭐ 83,698    \n   Implement a ChatGPT-like LLM in PyTorch from scratch, step by step  \n   🔗 [amzn.to/4fqvn0d](https://amzn.to/4fqvn0D)  \n\n4. \u003ca href=\"https://github.com/mlabonne/llm-course\"\u003emlabonne/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mlabonne/llm-course\"\u003ellm-course\u003c/a\u003e\u003c/b\u003e ⭐ 73,812    \n   Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.  \n   🔗 [mlabonne.github.io/blog](https://mlabonne.github.io/blog/)  \n\n5. \u003ca href=\"https://github.com/labmlai/annotated_deep_learning_paper_implementations\"\u003elabmlai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/labmlai/annotated_deep_learning_paper_implementations\"\u003eannotated_deep_learning_paper_implementations\u003c/a\u003e\u003c/b\u003e ⭐ 65,496    \n   🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠  \n   🔗 [nn.labml.ai](https://nn.labml.ai)  \n\n6. \u003ca href=\"https://github.com/jakevdp/pythondatasciencehandbook\"\u003ejakevdp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jakevdp/pythondatasciencehandbook\"\u003ePythonDataScienceHandbook\u003c/a\u003e\u003c/b\u003e ⭐ 46,573    \n   Python Data Science Handbook: full text in Jupyter Notebooks  \n   🔗 [jakevdp.github.io/pythondatasciencehandbook](http://jakevdp.github.io/PythonDataScienceHandbook)  \n\n7. \u003ca href=\"https://github.com/realpython/python-guide\"\u003erealpython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/realpython/python-guide\"\u003epython-guide\u003c/a\u003e\u003c/b\u003e ⭐ 29,471    \n   Python best practices guidebook, written for humans.   \n   🔗 [docs.python-guide.org](https://docs.python-guide.org)  \n\n8. \u003ca href=\"https://github.com/d2l-ai/d2l-en\"\u003ed2l-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/d2l-ai/d2l-en\"\u003ed2l-en\u003c/a\u003e\u003c/b\u003e ⭐ 28,013    \n   Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.  \n   🔗 [d2l.ai](https://D2L.ai)  \n\n9. \u003ca href=\"https://github.com/christoschristofidis/awesome-deep-learning\"\u003echristoschristofidis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/christoschristofidis/awesome-deep-learning\"\u003eawesome-deep-learning\u003c/a\u003e\u003c/b\u003e ⭐ 27,358    \n   A curated list of awesome Deep Learning tutorials, projects and communities.  \n\n10. \u003ca href=\"https://github.com/hannibal046/awesome-llm\"\u003ehannibal046/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hannibal046/awesome-llm\"\u003eAwesome-LLM\u003c/a\u003e\u003c/b\u003e ⭐ 26,079    \n   Awesome-LLM: a curated list of Large Language Model  \n\n11. \u003ca href=\"https://github.com/huggingface/agents-course\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/agents-course\"\u003eagents-course\u003c/a\u003e\u003c/b\u003e ⭐ 24,945    \n   This repository contains the Hugging Face Agents Course.   \n\n12. \u003ca href=\"https://github.com/wesm/pydata-book\"\u003ewesm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wesm/pydata-book\"\u003epydata-book\u003c/a\u003e\u003c/b\u003e ⭐ 24,209    \n   Materials and IPython notebooks for \"Python for Data Analysis\" by Wes McKinney, published by O'Reilly Media  \n\n13. \u003ca href=\"https://github.com/affaan-m/everything-claude-code\"\u003eaffaan-m/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/affaan-m/everything-claude-code\"\u003eeverything-claude-code\u003c/a\u003e\u003c/b\u003e ⭐ 23,150    \n   Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner.  \n\n14. \u003ca href=\"https://github.com/microsoft/recommenders\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/recommenders\"\u003erecommenders\u003c/a\u003e\u003c/b\u003e ⭐ 21,384    \n   Best Practices on Recommendation Systems  \n   🔗 [recommenders-team.github.io/recommenders/intro.html](https://recommenders-team.github.io/recommenders/intro.html)  \n\n15. \u003ca href=\"https://github.com/karpathy/nn-zero-to-hero\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/nn-zero-to-hero\"\u003enn-zero-to-hero\u003c/a\u003e\u003c/b\u003e ⭐ 20,041    \n   Neural Networks: Zero to Hero  \n\n16. \u003ca href=\"https://github.com/handsonllm/hands-on-large-language-models\"\u003ehandsonllm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/handsonllm/hands-on-large-language-models\"\u003eHands-On-Large-Language-Models\u003c/a\u003e\u003c/b\u003e ⭐ 19,993    \n   Official code repo for the O'Reilly Book - \"Hands-On Large Language Models\"  \n   🔗 [www.llm-book.com](https://www.llm-book.com/)  \n\n17. \u003ca href=\"https://github.com/fchollet/deep-learning-with-python-notebooks\"\u003efchollet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fchollet/deep-learning-with-python-notebooks\"\u003edeep-learning-with-python-notebooks\u003c/a\u003e\u003c/b\u003e ⭐ 19,876    \n   Jupyter notebooks for the code samples of the book \"Deep Learning with Python\"  \n\n18. \u003ca href=\"https://github.com/nirdiamant/agents-towards-production\"\u003enirdiamant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nirdiamant/agents-towards-production\"\u003eagents-towards-production\u003c/a\u003e\u003c/b\u003e ⭐ 16,944    \n   The open-source playbook for turning AI agents into real-world products.  \n\n19. \u003ca href=\"https://github.com/mrdbourke/pytorch-deep-learning\"\u003emrdbourke/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mrdbourke/pytorch-deep-learning\"\u003epytorch-deep-learning\u003c/a\u003e\u003c/b\u003e ⭐ 16,921    \n   Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.  \n   🔗 [learnpytorch.io](https://learnpytorch.io)  \n\n20. \u003ca href=\"https://github.com/zhanymkanov/fastapi-best-practices\"\u003ezhanymkanov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zhanymkanov/fastapi-best-practices\"\u003efastapi-best-practices\u003c/a\u003e\u003c/b\u003e ⭐ 16,156    \n   FastAPI Best Practices and Conventions we used at our startup  \n\n21. \u003ca href=\"https://github.com/naklecha/llama3-from-scratch\"\u003enaklecha/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/naklecha/llama3-from-scratch\"\u003ellama3-from-scratch\u003c/a\u003e\u003c/b\u003e ⭐ 15,243    \n   llama3 implementation one matrix multiplication at a time  \n\n22. \u003ca href=\"https://github.com/graykode/nlp-tutorial\"\u003egraykode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/graykode/nlp-tutorial\"\u003enlp-tutorial\u003c/a\u003e\u003c/b\u003e ⭐ 14,840    \n   Natural Language Processing Tutorial for Deep Learning Researchers  \n   🔗 [www.reddit.com/r/machinelearning/comments/amfinl/project_nlptutoral_repository_who_is_studying](https://www.reddit.com/r/MachineLearning/comments/amfinl/project_nlptutoral_repository_who_is_studying/)  \n\n23. \u003ca href=\"https://github.com/shangtongzhang/reinforcement-learning-an-introduction\"\u003eshangtongzhang/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shangtongzhang/reinforcement-learning-an-introduction\"\u003ereinforcement-learning-an-introduction\u003c/a\u003e\u003c/b\u003e ⭐ 14,515    \n   Python Implementation of Reinforcement Learning: An Introduction  \n\n24. \u003ca href=\"https://github.com/karpathy/micrograd\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/micrograd\"\u003emicrograd\u003c/a\u003e\u003c/b\u003e ⭐ 14,428    \n   A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API  \n\n25. \u003ca href=\"https://github.com/chiphuyen/aie-book\"\u003echiphuyen/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chiphuyen/aie-book\"\u003eaie-book\u003c/a\u003e\u003c/b\u003e ⭐ 13,206    \n   Code for AI Engineering: Building Applications with Foundation Models (Chip Huyen 2025)  \n\n26. \u003ca href=\"https://github.com/eugeneyan/open-llms\"\u003eeugeneyan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eugeneyan/open-llms\"\u003eopen-llms\u003c/a\u003e\u003c/b\u003e ⭐ 12,602    \n   📋 A list of open LLMs available for commercial use.  \n\n27. \u003ca href=\"https://github.com/rucaibox/llmsurvey\"\u003erucaibox/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rucaibox/llmsurvey\"\u003eLLMSurvey\u003c/a\u003e\u003c/b\u003e ⭐ 12,070    \n   The official GitHub page for the survey paper \"A Survey of Large Language Models\".  \n   🔗 [arxiv.org/abs/2303.18223](https://arxiv.org/abs/2303.18223)  \n\n28. \u003ca href=\"https://github.com/srush/gpu-puzzles\"\u003esrush/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/srush/gpu-puzzles\"\u003eGPU-Puzzles\u003c/a\u003e\u003c/b\u003e ⭐ 11,902    \n   Teaching beginner GPU programming in a completely interactive fashion  \n\n29. \u003ca href=\"https://github.com/openai/spinningup\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/spinningup\"\u003espinningup\u003c/a\u003e\u003c/b\u003e ⭐ 11,548    \n   An educational resource to help anyone learn deep reinforcement learning.  \n   🔗 [spinningup.openai.com](https://spinningup.openai.com/)  \n\n30. \u003ca href=\"https://github.com/nielsrogge/transformers-tutorials\"\u003enielsrogge/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nielsrogge/transformers-tutorials\"\u003eTransformers-Tutorials\u003c/a\u003e\u003c/b\u003e ⭐ 11,479    \n   This repository contains demos I made with the Transformers library by HuggingFace.  \n\n31. \u003ca href=\"https://github.com/mooler0410/llmspracticalguide\"\u003emooler0410/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mooler0410/llmspracticalguide\"\u003eLLMsPracticalGuide\u003c/a\u003e\u003c/b\u003e ⭐ 10,142    \n   A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)  \n   🔗 [arxiv.org/abs/2304.13712v2](https://arxiv.org/abs/2304.13712v2)  \n\n32. \u003ca href=\"https://github.com/kalyanks-nlp/llm-engineer-toolkit\"\u003ekalyanks-nlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kalyanks-nlp/llm-engineer-toolkit\"\u003ellm-engineer-toolkit\u003c/a\u003e\u003c/b\u003e ⭐ 9,688    \n   A curated list of  120+ LLM libraries category wise.   \n   🔗 [www.linkedin.com/in/kalyanksnlp](https://www.linkedin.com/in/kalyanksnlp/)  \n\n33. \u003ca href=\"https://github.com/roboflow/notebooks\"\u003eroboflow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/roboflow/notebooks\"\u003enotebooks\u003c/a\u003e\u003c/b\u003e ⭐ 9,117    \n   A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.  \n   🔗 [roboflow.com/models](https://roboflow.com/models)  \n\n34. \u003ca href=\"https://github.com/udlbook/udlbook\"\u003eudlbook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/udlbook/udlbook\"\u003eudlbook\u003c/a\u003e\u003c/b\u003e ⭐ 8,940    \n   Understanding Deep Learning - Simon J.D. Prince  \n\n35. \u003ca href=\"https://github.com/engineer1999/a-curated-list-of-ml-system-design-case-studies\"\u003eengineer1999/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/engineer1999/a-curated-list-of-ml-system-design-case-studies\"\u003eA-Curated-List-of-ML-System-Design-Case-Studies\u003c/a\u003e\u003c/b\u003e ⭐ 8,205    \n   Curated collection of 300+ case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design  \n\n36. \u003ca href=\"https://github.com/alirezadir/machine-learning-interview-enlightener\"\u003ealirezadir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alirezadir/machine-learning-interview-enlightener\"\u003eMachine-Learning-Interviews\u003c/a\u003e\u003c/b\u003e ⭐ 7,644    \n   This repo is meant to serve as a guide for Machine Learning/AI technical interviews.   \n\n37. \u003ca href=\"https://github.com/firmai/industry-machine-learning\"\u003efirmai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/firmai/industry-machine-learning\"\u003eindustry-machine-learning\u003c/a\u003e\u003c/b\u003e ⭐ 7,442    \n   A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)  \n   🔗 [www.sov.ai](https://www.sov.ai/)  \n\n38. \u003ca href=\"https://github.com/gkamradt/langchain-tutorials\"\u003egkamradt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gkamradt/langchain-tutorials\"\u003elangchain-tutorials\u003c/a\u003e\u003c/b\u003e ⭐ 7,366    \n   Overview and tutorial of the LangChain Library  \n\n39. \u003ca href=\"https://github.com/huggingface/smol-course\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/smol-course\"\u003esmol-course\u003c/a\u003e\u003c/b\u003e ⭐ 6,578    \n   a practical course on aligning language models for your specific use case. It's a handy way to get started with aligning language models, because everything runs on most local machines.  \n\n40. \u003ca href=\"https://github.com/neetcode-gh/leetcode\"\u003eneetcode-gh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neetcode-gh/leetcode\"\u003eleetcode\u003c/a\u003e\u003c/b\u003e ⭐ 6,258    \n   Leetcode solutions for NeetCode.io  \n\n41. \u003ca href=\"https://github.com/mrdbourke/tensorflow-deep-learning\"\u003emrdbourke/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mrdbourke/tensorflow-deep-learning\"\u003etensorflow-deep-learning\u003c/a\u003e\u003c/b\u003e ⭐ 5,831    \n   All course materials for the Zero to Mastery Deep Learning with TensorFlow course.  \n   🔗 [dbourke.link/ztmtfcourse](https://dbourke.link/ZTMTFcourse)  \n\n42. \u003ca href=\"https://github.com/udacity/deep-learning-v2-pytorch\"\u003eudacity/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/udacity/deep-learning-v2-pytorch\"\u003edeep-learning-v2-pytorch\u003c/a\u003e\u003c/b\u003e ⭐ 5,469    \n   Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101  \n\n43. \u003ca href=\"https://github.com/promptslab/awesome-prompt-engineering\"\u003epromptslab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/promptslab/awesome-prompt-engineering\"\u003eAwesome-Prompt-Engineering\u003c/a\u003e\u003c/b\u003e ⭐ 5,296    \n   This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc   \n   🔗 [discord.gg/m88xfymbk6](https://discord.gg/m88xfYMbK6)  \n\n44. \u003ca href=\"https://github.com/timofurrer/awesome-asyncio\"\u003etimofurrer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/timofurrer/awesome-asyncio\"\u003eawesome-asyncio\u003c/a\u003e\u003c/b\u003e ⭐ 4,987    \n   A curated list of awesome Python asyncio frameworks, libraries, software and resources  \n\n45. \u003ca href=\"https://github.com/rasbt/machine-learning-book\"\u003erasbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasbt/machine-learning-book\"\u003emachine-learning-book\u003c/a\u003e\u003c/b\u003e ⭐ 4,943    \n   Code Repository for Machine Learning with PyTorch and Scikit-Learn  \n   🔗 [sebastianraschka.com/books/#machine-learning-with-pytorch-and-scikit-learn](https://sebastianraschka.com/books/#machine-learning-with-pytorch-and-scikit-learn)  \n\n46. \u003ca href=\"https://github.com/huggingface/deep-rl-class\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/deep-rl-class\"\u003edeep-rl-class\u003c/a\u003e\u003c/b\u003e ⭐ 4,719    \n   This repo contains the Hugging Face Deep Reinforcement Learning Course.  \n\n47. \u003ca href=\"https://github.com/zotroneneis/machine_learning_basics\"\u003ezotroneneis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zotroneneis/machine_learning_basics\"\u003emachine_learning_basics\u003c/a\u003e\u003c/b\u003e ⭐ 4,402    \n   Plain python implementations of basic machine learning algorithms  \n\n48. \u003ca href=\"https://github.com/huggingface/diffusion-models-class\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/diffusion-models-class\"\u003ediffusion-models-class\u003c/a\u003e\u003c/b\u003e ⭐ 4,263    \n   Materials for the Hugging Face Diffusion Models Course  \n\n49. \u003ca href=\"https://github.com/amanchadha/coursera-deep-learning-specialization\"\u003eamanchadha/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/amanchadha/coursera-deep-learning-specialization\"\u003ecoursera-deep-learning-specialization\u003c/a\u003e\u003c/b\u003e ⭐ 4,149    \n   Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv...  \n\n50. \u003ca href=\"https://github.com/fluentpython/example-code-2e\"\u003efluentpython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fluentpython/example-code-2e\"\u003eexample-code-2e\u003c/a\u003e\u003c/b\u003e ⭐ 3,928    \n   Example code for Fluent Python, 2nd edition (O'Reilly 2022)   \n   🔗 [amzn.to/3j48u2j](https://amzn.to/3J48u2J)  \n\n51. \u003ca href=\"https://github.com/cosmicpython/book\"\u003ecosmicpython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cosmicpython/book\"\u003ebook\u003c/a\u003e\u003c/b\u003e ⭐ 3,708    \n   A Book about Pythonic Application Architecture Patterns for Managing Complexity.  Cosmos is the Opposite of Chaos you see. O'R. wouldn't actually let us call it \"Cosmic Python\" tho.  \n   🔗 [www.cosmicpython.com](https://www.cosmicpython.com)  \n\n52. \u003ca href=\"https://github.com/mrdbourke/zero-to-mastery-ml\"\u003emrdbourke/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mrdbourke/zero-to-mastery-ml\"\u003ezero-to-mastery-ml\u003c/a\u003e\u003c/b\u003e ⭐ 3,583    \n   All course materials for the Zero to Mastery Machine Learning and Data Science course.  \n   🔗 [dbourke.link/ztmmlcourse](https://dbourke.link/ZTMmlcourse)  \n\n53. \u003ca href=\"https://github.com/krzjoa/awesome-python-data-science\"\u003ekrzjoa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/krzjoa/awesome-python-data-science\"\u003eawesome-python-data-science\u003c/a\u003e\u003c/b\u003e ⭐ 3,318    \n   Probably the best curated list of data science software in Python.  \n   🔗 [krzjoa.github.io/awesome-python-data-science](https://krzjoa.github.io/awesome-python-data-science)  \n\n54. \u003ca href=\"https://github.com/huggingface/cookbook\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/cookbook\"\u003ecookbook\u003c/a\u003e\u003c/b\u003e ⭐ 2,580    \n   Community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models.  \n   🔗 [huggingface.co/learn/cookbook](https://huggingface.co/learn/cookbook)  \n\n55. \u003ca href=\"https://github.com/gerdm/prml\"\u003egerdm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gerdm/prml\"\u003eprml\u003c/a\u003e\u003c/b\u003e ⭐ 2,532    \n   Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop  \n\n56. \u003ca href=\"https://github.com/cerlymarco/medium_notebook\"\u003ecerlymarco/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cerlymarco/medium_notebook\"\u003eMEDIUM_NoteBook\u003c/a\u003e\u003c/b\u003e ⭐ 2,141    \n   Repository containing notebooks of my posts on Medium  \n\n57. \u003ca href=\"https://github.com/aburkov/thelmbook\"\u003eaburkov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aburkov/thelmbook\"\u003etheLMbook\u003c/a\u003e\u003c/b\u003e ⭐ 2,080    \n   Code for Hundred-Page Language Models Book by Andriy Burkov  \n   🔗 [www.thelmbook.com](https://www.thelmbook.com)  \n\n58. \u003ca href=\"https://github.com/huggingface/evaluation-guidebook\"\u003ehuggingface/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huggingface/evaluation-guidebook\"\u003eevaluation-guidebook\u003c/a\u003e\u003c/b\u003e ⭐ 2,040    \n   Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!  \n\n59. \u003ca href=\"https://github.com/atcold/nyu-dlsp21\"\u003eatcold/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/atcold/nyu-dlsp21\"\u003eNYU-DLSP21\u003c/a\u003e\u003c/b\u003e ⭐ 1,651    \n   NYU Deep Learning Spring 2021  \n   🔗 [atcold.github.io/nyu-dlsp21](https://atcold.github.io/NYU-DLSP21/)  \n\n60. \u003ca href=\"https://github.com/davidadsp/generative_deep_learning_2nd_edition\"\u003edavidadsp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/davidadsp/generative_deep_learning_2nd_edition\"\u003eGenerative_Deep_Learning_2nd_Edition\u003c/a\u003e\u003c/b\u003e ⭐ 1,443    \n   The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.  \n   🔗 [www.oreilly.com/library/view/generative-deep-learning/9781098134174](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/)  \n\n61. \u003ca href=\"https://github.com/rasbt/llm-workshop-2024\"\u003erasbt/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rasbt/llm-workshop-2024\"\u003eLLM-workshop-2024\u003c/a\u003e\u003c/b\u003e ⭐ 1,061    \n   A 4-hour coding workshop to understand how LLMs are implemented and used  \n\n62. \u003ca href=\"https://github.com/cfregly/ai-performance-engineering\"\u003ecfregly/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cfregly/ai-performance-engineering\"\u003eai-performance-engineering\u003c/a\u003e\u003c/b\u003e ⭐ 973    \n   AI Systems Performance Engineering code and resources for the O'Reilly book covering GPU optimization, distributed training, inference scaling  \n\n63. \u003ca href=\"https://github.com/dylanhogg/awesome-python\"\u003edylanhogg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dylanhogg/awesome-python\"\u003eawesome-python\u003c/a\u003e\u003c/b\u003e ⭐ 439    \n   🐍 Hand-picked awesome Python libraries and frameworks, organised by category  \n   🔗 [www.awesomepython.org](https://www.awesomepython.org)  \n\n## Template\n\nTemplate tools and libraries: cookiecutter repos, generators, quick-starts.  \n\n1. \u003ca href=\"https://github.com/tiangolo/full-stack-fastapi-template\"\u003etiangolo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tiangolo/full-stack-fastapi-template\"\u003efull-stack-fastapi-template\u003c/a\u003e\u003c/b\u003e ⭐ 41,003    \n   Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.  \n\n2. \u003ca href=\"https://github.com/cookiecutter/cookiecutter\"\u003ecookiecutter/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cookiecutter/cookiecutter\"\u003ecookiecutter\u003c/a\u003e\u003c/b\u003e ⭐ 24,573    \n   A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.  \n   🔗 [pypi.org/project/cookiecutter](https://pypi.org/project/cookiecutter/)  \n\n3. \u003ca href=\"https://github.com/drivendata/cookiecutter-data-science\"\u003edrivendata/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/drivendata/cookiecutter-data-science\"\u003ecookiecutter-data-science\u003c/a\u003e\u003c/b\u003e ⭐ 9,620    \n   A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.  \n   🔗 [cookiecutter-data-science.drivendata.org](https://cookiecutter-data-science.drivendata.org/)  \n\n4. \u003ca href=\"https://github.com/buuntu/fastapi-react\"\u003ebuuntu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/buuntu/fastapi-react\"\u003efastapi-react\u003c/a\u003e\u003c/b\u003e ⭐ 2,570    \n   🚀   Cookiecutter Template for FastAPI + React Projects.  Using PostgreSQL, SQLAlchemy, and Docker  \n\n5. \u003ca href=\"https://github.com/cjolowicz/cookiecutter-hypermodern-python\"\u003ecjolowicz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cjolowicz/cookiecutter-hypermodern-python\"\u003ecookiecutter-hypermodern-python\u003c/a\u003e\u003c/b\u003e ⭐ 1,902    \n   Cookiecutter template for a Python package based on the Hypermodern Python article series.  \n   🔗 [cookiecutter-hypermodern-python.readthedocs.io](http://cookiecutter-hypermodern-python.readthedocs.io/)  \n\n6. \u003ca href=\"https://github.com/fmind/mlops-python-package\"\u003efmind/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fmind/mlops-python-package\"\u003emlops-python-package\u003c/a\u003e\u003c/b\u003e ⭐ 1,385    \n   Best practices designed to support your MLOPs initiatives. You can use this package as part of your MLOps toolkit or platform e.g. Model Registry, Experiment Tracking, Realtime Inference  \n   🔗 [fmind.github.io/mlops-python-package](https://fmind.github.io/mlops-python-package/)  \n\n7. \u003ca href=\"https://github.com/fpgmaas/cookiecutter-uv\"\u003efpgmaas/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fpgmaas/cookiecutter-uv\"\u003ecookiecutter-uv\u003c/a\u003e\u003c/b\u003e ⭐ 1,223    \n   A modern cookiecutter template for Python projects that use uv for dependency management   \n   🔗 [fpgmaas.github.io/cookiecutter-uv](https://fpgmaas.github.io/cookiecutter-uv)  \n\n8. \u003ca href=\"https://github.com/tezromach/python-package-template\"\u003etezromach/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tezromach/python-package-template\"\u003epython-package-template\u003c/a\u003e\u003c/b\u003e ⭐ 1,096    \n   🚀 Your next Python package needs a bleeding-edge project structure.  \n\n9. \u003ca href=\"https://github.com/callmesora/llmops-python-package\"\u003ecallmesora/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/callmesora/llmops-python-package\"\u003ellmops-python-package\u003c/a\u003e\u003c/b\u003e ⭐ 887    \n   Best practices designed to support your LLMOps initiatives. You can use this package as part of your LLMOps toolkit or platform e.g. Model Registry, Experiment Tracking, Realtime Inference  \n\n## Terminal\n\nTerminal and console tools and libraries: CLI tools, terminal based formatters, progress bars.  \n\n1. \u003ca href=\"https://github.com/anthropics/claude-code\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/claude-code\"\u003eclaude-code\u003c/a\u003e\u003c/b\u003e ⭐ 60,052    \n   Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows  \n   🔗 [code.claude.com/docs/en/overview](https://code.claude.com/docs/en/overview)  \n\n2. \u003ca href=\"https://github.com/willmcgugan/rich\"\u003ewillmcgugan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/willmcgugan/rich\"\u003erich\u003c/a\u003e\u003c/b\u003e ⭐ 55,226    \n   Rich is a Python library for rich text and beautiful formatting in the terminal.  \n   🔗 [rich.readthedocs.io/en/latest](https://rich.readthedocs.io/en/latest/)  \n\n3. \u003ca href=\"https://github.com/aider-ai/aider\"\u003eaider-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aider-ai/aider\"\u003eaider\u003c/a\u003e\u003c/b\u003e ⭐ 40,036    \n   Aider lets you pair program with LLMs, to edit code in your local git repository  \n   🔗 [aider.chat](https://aider.chat/)  \n\n4. \u003ca href=\"https://github.com/willmcgugan/textual\"\u003ewillmcgugan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/willmcgugan/textual\"\u003etextual\u003c/a\u003e\u003c/b\u003e ⭐ 33,821    \n   The lean application framework for Python.  Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.  \n   🔗 [textual.textualize.io](https://textual.textualize.io/)  \n\n5. \u003ca href=\"https://github.com/tqdm/tqdm\"\u003etqdm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tqdm/tqdm\"\u003etqdm\u003c/a\u003e\u003c/b\u003e ⭐ 30,889    \n   :zap: A Fast, Extensible Progress Bar for Python and CLI  \n   🔗 [tqdm.github.io](https://tqdm.github.io)  \n\n6. \u003ca href=\"https://github.com/google/python-fire\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/python-fire\"\u003epython-fire\u003c/a\u003e\u003c/b\u003e ⭐ 28,072    \n   Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.  \n\n7. \u003ca href=\"https://github.com/tiangolo/typer\"\u003etiangolo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tiangolo/typer\"\u003etyper\u003c/a\u003e\u003c/b\u003e ⭐ 18,686    \n   Typer, build great CLIs. Easy to code. Based on Python type hints.  \n   🔗 [typer.tiangolo.com](https://typer.tiangolo.com/)  \n\n8. \u003ca href=\"https://github.com/pallets/click\"\u003epallets/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pallets/click\"\u003eclick\u003c/a\u003e\u003c/b\u003e ⭐ 17,141    \n   Python composable command line interface toolkit  \n   🔗 [click.palletsprojects.com](https://click.palletsprojects.com)  \n\n9. \u003ca href=\"https://github.com/simonw/llm\"\u003esimonw/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/simonw/llm\"\u003ellm\u003c/a\u003e\u003c/b\u003e ⭐ 10,950    \n   A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine.  \n   🔗 [llm.datasette.io](https://llm.datasette.io)  \n\n10. \u003ca href=\"https://github.com/prompt-toolkit/python-prompt-toolkit\"\u003eprompt-toolkit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prompt-toolkit/python-prompt-toolkit\"\u003epython-prompt-toolkit\u003c/a\u003e\u003c/b\u003e ⭐ 10,225    \n   Library for building powerful interactive command line applications in Python  \n   🔗 [python-prompt-toolkit.readthedocs.io](https://python-prompt-toolkit.readthedocs.io/)  \n\n11. \u003ca href=\"https://github.com/saulpw/visidata\"\u003esaulpw/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/saulpw/visidata\"\u003evisidata\u003c/a\u003e\u003c/b\u003e ⭐ 8,755    \n   A terminal spreadsheet multitool for discovering and arranging data  \n   🔗 [visidata.org](http://visidata.org)  \n\n12. \u003ca href=\"https://github.com/xxh/xxh\"\u003exxh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xxh/xxh\"\u003exxh\u003c/a\u003e\u003c/b\u003e ⭐ 5,886    \n   🚀 Bring your favorite shell wherever you go through the ssh. Xonsh shell, fish, zsh, osquery and so on.  \n\n13. \u003ca href=\"https://github.com/tconbeer/harlequin\"\u003etconbeer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tconbeer/harlequin\"\u003eharlequin\u003c/a\u003e\u003c/b\u003e ⭐ 5,646    \n   The SQL IDE for Your Terminal.  \n   🔗 [harlequin.sh](https://harlequin.sh)  \n\n14. \u003ca href=\"https://github.com/manrajgrover/halo\"\u003emanrajgrover/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/manrajgrover/halo\"\u003ehalo\u003c/a\u003e\u003c/b\u003e ⭐ 3,086    \n   💫 Beautiful spinners for terminal, IPython and Jupyter  \n\n15. \u003ca href=\"https://github.com/textualize/trogon\"\u003etextualize/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/textualize/trogon\"\u003etrogon\u003c/a\u003e\u003c/b\u003e ⭐ 2,791    \n   Easily turn your Click CLI into a powerful terminal application  \n\n16. \u003ca href=\"https://github.com/darrenburns/elia\"\u003edarrenburns/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/darrenburns/elia\"\u003eelia\u003c/a\u003e\u003c/b\u003e ⭐ 2,416    \n   A snappy, keyboard-centric terminal user interface for interacting with large language models. Chat with ChatGPT, Claude, Llama 3, Phi 3, Mistral, Gemma and more.  \n\n17. \u003ca href=\"https://github.com/shobrook/wut\"\u003eshobrook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/shobrook/wut\"\u003ewut\u003c/a\u003e\u003c/b\u003e ⭐ 1,406    \n   Just type wut and an LLM will help you understand whatever's in your terminal. You'll be surprised how useful this can be.  \n\n18. \u003ca href=\"https://github.com/1j01/textual-paint\"\u003e1j01/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/1j01/textual-paint\"\u003etextual-paint\u003c/a\u003e\u003c/b\u003e ⭐ 1,083    \n   :art: MS Paint in your terminal.  \n   🔗 [pypi.org/project/textual-paint](https://pypi.org/project/textual-paint/)  \n\n## Testing\n\nTesting libraries: unit testing, load testing, acceptance testing, code coverage, browser automation, plugins.  \n\n1. \u003ca href=\"https://github.com/mitmproxy/mitmproxy\"\u003emitmproxy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mitmproxy/mitmproxy\"\u003emitmproxy\u003c/a\u003e\u003c/b\u003e ⭐ 42,031    \n   An interactive TLS-capable intercepting HTTP proxy for penetration testers and software developers.  \n   🔗 [mitmproxy.org](https://mitmproxy.org)  \n\n2. \u003ca href=\"https://github.com/locustio/locust\"\u003elocustio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/locustio/locust\"\u003elocust\u003c/a\u003e\u003c/b\u003e ⭐ 27,382    \n   Write scalable load tests in plain Python 🚗💨  \n\n3. \u003ca href=\"https://github.com/microsoft/playwright-python\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/playwright-python\"\u003eplaywright-python\u003c/a\u003e\u003c/b\u003e ⭐ 14,182    \n   Playwright is a Python library to automate Chromium, Firefox and WebKit browsers with a single API.  \n   🔗 [playwright.dev/python](https://playwright.dev/python/)  \n\n4. \u003ca href=\"https://github.com/pytest-dev/pytest\"\u003epytest-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytest-dev/pytest\"\u003epytest\u003c/a\u003e\u003c/b\u003e ⭐ 13,483    \n   The pytest framework makes it easy to write small tests, yet scales to support complex functional testing  \n   🔗 [pytest.org](https://pytest.org)  \n\n5. \u003ca href=\"https://github.com/confident-ai/deepeval\"\u003econfident-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/confident-ai/deepeval\"\u003edeepeval\u003c/a\u003e\u003c/b\u003e ⭐ 13,158    \n   LLM evaluation framework similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc  \n   🔗 [deepeval.com](https://deepeval.com)  \n\n6. \u003ca href=\"https://github.com/seleniumbase/seleniumbase\"\u003eseleniumbase/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/seleniumbase/seleniumbase\"\u003eSeleniumBase\u003c/a\u003e\u003c/b\u003e ⭐ 12,116    \n   Python APIs for web automation, testing, and bypassing bot-detection with ease.  \n   🔗 [seleniumbase.io](https://seleniumbase.io)  \n\n7. \u003ca href=\"https://github.com/robotframework/robotframework\"\u003erobotframework/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/robotframework/robotframework\"\u003erobotframework\u003c/a\u003e\u003c/b\u003e ⭐ 11,370    \n   Generic automation framework for acceptance testing and RPA  \n   🔗 [robotframework.org](http://robotframework.org)  \n\n8. \u003ca href=\"https://github.com/hypothesisworks/hypothesis\"\u003ehypothesisworks/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hypothesisworks/hypothesis\"\u003ehypothesis\u003c/a\u003e\u003c/b\u003e ⭐ 8,407    \n   The property-based testing library for Python  \n   🔗 [hypothesis.works](https://hypothesis.works)  \n\n9. \u003ca href=\"https://github.com/getmoto/moto\"\u003egetmoto/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/getmoto/moto\"\u003emoto\u003c/a\u003e\u003c/b\u003e ⭐ 8,161    \n   A library that allows you to easily mock out tests based on AWS infrastructure.  \n   🔗 [docs.getmoto.org/en/latest](http://docs.getmoto.org/en/latest/)  \n\n10. \u003ca href=\"https://github.com/newsapps/beeswithmachineguns\"\u003enewsapps/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/newsapps/beeswithmachineguns\"\u003ebeeswithmachineguns\u003c/a\u003e\u003c/b\u003e ⭐ 6,618    \n   A utility for arming (creating) many bees (micro EC2 instances) to attack (load test) targets (web applications).  \n   🔗 [apps.chicagotribune.com](http://apps.chicagotribune.com/)  \n\n11. \u003ca href=\"https://github.com/codium-ai/cover-agent\"\u003ecodium-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/codium-ai/cover-agent\"\u003eqodo-cover\u003c/a\u003e\u003c/b\u003e ⭐ 5,262    \n   Qodo-Cover: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! 💻🤖🧪🐞  \n   🔗 [qodo.ai](https://qodo.ai/)  \n\n12. \u003ca href=\"https://github.com/spulec/freezegun\"\u003espulec/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spulec/freezegun\"\u003efreezegun\u003c/a\u003e\u003c/b\u003e ⭐ 4,485    \n   Let your Python tests travel through time  \n\n13. \u003ca href=\"https://github.com/getsentry/responses\"\u003egetsentry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/getsentry/responses\"\u003eresponses\u003c/a\u003e\u003c/b\u003e ⭐ 4,316    \n   A utility for mocking out the Python Requests library.  \n\n14. \u003ca href=\"https://github.com/tox-dev/tox\"\u003etox-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tox-dev/tox\"\u003etox\u003c/a\u003e\u003c/b\u003e ⭐ 3,892    \n   Command line driven CI frontend and development task automation tool.  \n   🔗 [tox.wiki](https://tox.wiki)  \n\n15. \u003ca href=\"https://github.com/nedbat/coveragepy\"\u003enedbat/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nedbat/coveragepy\"\u003ecoveragepy\u003c/a\u003e\u003c/b\u003e ⭐ 3,308    \n   The code coverage tool for Python  \n   🔗 [coverage.readthedocs.io](https://coverage.readthedocs.io)  \n\n## Machine Learning - Time Series\n\nMachine learning and classical timeseries libraries: forecasting, seasonality, anomaly detection, econometrics.  \n\n1. \u003ca href=\"https://github.com/facebook/prophet\"\u003efacebook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebook/prophet\"\u003eprophet\u003c/a\u003e\u003c/b\u003e ⭐ 19,969    \n   Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.  \n   🔗 [facebook.github.io/prophet](https://facebook.github.io/prophet)  \n\n2. \u003ca href=\"https://github.com/sktime/sktime\"\u003esktime/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sktime/sktime\"\u003esktime\u003c/a\u003e\u003c/b\u003e ⭐ 9,464    \n   A unified framework for machine learning with time series  \n   🔗 [www.sktime.net](https://www.sktime.net)  \n\n3. \u003ca href=\"https://github.com/unit8co/darts\"\u003eunit8co/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/unit8co/darts\"\u003edarts\u003c/a\u003e\u003c/b\u003e ⭐ 9,161    \n   A python library for user-friendly forecasting and anomaly detection on time series.  \n   🔗 [unit8co.github.io/darts](https://unit8co.github.io/darts/)  \n\n4. \u003ca href=\"https://github.com/blue-yonder/tsfresh\"\u003eblue-yonder/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blue-yonder/tsfresh\"\u003etsfresh\u003c/a\u003e\u003c/b\u003e ⭐ 9,086    \n   Automatic extraction of relevant features from time series:  \n   🔗 [tsfresh.readthedocs.io](http://tsfresh.readthedocs.io)  \n\n5. \u003ca href=\"https://github.com/google-research/timesfm\"\u003egoogle-research/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google-research/timesfm\"\u003etimesfm\u003c/a\u003e\u003c/b\u003e ⭐ 7,665    \n   TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.  \n   🔗 [research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/)  \n\n6. \u003ca href=\"https://github.com/facebookresearch/kats\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/kats\"\u003eKats\u003c/a\u003e\u003c/b\u003e ⭐ 6,278    \n   Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.   \n\n7. \u003ca href=\"https://github.com/awslabs/gluonts\"\u003eawslabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/awslabs/gluonts\"\u003egluonts\u003c/a\u003e\u003c/b\u003e ⭐ 5,123    \n   Probabilistic time series modeling in Python  \n   🔗 [ts.gluon.ai](https://ts.gluon.ai)  \n\n8. \u003ca href=\"https://github.com/amazon-science/chronos-forecasting\"\u003eamazon-science/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/amazon-science/chronos-forecasting\"\u003echronos-forecasting\u003c/a\u003e\u003c/b\u003e ⭐ 4,691    \n   Chronos: Pretrained Models for Time Series Forecasting  \n   🔗 [arxiv.org/abs/2510.15821](https://arxiv.org/abs/2510.15821)  \n\n9. \u003ca href=\"https://github.com/nixtla/statsforecast\"\u003enixtla/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nixtla/statsforecast\"\u003estatsforecast\u003c/a\u003e\u003c/b\u003e ⭐ 4,661    \n   Lightning ⚡️ fast forecasting with statistical and econometric models.  \n   🔗 [nixtlaverse.nixtla.io/statsforecast](https://nixtlaverse.nixtla.io/statsforecast)  \n\n10. \u003ca href=\"https://github.com/salesforce/merlion\"\u003esalesforce/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/salesforce/merlion\"\u003eMerlion\u003c/a\u003e\u003c/b\u003e ⭐ 4,474    \n   Merlion: A Machine Learning Framework for Time Series Intelligence  \n\n11. \u003ca href=\"https://github.com/tdameritrade/stumpy\"\u003etdameritrade/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tdameritrade/stumpy\"\u003estumpy\u003c/a\u003e\u003c/b\u003e ⭐ 4,055    \n   STUMPY is a powerful and scalable Python library for modern time series analysis  \n   🔗 [stumpy.readthedocs.io/en/latest](https://stumpy.readthedocs.io/en/latest/)  \n\n12. \u003ca href=\"https://github.com/yuqinie98/patchtst\"\u003eyuqinie98/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yuqinie98/patchtst\"\u003ePatchTST\u003c/a\u003e\u003c/b\u003e ⭐ 2,409    \n   An offical implementation of PatchTST: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers  \n\n13. \u003ca href=\"https://github.com/aistream-peelout/flow-forecast\"\u003eaistream-peelout/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aistream-peelout/flow-forecast\"\u003eflow-forecast\u003c/a\u003e\u003c/b\u003e ⭐ 2,268    \n   Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).  \n   🔗 [flow-forecast.atlassian.net/wiki/spaces/ff/overview](https://flow-forecast.atlassian.net/wiki/spaces/FF/overview)  \n\n14. \u003ca href=\"https://github.com/uber/orbit\"\u003euber/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/uber/orbit\"\u003eorbit\u003c/a\u003e\u003c/b\u003e ⭐ 2,028    \n   A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.  \n   🔗 [orbit-ml.readthedocs.io/en/stable](https://orbit-ml.readthedocs.io/en/stable/)  \n\n15. \u003ca href=\"https://github.com/time-series-foundation-models/lag-llama\"\u003etime-series-foundation-models/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/time-series-foundation-models/lag-llama\"\u003elag-llama\u003c/a\u003e\u003c/b\u003e ⭐ 1,537    \n   Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting  \n\n16. \u003ca href=\"https://github.com/ngruver/llmtime\"\u003engruver/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ngruver/llmtime\"\u003ellmtime\u003c/a\u003e\u003c/b\u003e ⭐ 819    \n   LLMTime, a method for zero-shot time series forecasting with large language models (LLMs) by encoding numbers as text and sampling possible extrapolations as text completions  \n   🔗 [arxiv.org/abs/2310.07820](https://arxiv.org/abs/2310.07820)  \n\n17. \u003ca href=\"https://github.com/google/temporian\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/temporian\"\u003etemporian\u003c/a\u003e\u003c/b\u003e ⭐ 708    \n   Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖  \n   🔗 [temporian.readthedocs.io](https://temporian.readthedocs.io)  \n\n## Typing\n\nTyping libraries: static and run-time type checking, annotations.  \n\n1. \u003ca href=\"https://github.com/python/mypy\"\u003epython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python/mypy\"\u003emypy\u003c/a\u003e\u003c/b\u003e ⭐ 20,144    \n   Optional static typing for Python  \n   🔗 [www.mypy-lang.org](https://www.mypy-lang.org/)  \n\n2. \u003ca href=\"https://github.com/astral-sh/ty\"\u003eastral-sh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/astral-sh/ty\"\u003ety\u003c/a\u003e\u003c/b\u003e ⭐ 16,731    \n   An extremely fast Python type checker and language server, written in Rust.  \n   🔗 [docs.astral.sh/ty](https://docs.astral.sh/ty/)  \n\n3. \u003ca href=\"https://github.com/microsoft/pyright\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/pyright\"\u003epyright\u003c/a\u003e\u003c/b\u003e ⭐ 15,153    \n   Static Type Checker for Python  \n\n4. \u003ca href=\"https://github.com/facebook/pyre-check\"\u003efacebook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebook/pyre-check\"\u003epyre-check\u003c/a\u003e\u003c/b\u003e ⭐ 7,141    \n   Performant type-checking for python.  \n   🔗 [pyre-check.org](https://pyre-check.org/)  \n\n5. \u003ca href=\"https://github.com/python-attrs/attrs\"\u003epython-attrs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-attrs/attrs\"\u003eattrs\u003c/a\u003e\u003c/b\u003e ⭐ 5,711    \n   Python Classes Without Boilerplate  \n   🔗 [www.attrs.org](https://www.attrs.org/)  \n\n6. \u003ca href=\"https://github.com/facebook/pyrefly\"\u003efacebook/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebook/pyrefly\"\u003epyrefly\u003c/a\u003e\u003c/b\u003e ⭐ 5,247    \n   A fast type checker and IDE for Python. (A new version of Pyre)  \n   🔗 [pyrefly.org](http://pyrefly.org/)  \n\n7. \u003ca href=\"https://github.com/google/pytype\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/pytype\"\u003epytype\u003c/a\u003e\u003c/b\u003e ⭐ 5,033    \n   A static type analyzer for Python code  \n   🔗 [google.github.io/pytype](https://google.github.io/pytype)  \n\n8. \u003ca href=\"https://github.com/instagram/monkeytype\"\u003einstagram/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/instagram/monkeytype\"\u003eMonkeyType\u003c/a\u003e\u003c/b\u003e ⭐ 4,989    \n   A Python library that generates static type annotations by collecting runtime types  \n\n9. \u003ca href=\"https://github.com/python/typeshed\"\u003epython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python/typeshed\"\u003etypeshed\u003c/a\u003e\u003c/b\u003e ⭐ 4,988    \n   Collection of library stubs for Python, with static types  \n\n10. \u003ca href=\"https://github.com/koxudaxi/datamodel-code-generator\"\u003ekoxudaxi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/koxudaxi/datamodel-code-generator\"\u003edatamodel-code-generator\u003c/a\u003e\u003c/b\u003e ⭐ 3,718    \n   Python data model generator (Pydantic, dataclasses, TypedDict, msgspec) from OpenAPI, JSON Schema, GraphQL, and raw data (JSON/YAML/CSV).  \n   🔗 [koxudaxi.github.io/datamodel-code-generator](https://koxudaxi.github.io/datamodel-code-generator/)  \n\n11. \u003ca href=\"https://github.com/detachhead/basedpyright\"\u003edetachhead/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/detachhead/basedpyright\"\u003ebasedpyright\u003c/a\u003e\u003c/b\u003e ⭐ 3,054    \n   Basedpyright is a fork of pyright with various type checking improvements, pylance features and more.  \n   🔗 [docs.basedpyright.com](https://docs.basedpyright.com)  \n\n12. \u003ca href=\"https://github.com/mtshiba/pylyzer\"\u003emtshiba/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mtshiba/pylyzer\"\u003epylyzer\u003c/a\u003e\u003c/b\u003e ⭐ 2,875    \n   A fast, feature-rich static code analyzer \u0026 language server for Python  \n   🔗 [mtshiba.github.io/pylyzer](http://mtshiba.github.io/pylyzer/)  \n\n13. \u003ca href=\"https://github.com/microsoft/pylance-release\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/pylance-release\"\u003epylance-release\u003c/a\u003e\u003c/b\u003e ⭐ 1,977    \n   Fast, feature-rich language support for Python. Documentation and issues for Pylance.  \n\n14. \u003ca href=\"https://github.com/robertcraigie/pyright-python\"\u003erobertcraigie/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/robertcraigie/pyright-python\"\u003epyright-python\u003c/a\u003e\u003c/b\u003e ⭐ 258    \n   Python command line wrapper for pyright, a static type checker  \n   🔗 [pypi.org/project/pyright](https://pypi.org/project/pyright/)  \n\n## Utility\n\nGeneral utility libraries: miscellaneous tools, linters, code formatters, version management, package tools, documentation tools.  \n\n1. \u003ca href=\"https://github.com/yt-dlp/yt-dlp\"\u003eyt-dlp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/yt-dlp/yt-dlp\"\u003eyt-dlp\u003c/a\u003e\u003c/b\u003e ⭐ 143,790    \n   A feature-rich command-line audio/video downloader  \n   🔗 [discord.gg/h5mncfw63r](https://discord.gg/H5MNcFW63r)  \n\n2. \u003ca href=\"https://github.com/home-assistant/core\"\u003ehome-assistant/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/home-assistant/core\"\u003ecore\u003c/a\u003e\u003c/b\u003e ⭐ 84,357    \n   🏡 Open source home automation that puts local control and privacy first.  \n   🔗 [www.home-assistant.io](https://www.home-assistant.io)  \n\n3. \u003ca href=\"https://github.com/abi/screenshot-to-code\"\u003eabi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/abi/screenshot-to-code\"\u003escreenshot-to-code\u003c/a\u003e\u003c/b\u003e ⭐ 71,484    \n   Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)  \n   🔗 [screenshottocode.com](https://screenshottocode.com)  \n\n4. \u003ca href=\"https://github.com/python/cpython\"\u003epython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python/cpython\"\u003ecpython\u003c/a\u003e\u003c/b\u003e ⭐ 71,201    \n   The Python programming language  \n   🔗 [www.python.org](https://www.python.org)  \n\n5. \u003ca href=\"https://github.com/localstack/localstack\"\u003elocalstack/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/localstack/localstack\"\u003elocalstack\u003c/a\u003e\u003c/b\u003e ⭐ 64,183    \n   💻 A fully functional local AWS cloud stack. Develop and test your cloud \u0026 Serverless apps offline  \n   🔗 [localstack.cloud](https://localstack.cloud)  \n\n6. \u003ca href=\"https://github.com/ggerganov/whisper.cpp\"\u003eggerganov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ggerganov/whisper.cpp\"\u003ewhisper.cpp\u003c/a\u003e\u003c/b\u003e ⭐ 46,061    \n   Port of OpenAI's Whisper model in C/C++  \n\n7. \u003ca href=\"https://github.com/faif/python-patterns\"\u003efaif/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/faif/python-patterns\"\u003epython-patterns\u003c/a\u003e\u003c/b\u003e ⭐ 42,688    \n   A collection of design patterns/idioms in Python  \n\n8. \u003ca href=\"https://github.com/mingrammer/diagrams\"\u003emingrammer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mingrammer/diagrams\"\u003ediagrams\u003c/a\u003e\u003c/b\u003e ⭐ 41,945    \n   :art: Diagram as Code for prototyping cloud system architectures  \n   🔗 [diagrams.mingrammer.com](https://diagrams.mingrammer.com)  \n\n9. \u003ca href=\"https://github.com/openai/openai-python\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/openai-python\"\u003eopenai-python\u003c/a\u003e\u003c/b\u003e ⭐ 29,758    \n   The official Python library for the OpenAI API  \n   🔗 [pypi.org/project/openai](https://pypi.org/project/openai/)  \n\n10. \u003ca href=\"https://github.com/blakeblackshear/frigate\"\u003eblakeblackshear/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blakeblackshear/frigate\"\u003efrigate\u003c/a\u003e\u003c/b\u003e ⭐ 29,705    \n   NVR with realtime local object detection for IP cameras  \n   🔗 [frigate.video](https://frigate.video)  \n\n11. \u003ca href=\"https://github.com/pydantic/pydantic\"\u003epydantic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydantic/pydantic\"\u003epydantic\u003c/a\u003e\u003c/b\u003e ⭐ 26,549    \n   Data validation using Python type hints  \n   🔗 [docs.pydantic.dev](https://docs.pydantic.dev)  \n\n12. \u003ca href=\"https://github.com/squidfunk/mkdocs-material\"\u003esquidfunk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/squidfunk/mkdocs-material\"\u003emkdocs-material\u003c/a\u003e\u003c/b\u003e ⭐ 25,858    \n   Documentation that simply works  \n   🔗 [squidfunk.github.io/mkdocs-material](https://squidfunk.github.io/mkdocs-material/)  \n\n13. \u003ca href=\"https://github.com/keon/algorithms\"\u003ekeon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/keon/algorithms\"\u003ealgorithms\u003c/a\u003e\u003c/b\u003e ⭐ 24,956    \n   Minimal examples of data structures and algorithms in Python  \n\n14. \u003ca href=\"https://github.com/norvig/pytudes\"\u003enorvig/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/norvig/pytudes\"\u003epytudes\u003c/a\u003e\u003c/b\u003e ⭐ 24,251    \n   Python programs, usually short, of considerable difficulty, to perfect particular skills.  \n\n15. \u003ca href=\"https://github.com/delgan/loguru\"\u003edelgan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/delgan/loguru\"\u003eloguru\u003c/a\u003e\u003c/b\u003e ⭐ 23,492    \n   Python logging made (stupidly) simple  \n   🔗 [loguru.readthedocs.io](https://loguru.readthedocs.io)  \n\n16. \u003ca href=\"https://github.com/facebookresearch/audiocraft\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/audiocraft\"\u003eaudiocraft\u003c/a\u003e\u003c/b\u003e ⭐ 22,929    \n   Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.  \n\n17. \u003ca href=\"https://github.com/chriskiehl/gooey\"\u003echriskiehl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chriskiehl/gooey\"\u003eGooey\u003c/a\u003e\u003c/b\u003e ⭐ 22,040    \n   Turn (almost) any Python command line program into a full GUI application with one line  \n\n18. \u003ca href=\"https://github.com/rustpython/rustpython\"\u003erustpython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rustpython/rustpython\"\u003eRustPython\u003c/a\u003e\u003c/b\u003e ⭐ 21,718    \n   A Python Interpreter written in Rust  \n   🔗 [rustpython.github.io](https://rustpython.github.io)  \n\n19. \u003ca href=\"https://github.com/mkdocs/mkdocs\"\u003emkdocs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mkdocs/mkdocs\"\u003emkdocs\u003c/a\u003e\u003c/b\u003e ⭐ 21,625    \n   Project documentation with Markdown.  \n   🔗 [www.mkdocs.org](https://www.mkdocs.org)  \n\n20. \u003ca href=\"https://github.com/micropython/micropython\"\u003emicropython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/micropython/micropython\"\u003emicropython\u003c/a\u003e\u003c/b\u003e ⭐ 21,373    \n   MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems  \n   🔗 [micropython.org](https://micropython.org)  \n\n21. \u003ca href=\"https://github.com/higherorderco/bend\"\u003ehigherorderco/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/higherorderco/bend\"\u003eBend\u003c/a\u003e\u003c/b\u003e ⭐ 19,144    \n   A massively parallel, high-level programming language  \n   🔗 [higherorderco.com](https://higherorderco.com)  \n\n22. \u003ca href=\"https://github.com/kivy/kivy\"\u003ekivy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kivy/kivy\"\u003ekivy\u003c/a\u003e\u003c/b\u003e ⭐ 18,828    \n   Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS  \n   🔗 [kivy.org](https://kivy.org)  \n\n23. \u003ca href=\"https://github.com/openai/triton\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/triton\"\u003etriton\u003c/a\u003e\u003c/b\u003e ⭐ 18,225    \n   Development repository for the Triton language and compiler  \n   🔗 [triton-lang.org](https://triton-lang.org/)  \n\n24. \u003ca href=\"https://github.com/comet-ml/opik\"\u003ecomet-ml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/comet-ml/opik\"\u003eopik\u003c/a\u003e\u003c/b\u003e ⭐ 17,482    \n   Opik is an open-source platform for evaluating, testing and monitoring LLM applications.  \n   🔗 [www.comet.com/docs/opik](https://www.comet.com/docs/opik/)  \n\n25. \u003ca href=\"https://github.com/ipython/ipython\"\u003eipython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ipython/ipython\"\u003eipython\u003c/a\u003e\u003c/b\u003e ⭐ 16,662    \n   Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.  \n   🔗 [ipython.readthedocs.org](https://ipython.readthedocs.org)  \n\n26. \u003ca href=\"https://github.com/alievk/avatarify-python\"\u003ealievk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/alievk/avatarify-python\"\u003eavatarify-python\u003c/a\u003e\u003c/b\u003e ⭐ 16,550    \n   Avatars for Zoom, Skype and other video-conferencing apps.  \n\n27. \u003ca href=\"https://github.com/caronc/apprise\"\u003ecaronc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/caronc/apprise\"\u003eapprise\u003c/a\u003e\u003c/b\u003e ⭐ 15,648    \n   Apprise - Push Notifications that work with just about every platform!  \n   🔗 [hub.docker.com/r/caronc/apprise](https://hub.docker.com/r/caronc/apprise)  \n\n28. \u003ca href=\"https://github.com/pyo3/pyo3\"\u003epyo3/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyo3/pyo3\"\u003epyo3\u003c/a\u003e\u003c/b\u003e ⭐ 15,202    \n   Rust bindings for the Python interpreter  \n   🔗 [pyo3.rs](https://pyo3.rs)  \n\n29. \u003ca href=\"https://github.com/google/brotli\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/brotli\"\u003ebrotli\u003c/a\u003e\u003c/b\u003e ⭐ 14,548    \n   Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling  \n\n30. \u003ca href=\"https://github.com/nuitka/nuitka\"\u003enuitka/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nuitka/nuitka\"\u003eNuitka\u003c/a\u003e\u003c/b\u003e ⭐ 14,417    \n   Nuitka is a Python compiler written in Python.  It's fully compatible with Python 2.6, 2.7, 3.4-3.13. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.   \n   🔗 [nuitka.net](http://nuitka.net)  \n\n31. \u003ca href=\"https://github.com/zulko/moviepy\"\u003ezulko/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zulko/moviepy\"\u003emoviepy\u003c/a\u003e\u003c/b\u003e ⭐ 14,268    \n   Video editing with Python  \n   🔗 [zulko.github.io/moviepy](https://zulko.github.io/moviepy/)  \n\n32. \u003ca href=\"https://github.com/pyodide/pyodide\"\u003epyodide/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyodide/pyodide\"\u003epyodide\u003c/a\u003e\u003c/b\u003e ⭐ 14,148    \n   Pyodide is a Python distribution for the browser and Node.js based on WebAssembly  \n   🔗 [pyodide.org/en/stable](https://pyodide.org/en/stable/)  \n\n33. \u003ca href=\"https://github.com/python-pillow/pillow\"\u003epython-pillow/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-pillow/pillow\"\u003ePillow\u003c/a\u003e\u003c/b\u003e ⭐ 13,332    \n   The Python Imaging Library adds image processing capabilities to Python (Pillow is the friendly PIL fork)  \n   🔗 [python-pillow.github.io](https://python-pillow.github.io)  \n\n34. \u003ca href=\"https://github.com/pytube/pytube\"\u003epytube/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytube/pytube\"\u003epytube\u003c/a\u003e\u003c/b\u003e ⭐ 13,066    \n   A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.  \n   🔗 [pytube.io](https://pytube.io)  \n\n35. \u003ca href=\"https://github.com/ninja-build/ninja\"\u003eninja-build/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ninja-build/ninja\"\u003eninja\u003c/a\u003e\u003c/b\u003e ⭐ 12,636    \n   Ninja is a small build system with a focus on speed.  \n   🔗 [ninja-build.org](https://ninja-build.org/)  \n\n36. \u003ca href=\"https://github.com/asweigart/pyautogui\"\u003easweigart/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/asweigart/pyautogui\"\u003epyautogui\u003c/a\u003e\u003c/b\u003e ⭐ 12,236    \n   A cross-platform GUI automation Python module for human beings. Used to programmatically control the mouse \u0026 keyboard.  \n\n37. \u003ca href=\"https://github.com/dbader/schedule\"\u003edbader/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dbader/schedule\"\u003eschedule\u003c/a\u003e\u003c/b\u003e ⭐ 12,225    \n   Python job scheduling for humans.  \n   🔗 [schedule.readthedocs.io](https://schedule.readthedocs.io/)  \n\n38. \u003ca href=\"https://github.com/secdev/scapy\"\u003esecdev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/secdev/scapy\"\u003escapy\u003c/a\u003e\u003c/b\u003e ⭐ 12,003    \n   Scapy: the Python-based interactive packet manipulation program \u0026 library.  \n   🔗 [scapy.net](https://scapy.net)  \n\n39. \u003ca href=\"https://github.com/magicstack/uvloop\"\u003emagicstack/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/magicstack/uvloop\"\u003euvloop\u003c/a\u003e\u003c/b\u003e ⭐ 11,607    \n   Ultra fast asyncio event loop.  \n\n40. \u003ca href=\"https://github.com/icloud-photos-downloader/icloud_photos_downloader\"\u003eicloud-photos-downloader/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/icloud-photos-downloader/icloud_photos_downloader\"\u003eicloud_photos_downloader\u003c/a\u003e\u003c/b\u003e ⭐ 11,439    \n   A command-line tool to download photos from iCloud  \n\n41. \u003ca href=\"https://github.com/pallets/jinja\"\u003epallets/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pallets/jinja\"\u003ejinja\u003c/a\u003e\u003c/b\u003e ⭐ 11,404    \n   A very fast and expressive template engine.  \n   🔗 [jinja.palletsprojects.com](https://jinja.palletsprojects.com)  \n\n42. \u003ca href=\"https://github.com/aristocratos/bpytop\"\u003earistocratos/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aristocratos/bpytop\"\u003ebpytop\u003c/a\u003e\u003c/b\u003e ⭐ 10,854    \n   Linux/OSX/FreeBSD resource monitor  \n\n43. \u003ca href=\"https://github.com/cython/cython\"\u003ecython/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cython/cython\"\u003ecython\u003c/a\u003e\u003c/b\u003e ⭐ 10,580    \n   The most widely used Python to C compiler  \n   🔗 [cython.org](https://cython.org)  \n\n44. \u003ca href=\"https://github.com/facebookresearch/hydra\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/hydra\"\u003ehydra\u003c/a\u003e\u003c/b\u003e ⭐ 10,139    \n   Hydra is a framework for elegantly configuring complex applications  \n   🔗 [hydra.cc](https://hydra.cc)  \n\n45. \u003ca href=\"https://github.com/py-pdf/pypdf2\"\u003epy-pdf/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/py-pdf/pypdf2\"\u003epypdf\u003c/a\u003e\u003c/b\u003e ⭐ 9,761    \n   A pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files  \n   🔗 [pypdf.readthedocs.io/en/latest](https://pypdf.readthedocs.io/en/latest/)  \n\n46. \u003ca href=\"https://github.com/boto/boto3\"\u003eboto/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/boto/boto3\"\u003eboto3\u003c/a\u003e\u003c/b\u003e ⭐ 9,665    \n   Boto3, an AWS SDK for Python  \n   🔗 [aws.amazon.com/sdk-for-python](https://aws.amazon.com/sdk-for-python/)  \n\n47. \u003ca href=\"https://github.com/paramiko/paramiko\"\u003eparamiko/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/paramiko/paramiko\"\u003eparamiko\u003c/a\u003e\u003c/b\u003e ⭐ 9,661    \n   The leading native Python SSHv2 protocol library.  \n   🔗 [paramiko.org](http://paramiko.org)  \n\n48. \u003ca href=\"https://github.com/aws/serverless-application-model\"\u003eaws/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aws/serverless-application-model\"\u003eserverless-application-model\u003c/a\u003e\u003c/b\u003e ⭐ 9,546    \n   The AWS Serverless Application Model (AWS SAM) transform is a AWS CloudFormation macro that transforms SAM templates into CloudFormation templates.  \n   🔗 [aws.amazon.com/serverless/sam](https://aws.amazon.com/serverless/sam)  \n\n49. \u003ca href=\"https://github.com/xonsh/xonsh\"\u003exonsh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/xonsh/xonsh\"\u003exonsh\u003c/a\u003e\u003c/b\u003e ⭐ 9,186    \n   🐚 Python-powered shell. Full-featured and cross-platform.  \n   🔗 [xon.sh](http://xon.sh)  \n\n50. \u003ca href=\"https://github.com/arrow-py/arrow\"\u003earrow-py/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/arrow-py/arrow\"\u003earrow\u003c/a\u003e\u003c/b\u003e ⭐ 9,015    \n   🏹 Better dates \u0026 times for Python  \n   🔗 [arrow.readthedocs.io](https://arrow.readthedocs.io)  \n\n51. \u003ca href=\"https://github.com/googleapis/google-api-python-client\"\u003egoogleapis/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/googleapis/google-api-python-client\"\u003egoogle-api-python-client\u003c/a\u003e\u003c/b\u003e ⭐ 8,678    \n   🐍 The official Python client library for Google's discovery based APIs.  \n   🔗 [googleapis.github.io/google-api-python-client/docs](https://googleapis.github.io/google-api-python-client/docs/)  \n\n52. \u003ca href=\"https://github.com/eternnoir/pytelegrambotapi\"\u003eeternnoir/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/eternnoir/pytelegrambotapi\"\u003epyTelegramBotAPI\u003c/a\u003e\u003c/b\u003e ⭐ 8,662    \n   Python Telegram bot api.  \n\n53. \u003ca href=\"https://github.com/theskumar/python-dotenv\"\u003etheskumar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/theskumar/python-dotenv\"\u003epython-dotenv\u003c/a\u003e\u003c/b\u003e ⭐ 8,619    \n   Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.  \n   🔗 [saurabh-kumar.com/python-dotenv](https://saurabh-kumar.com/python-dotenv/)  \n\n54. \u003ca href=\"https://github.com/kellyjonbrazil/jc\"\u003ekellyjonbrazil/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/kellyjonbrazil/jc\"\u003ejc\u003c/a\u003e\u003c/b\u003e ⭐ 8,510    \n   CLI tool and python library that converts the output of popular command-line tools, file-types, and common strings to JSON, YAML, or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts.  \n\n55. \u003ca href=\"https://github.com/jasonppy/voicecraft\"\u003ejasonppy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jasonppy/voicecraft\"\u003eVoiceCraft\u003c/a\u003e\u003c/b\u003e ⭐ 8,456    \n   Zero-Shot Speech Editing and Text-to-Speech in the Wild  \n\n56. \u003ca href=\"https://github.com/jd/tenacity\"\u003ejd/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jd/tenacity\"\u003etenacity\u003c/a\u003e\u003c/b\u003e ⭐ 8,287    \n   Retrying library for Python  \n   🔗 [tenacity.readthedocs.io](http://tenacity.readthedocs.io)  \n\n57. \u003ca href=\"https://github.com/googlecloudplatform/python-docs-samples\"\u003egooglecloudplatform/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/googlecloudplatform/python-docs-samples\"\u003epython-docs-samples\u003c/a\u003e\u003c/b\u003e ⭐ 7,956    \n   Code samples used on cloud.google.com  \n\n58. \u003ca href=\"https://github.com/timdettmers/bitsandbytes\"\u003etimdettmers/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/timdettmers/bitsandbytes\"\u003ebitsandbytes\u003c/a\u003e\u003c/b\u003e ⭐ 7,912    \n   Accessible large language models via k-bit quantization for PyTorch.  \n   🔗 [huggingface.co/docs/bitsandbytes/main/en/index](https://huggingface.co/docs/bitsandbytes/main/en/index)  \n\n59. \u003ca href=\"https://github.com/ijl/orjson\"\u003eijl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ijl/orjson\"\u003eorjson\u003c/a\u003e\u003c/b\u003e ⭐ 7,827    \n   Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy  \n\n60. \u003ca href=\"https://github.com/pygithub/pygithub\"\u003epygithub/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pygithub/pygithub\"\u003ePyGithub\u003c/a\u003e\u003c/b\u003e ⭐ 7,647    \n   Typed interactions with the GitHub API v3  \n   🔗 [pygithub.readthedocs.io](https://pygithub.readthedocs.io/)  \n\n61. \u003ca href=\"https://github.com/sphinx-doc/sphinx\"\u003esphinx-doc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sphinx-doc/sphinx\"\u003esphinx\u003c/a\u003e\u003c/b\u003e ⭐ 7,627    \n   The Sphinx documentation generator  \n   🔗 [www.sphinx-doc.org](https://www.sphinx-doc.org/)  \n\n62. \u003ca href=\"https://github.com/google/latexify_py\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/latexify_py\"\u003elatexify_py\u003c/a\u003e\u003c/b\u003e ⭐ 7,592    \n   A library to generate LaTeX expression from Python code.  \n\n63. \u003ca href=\"https://github.com/pyca/cryptography\"\u003epyca/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyca/cryptography\"\u003ecryptography\u003c/a\u003e\u003c/b\u003e ⭐ 7,446    \n   cryptography is a package designed to expose cryptographic primitives and recipes to Python developers.  \n   🔗 [cryptography.io](https://cryptography.io)  \n\n64. \u003ca href=\"https://github.com/bndr/pipreqs\"\u003ebndr/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bndr/pipreqs\"\u003epipreqs\u003c/a\u003e\u003c/b\u003e ⭐ 7,417    \n   pipreqs - Generate pip requirements.txt file based on imports of any project. Looking for maintainers to move this project forward.  \n\n65. \u003ca href=\"https://github.com/agronholm/apscheduler\"\u003eagronholm/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/agronholm/apscheduler\"\u003eapscheduler\u003c/a\u003e\u003c/b\u003e ⭐ 7,257    \n   Task scheduling library for Python  \n   🔗 [apscheduler.readthedocs.io](https://apscheduler.readthedocs.io/)  \n\n66. \u003ca href=\"https://github.com/gorakhargosh/watchdog\"\u003egorakhargosh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gorakhargosh/watchdog\"\u003ewatchdog\u003c/a\u003e\u003c/b\u003e ⭐ 7,234    \n   Python library and shell utilities to monitor filesystem events.  \n   🔗 [packages.python.org/watchdog](http://packages.python.org/watchdog/)  \n\n67. \u003ca href=\"https://github.com/marshmallow-code/marshmallow\"\u003emarshmallow-code/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marshmallow-code/marshmallow\"\u003emarshmallow\u003c/a\u003e\u003c/b\u003e ⭐ 7,231    \n   A lightweight library for converting complex objects to and from simple Python datatypes.  \n   🔗 [marshmallow.readthedocs.io](https://marshmallow.readthedocs.io/)  \n\n68. \u003ca href=\"https://github.com/hugapi/hug\"\u003ehugapi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hugapi/hug\"\u003ehug\u003c/a\u003e\u003c/b\u003e ⭐ 6,906    \n   Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler.  \n\n69. \u003ca href=\"https://github.com/pdfminer/pdfminer.six\"\u003epdfminer/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pdfminer/pdfminer.six\"\u003epdfminer.six\u003c/a\u003e\u003c/b\u003e ⭐ 6,871    \n   Community maintained fork of pdfminer - we fathom PDF  \n   🔗 [pdfminersix.readthedocs.io](https://pdfminersix.readthedocs.io)  \n\n70. \u003ca href=\"https://github.com/openai/point-e\"\u003eopenai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/openai/point-e\"\u003epoint-e\u003c/a\u003e\u003c/b\u003e ⭐ 6,849    \n   Point cloud diffusion for 3D model synthesis  \n\n71. \u003ca href=\"https://github.com/traceloop/openllmetry\"\u003etraceloop/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/traceloop/openllmetry\"\u003eopenllmetry\u003c/a\u003e\u003c/b\u003e ⭐ 6,784    \n   Open-source observability for your GenAI or LLM application, based on OpenTelemetry  \n   🔗 [www.traceloop.com/openllmetry](https://www.traceloop.com/openllmetry)  \n\n72. \u003ca href=\"https://github.com/sdispater/pendulum\"\u003esdispater/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sdispater/pendulum\"\u003ependulum\u003c/a\u003e\u003c/b\u003e ⭐ 6,609    \n   Python datetimes made easy  \n   🔗 [pendulum.eustace.io](https://pendulum.eustace.io)  \n\n73. \u003ca href=\"https://github.com/scikit-image/scikit-image\"\u003escikit-image/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/scikit-image/scikit-image\"\u003escikit-image\u003c/a\u003e\u003c/b\u003e ⭐ 6,431    \n   Image processing in Python  \n   🔗 [scikit-image.org](https://scikit-image.org)  \n\n74. \u003ca href=\"https://github.com/pytransitions/transitions\"\u003epytransitions/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytransitions/transitions\"\u003etransitions\u003c/a\u003e\u003c/b\u003e ⭐ 6,392    \n   A lightweight, object-oriented finite state machine implementation in Python with many extensions  \n\n75. \u003ca href=\"https://github.com/wireservice/csvkit\"\u003ewireservice/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wireservice/csvkit\"\u003ecsvkit\u003c/a\u003e\u003c/b\u003e ⭐ 6,328    \n   A suite of utilities for converting to and working with CSV, the king of tabular file formats.  \n   🔗 [csvkit.readthedocs.io](https://csvkit.readthedocs.io)  \n\n76. \u003ca href=\"https://github.com/rsalmei/alive-progress\"\u003ersalmei/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rsalmei/alive-progress\"\u003ealive-progress\u003c/a\u003e\u003c/b\u003e ⭐ 6,221    \n   A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!  \n\n77. \u003ca href=\"https://github.com/spotify/pedalboard\"\u003espotify/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spotify/pedalboard\"\u003epedalboard\u003c/a\u003e\u003c/b\u003e ⭐ 5,941    \n   🎛 🔊 A Python library for audio.  \n   🔗 [spotify.github.io/pedalboard](https://spotify.github.io/pedalboard)  \n\n78. \u003ca href=\"https://github.com/pywinauto/pywinauto\"\u003epywinauto/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pywinauto/pywinauto\"\u003epywinauto\u003c/a\u003e\u003c/b\u003e ⭐ 5,863    \n   Windows GUI Automation with Python (based on text properties)  \n   🔗 [pywinauto.github.io](http://pywinauto.github.io/)  \n\n79. \u003ca href=\"https://github.com/tebelorg/rpa-python\"\u003etebelorg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tebelorg/rpa-python\"\u003eRPA-Python\u003c/a\u003e\u003c/b\u003e ⭐ 5,442    \n   Python package for doing RPA  \n\n80. \u003ca href=\"https://github.com/buildbot/buildbot\"\u003ebuildbot/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/buildbot/buildbot\"\u003ebuildbot\u003c/a\u003e\u003c/b\u003e ⭐ 5,423    \n   Python-based continuous integration testing framework; your pull requests are more than welcome!  \n   🔗 [www.buildbot.net](https://www.buildbot.net)  \n\n81. \u003ca href=\"https://github.com/prompt-toolkit/ptpython\"\u003eprompt-toolkit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/prompt-toolkit/ptpython\"\u003eptpython\u003c/a\u003e\u003c/b\u003e ⭐ 5,394    \n   A better Python REPL  \n\n82. \u003ca href=\"https://github.com/pythonnet/pythonnet\"\u003epythonnet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pythonnet/pythonnet\"\u003epythonnet\u003c/a\u003e\u003c/b\u003e ⭐ 5,385    \n   Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.  \n   🔗 [pythonnet.github.io](http://pythonnet.github.io)  \n\n83. \u003ca href=\"https://github.com/pyo3/maturin\"\u003epyo3/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyo3/maturin\"\u003ematurin\u003c/a\u003e\u003c/b\u003e ⭐ 5,317    \n   Build and publish crates with pyo3, cffi and uniffi bindings as well as rust binaries as python packages  \n   🔗 [maturin.rs](https://maturin.rs)  \n\n84. \u003ca href=\"https://github.com/pycqa/pycodestyle\"\u003epycqa/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pycqa/pycodestyle\"\u003epycodestyle\u003c/a\u003e\u003c/b\u003e ⭐ 5,149    \n   Simple Python style checker in one Python file  \n   🔗 [pycodestyle.pycqa.org](https://pycodestyle.pycqa.org)  \n\n85. \u003ca href=\"https://github.com/ashleve/lightning-hydra-template\"\u003eashleve/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ashleve/lightning-hydra-template\"\u003elightning-hydra-template\u003c/a\u003e\u003c/b\u003e ⭐ 5,116    \n   PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation.  ⚡🔥⚡  \n\n86. \u003ca href=\"https://github.com/pytoolz/toolz\"\u003epytoolz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pytoolz/toolz\"\u003etoolz\u003c/a\u003e\u003c/b\u003e ⭐ 5,112    \n   A functional standard library for Python.  \n   🔗 [toolz.readthedocs.org](http://toolz.readthedocs.org/)  \n\n87. \u003ca href=\"https://github.com/bogdanp/dramatiq\"\u003ebogdanp/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bogdanp/dramatiq\"\u003edramatiq\u003c/a\u003e\u003c/b\u003e ⭐ 5,098    \n   A fast and reliable background task processing library for Python 3.  \n   🔗 [dramatiq.io](https://dramatiq.io)  \n\n88. \u003ca href=\"https://github.com/gitpython-developers/gitpython\"\u003egitpython-developers/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gitpython-developers/gitpython\"\u003eGitPython\u003c/a\u003e\u003c/b\u003e ⭐ 5,063    \n   GitPython is a python library used to interact with Git repositories.  \n   🔗 [gitpython.readthedocs.org](http://gitpython.readthedocs.org)  \n\n89. \u003ca href=\"https://github.com/jorgebastida/awslogs\"\u003ejorgebastida/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jorgebastida/awslogs\"\u003eawslogs\u003c/a\u003e\u003c/b\u003e ⭐ 4,975    \n   AWS CloudWatch logs for Humans™  \n\n90. \u003ca href=\"https://github.com/ets-labs/python-dependency-injector\"\u003eets-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ets-labs/python-dependency-injector\"\u003epython-dependency-injector\u003c/a\u003e\u003c/b\u003e ⭐ 4,779    \n   Dependency injection framework for Python  \n   🔗 [python-dependency-injector.ets-labs.org](https://python-dependency-injector.ets-labs.org/)  \n\n91. \u003ca href=\"https://github.com/pyinvoke/invoke\"\u003epyinvoke/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyinvoke/invoke\"\u003einvoke\u003c/a\u003e\u003c/b\u003e ⭐ 4,695    \n   Pythonic task management \u0026 command execution.  \n   🔗 [pyinvoke.org](http://pyinvoke.org)  \n\n92. \u003ca href=\"https://github.com/pyinfra-dev/pyinfra\"\u003epyinfra-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyinfra-dev/pyinfra\"\u003epyinfra\u003c/a\u003e\u003c/b\u003e ⭐ 4,673    \n   🔧 pyinfra turns Python code into shell commands and runs them on your servers. Execute ad-hoc commands and write declarative operations. Target SSH servers, local machine and Docker containers. Fast and scales from one server to thousands.  \n   🔗 [pyinfra.com](https://pyinfra.com)  \n\n93. \u003ca href=\"https://github.com/spotify/basic-pitch\"\u003espotify/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/spotify/basic-pitch\"\u003ebasic-pitch\u003c/a\u003e\u003c/b\u003e ⭐ 4,609    \n   A lightweight yet powerful audio-to-MIDI converter with pitch bend detection  \n   🔗 [basicpitch.io](https://basicpitch.io)  \n\n94. \u003ca href=\"https://github.com/blealtan/efficient-kan\"\u003eblealtan/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/blealtan/efficient-kan\"\u003eefficient-kan\u003c/a\u003e\u003c/b\u003e ⭐ 4,566    \n   An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).  \n\n95. \u003ca href=\"https://github.com/pydantic/monty\"\u003epydantic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydantic/monty\"\u003emonty\u003c/a\u003e\u003c/b\u003e ⭐ 4,523    \n   A minimal, secure Python interpreter written in Rust for use by AI  \n\n96. \u003ca href=\"https://github.com/hynek/structlog\"\u003ehynek/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hynek/structlog\"\u003estructlog\u003c/a\u003e\u003c/b\u003e ⭐ 4,517    \n   Simple, powerful, and fast logging for Python.  \n   🔗 [www.structlog.org](https://www.structlog.org/)  \n\n97. \u003ca href=\"https://github.com/adafruit/circuitpython\"\u003eadafruit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/adafruit/circuitpython\"\u003ecircuitpython\u003c/a\u003e\u003c/b\u003e ⭐ 4,455    \n   CircuitPython - a Python implementation for teaching coding with microcontrollers  \n   🔗 [circuitpython.org](https://circuitpython.org)  \n\n98. \u003ca href=\"https://github.com/miguelgrinberg/python-socketio\"\u003emiguelgrinberg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/miguelgrinberg/python-socketio\"\u003epython-socketio\u003c/a\u003e\u003c/b\u003e ⭐ 4,309    \n   Python Socket.IO server and client  \n\n99. \u003ca href=\"https://github.com/evhub/coconut\"\u003eevhub/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/evhub/coconut\"\u003ecoconut\u003c/a\u003e\u003c/b\u003e ⭐ 4,299    \n   Coconut (coconut-lang.org) is a variant of Python that adds on top of Python syntax new features for simple, elegant, Pythonic functional programming.  \n   🔗 [coconut-lang.org](http://coconut-lang.org)  \n\n100. \u003ca href=\"https://github.com/pydata/xarray\"\u003epydata/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydata/xarray\"\u003exarray\u003c/a\u003e\u003c/b\u003e ⭐ 4,070    \n   N-D labeled arrays and datasets in Python  \n   🔗 [xarray.dev](https://xarray.dev)  \n\n101. \u003ca href=\"https://github.com/pydantic/logfire\"\u003epydantic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydantic/logfire\"\u003elogfire\u003c/a\u003e\u003c/b\u003e ⭐ 3,955    \n   AI observability platform for production LLM and agent systems.  \n   🔗 [logfire.pydantic.dev/docs](https://logfire.pydantic.dev/docs/)  \n\n102. \u003ca href=\"https://github.com/tartley/colorama\"\u003etartley/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tartley/colorama\"\u003ecolorama\u003c/a\u003e\u003c/b\u003e ⭐ 3,765    \n   Simple cross-platform colored terminal text in Python  \n\n103. \u003ca href=\"https://github.com/camelot-dev/camelot\"\u003ecamelot-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/camelot-dev/camelot\"\u003ecamelot\u003c/a\u003e\u003c/b\u003e ⭐ 3,576    \n   A Python library to extract tabular data from PDFs  \n   🔗 [camelot-py.readthedocs.io](https://camelot-py.readthedocs.io)  \n\n104. \u003ca href=\"https://github.com/jorisschellekens/borb\"\u003ejorisschellekens/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jorisschellekens/borb\"\u003eborb\u003c/a\u003e\u003c/b\u003e ⭐ 3,551    \n   borb is a library for reading, creating and manipulating PDF files in python.  \n   🔗 [borbpdf.com](https://borbpdf.com/)  \n\n105. \u003ca href=\"https://github.com/jcrist/msgspec\"\u003ejcrist/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jcrist/msgspec\"\u003emsgspec\u003c/a\u003e\u003c/b\u003e ⭐ 3,530    \n   A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML  \n   🔗 [jcristharif.com/msgspec](https://jcristharif.com/msgspec/)  \n\n106. \u003ca href=\"https://github.com/osohq/oso\"\u003eosohq/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/osohq/oso\"\u003eoso\u003c/a\u003e\u003c/b\u003e ⭐ 3,494    \n   Deprecated: See README  \n\n107. \u003ca href=\"https://github.com/pyserial/pyserial\"\u003epyserial/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyserial/pyserial\"\u003epyserial\u003c/a\u003e\u003c/b\u003e ⭐ 3,490    \n   Python serial port access library  \n   🔗 [pyserial.readthedocs.io/en/latest](http://pyserial.readthedocs.io/en/latest/)  \n\n108. \u003ca href=\"https://github.com/karpathy/reader3\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/reader3\"\u003ereader3\u003c/a\u003e\u003c/b\u003e ⭐ 3,261    \n   A lightweight, self-hosted EPUB reader that lets you read through EPUB books one chapter at a time.  \n\n109. \u003ca href=\"https://github.com/libaudioflux/audioflux\"\u003elibaudioflux/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/libaudioflux/audioflux\"\u003eaudioFlux\u003c/a\u003e\u003c/b\u003e ⭐ 3,241    \n   A library for audio and music analysis, feature extraction.  \n   🔗 [audioflux.top](https://audioflux.top)  \n\n110. \u003ca href=\"https://github.com/rhettbull/osxphotos\"\u003erhettbull/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rhettbull/osxphotos\"\u003eosxphotos\u003c/a\u003e\u003c/b\u003e ⭐ 3,230    \n   Python app to work with pictures and associated metadata from Apple Photos on macOS. Also includes a package to provide programmatic access to the Photos library, pictures, and metadata.   \n\n111. \u003ca href=\"https://github.com/cdgriffith/box\"\u003ecdgriffith/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/cdgriffith/box\"\u003eBox\u003c/a\u003e\u003c/b\u003e ⭐ 2,815    \n   Python dictionaries with advanced dot notation access  \n   🔗 [github.com/cdgriffith/box/wiki](https://github.com/cdgriffith/Box/wiki)  \n\n112. \u003ca href=\"https://github.com/whylabs/whylogs\"\u003ewhylabs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/whylabs/whylogs\"\u003ewhylogs\u003c/a\u003e\u003c/b\u003e ⭐ 2,788    \n   An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality \u0026 model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety \u0026 robustness. 📈  \n   🔗 [whylogs.readthedocs.io](https://whylogs.readthedocs.io/)  \n\n113. \u003ca href=\"https://github.com/liiight/notifiers\"\u003eliiight/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/liiight/notifiers\"\u003enotifiers\u003c/a\u003e\u003c/b\u003e ⭐ 2,728    \n   The easy way to send notifications  \n   🔗 [notifiers.readthedocs.io](http://notifiers.readthedocs.io/)  \n\n114. \u003ca href=\"https://github.com/litl/backoff\"\u003elitl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/litl/backoff\"\u003ebackoff\u003c/a\u003e\u003c/b\u003e ⭐ 2,702    \n   Python library providing function decorators for configurable backoff and retry  \n\n115. \u003ca href=\"https://github.com/anthropics/anthropic-sdk-python\"\u003eanthropics/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anthropics/anthropic-sdk-python\"\u003eanthropic-sdk-python\u003c/a\u003e\u003c/b\u003e ⭐ 2,651    \n   SDK providing access to Anthropic's safety-first language model APIs  \n\n116. \u003ca href=\"https://github.com/dosisod/refurb\"\u003edosisod/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dosisod/refurb\"\u003erefurb\u003c/a\u003e\u003c/b\u003e ⭐ 2,519    \n   A tool for refurbishing and modernizing Python codebases  \n\n117. \u003ca href=\"https://github.com/pyston/pyston\"\u003epyston/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyston/pyston\"\u003epyston\u003c/a\u003e\u003c/b\u003e ⭐ 2,509    \n   (No longer maintained) A faster and highly-compatible implementation of the Python programming language.  \n   🔗 [www.pyston.org](https://www.pyston.org/)  \n\n118. \u003ca href=\"https://github.com/astanin/python-tabulate\"\u003eastanin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/astanin/python-tabulate\"\u003epython-tabulate\u003c/a\u003e\u003c/b\u003e ⭐ 2,508    \n   Pretty-print tabular data in Python, a library and a command-line utility. Repository migrated from bitbucket.org/astanin/python-tabulate.  \n   🔗 [pypi.org/project/tabulate](https://pypi.org/project/tabulate/)  \n\n119. \u003ca href=\"https://github.com/omry/omegaconf\"\u003eomry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/omry/omegaconf\"\u003eomegaconf\u003c/a\u003e\u003c/b\u003e ⭐ 2,330    \n   Flexible Python configuration system. The last one you will ever need.  \n\n120. \u003ca href=\"https://github.com/ariebovenberg/whenever\"\u003eariebovenberg/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ariebovenberg/whenever\"\u003ewhenever\u003c/a\u003e\u003c/b\u003e ⭐ 2,285    \n   ⏰ Modern datetime library for Python  \n   🔗 [whenever.rtfd.io](https://whenever.rtfd.io)  \n\n121. \u003ca href=\"https://github.com/open-telemetry/opentelemetry-python\"\u003eopen-telemetry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/open-telemetry/opentelemetry-python\"\u003eopentelemetry-python\u003c/a\u003e\u003c/b\u003e ⭐ 2,285    \n   OpenTelemetry Python API and SDK   \n   🔗 [opentelemetry.io](https://opentelemetry.io)  \n\n122. \u003ca href=\"https://github.com/p0dalirius/coercer\"\u003ep0dalirius/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/p0dalirius/coercer\"\u003eCoercer\u003c/a\u003e\u003c/b\u003e ⭐ 2,164    \n   A python script to automatically coerce a Windows server to authenticate on an arbitrary machine through 12 methods.  \n   🔗 [podalirius.net](https://podalirius.net/)  \n\n123. \u003ca href=\"https://github.com/pygments/pygments\"\u003epygments/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pygments/pygments\"\u003epygments\u003c/a\u003e\u003c/b\u003e ⭐ 2,098    \n   Pygments is a generic syntax highlighter written in Python  \n   🔗 [pygments.org](http://pygments.org/)  \n\n124. \u003ca href=\"https://github.com/mkdocstrings/mkdocstrings\"\u003emkdocstrings/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mkdocstrings/mkdocstrings\"\u003emkdocstrings\u003c/a\u003e\u003c/b\u003e ⭐ 2,043    \n   📘 Automatic documentation from sources, for MkDocs.  \n   🔗 [mkdocstrings.github.io](https://mkdocstrings.github.io/)  \n\n125. \u003ca href=\"https://github.com/karpathy/rendergit\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/rendergit\"\u003erendergit\u003c/a\u003e\u003c/b\u003e ⭐ 2,011    \n   Render any git repo into a single static HTML page for humans or LLMs  \n\n126. \u003ca href=\"https://github.com/chrishayuk/mcp-cli\"\u003echrishayuk/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/chrishayuk/mcp-cli\"\u003emcp-cli\u003c/a\u003e\u003c/b\u003e ⭐ 1,844    \n   A protocol-level CLI designed to interact with a Model Context Protocol server. The client allows users to send commands, query data, and interact with various resources provided by the server.  \n\n127. \u003ca href=\"https://github.com/extensityai/symbolicai\"\u003eextensityai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/extensityai/symbolicai\"\u003esymbolicai\u003c/a\u003e\u003c/b\u003e ⭐ 1,659    \n   Compositional Differentiable Programming Library - divide-and-conquer approach to break down a complex problem into smaller, more manageable problems.  \n\n128. \u003ca href=\"https://github.com/pypy/pypy\"\u003epypy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pypy/pypy\"\u003epypy\u003c/a\u003e\u003c/b\u003e ⭐ 1,635    \n   PyPy is a very fast and compliant implementation of the Python language.  \n   🔗 [pypy.org](https://pypy.org)  \n\n129. \u003ca href=\"https://github.com/lcompilers/lpython\"\u003elcompilers/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lcompilers/lpython\"\u003elpython\u003c/a\u003e\u003c/b\u003e ⭐ 1,627    \n   Python compiler  \n   🔗 [lpython.org](https://lpython.org/)  \n\n130. \u003ca href=\"https://github.com/juanbindez/pytubefix\"\u003ejuanbindez/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/juanbindez/pytubefix\"\u003epytubefix\u003c/a\u003e\u003c/b\u003e ⭐ 1,445    \n   Python3 library for downloading YouTube Videos.   \n   🔗 [pytubefix.readthedocs.io](https://pytubefix.readthedocs.io)  \n\n131. \u003ca href=\"https://github.com/daveebbelaar/python-whatsapp-bot\"\u003edaveebbelaar/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/daveebbelaar/python-whatsapp-bot\"\u003epython-whatsapp-bot\u003c/a\u003e\u003c/b\u003e ⭐ 1,438    \n   This guide will walk you through the process of creating a WhatsApp bot using the Meta (formerly Facebook) Cloud API with pure Python, and Flask  \n   🔗 [www.datalumina.com](https://www.datalumina.com)  \n\n132. \u003ca href=\"https://github.com/pydantic/pydantic-settings\"\u003epydantic/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pydantic/pydantic-settings\"\u003epydantic-settings\u003c/a\u003e\u003c/b\u003e ⭐ 1,227    \n   Settings management using pydantic  \n   🔗 [docs.pydantic.dev/latest/usage/pydantic_settings](https://docs.pydantic.dev/latest/usage/pydantic_settings/)  \n\n133. \u003ca href=\"https://github.com/barracuda-fsh/pyobd\"\u003ebarracuda-fsh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/barracuda-fsh/pyobd\"\u003epyobd\u003c/a\u003e\u003c/b\u003e ⭐ 1,140    \n   An OBD-II compliant car diagnostic tool  \n\n134. \u003ca href=\"https://github.com/modal-labs/modal-examples\"\u003emodal-labs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/modal-labs/modal-examples\"\u003emodal-examples\u003c/a\u003e\u003c/b\u003e ⭐ 1,087    \n   Examples of programs built using Modal  \n   🔗 [modal.com/docs](https://modal.com/docs)  \n\n135. \u003ca href=\"https://github.com/lastmile-ai/aiconfig\"\u003elastmile-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lastmile-ai/aiconfig\"\u003eaiconfig\u003c/a\u003e\u003c/b\u003e ⭐ 1,077    \n   AIConfig saves prompts, models and model parameters as source control friendly configs. This allows you to iterate on prompts and model parameters separately from your application code.  \n   🔗 [aiconfig.lastmileai.dev](https://aiconfig.lastmileai.dev)  \n\n136. \u003ca href=\"https://github.com/tavily-ai/tavily-python\"\u003etavily-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tavily-ai/tavily-python\"\u003etavily-python\u003c/a\u003e\u003c/b\u003e ⭐ 981    \n   The Tavily Python wrapper allows for easy interaction with the Tavily API, offering the full range of our search and extract functionalities directly from your Python programs.  \n   🔗 [pypi.org/project/tavily-python](https://pypi.org/project/tavily-python/)  \n\n137. \u003ca href=\"https://github.com/tox-dev/filelock\"\u003etox-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tox-dev/filelock\"\u003efilelock\u003c/a\u003e\u003c/b\u003e ⭐ 926    \n   A platform independent file lock in Python, which provides a simple way of inter-process communication  \n   🔗 [py-filelock.readthedocs.io](https://py-filelock.readthedocs.io)  \n\n138. \u003ca href=\"https://github.com/secretiveshell/mcp-bridge\"\u003esecretiveshell/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/secretiveshell/mcp-bridge\"\u003eMCP-Bridge\u003c/a\u003e\u003c/b\u003e ⭐ 895    \n   A middleware to provide an openAI compatible endpoint that can call MCP tools  \n\n139. \u003ca href=\"https://github.com/google/pyglove\"\u003egoogle/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/google/pyglove\"\u003epyglove\u003c/a\u003e\u003c/b\u003e ⭐ 708    \n   Manipulating Python Programs  \n\n140. \u003ca href=\"https://github.com/neuml/annotateai\"\u003eneuml/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neuml/annotateai\"\u003eannotateai\u003c/a\u003e\u003c/b\u003e ⭐ 400    \n   Automatically annotates papers using Large Language Models (LLMs)  \n\n## Vizualisation\n\nVizualisation tools and libraries. Application frameworks, 2D/3D plotting, dashboards, WebGL.  \n\n1. \u003ca href=\"https://github.com/apache/superset\"\u003eapache/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/apache/superset\"\u003esuperset\u003c/a\u003e\u003c/b\u003e ⭐ 70,248    \n   Apache Superset is a Data Visualization and Data Exploration Platform  \n   🔗 [superset.apache.org](https://superset.apache.org/)  \n\n2. \u003ca href=\"https://github.com/streamlit/streamlit\"\u003estreamlit/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/streamlit/streamlit\"\u003estreamlit\u003c/a\u003e\u003c/b\u003e ⭐ 43,180    \n   Streamlit — A faster way to build and share data apps.  \n   🔗 [streamlit.io](https://streamlit.io)  \n\n3. \u003ca href=\"https://github.com/gradio-app/gradio\"\u003egradio-app/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/gradio-app/gradio\"\u003egradio\u003c/a\u003e\u003c/b\u003e ⭐ 41,415    \n   Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!  \n   🔗 [www.gradio.app](http://www.gradio.app)  \n\n4. \u003ca href=\"https://github.com/danny-avila/librechat\"\u003edanny-avila/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/danny-avila/librechat\"\u003eLibreChat\u003c/a\u003e\u003c/b\u003e ⭐ 33,310    \n   LibreChat is a free, open source AI chat platform. This Web UI offers vast customization, supporting numerous AI providers, services, and integrations.  \n   🔗 [librechat.ai](https://librechat.ai/)  \n\n5. \u003ca href=\"https://github.com/plotly/dash\"\u003eplotly/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plotly/dash\"\u003edash\u003c/a\u003e\u003c/b\u003e ⭐ 24,412    \n   Data Apps \u0026 Dashboards for Python. No JavaScript Required.  \n   🔗 [plotly.com/dash](https://plotly.com/dash)  \n\n6. \u003ca href=\"https://github.com/matplotlib/matplotlib\"\u003ematplotlib/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/matplotlib/matplotlib\"\u003ematplotlib\u003c/a\u003e\u003c/b\u003e ⭐ 22,262    \n   matplotlib: plotting with Python  \n   🔗 [matplotlib.org/stable](https://matplotlib.org/stable/)  \n\n7. \u003ca href=\"https://github.com/bokeh/bokeh\"\u003ebokeh/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bokeh/bokeh\"\u003ebokeh\u003c/a\u003e\u003c/b\u003e ⭐ 20,307    \n   Interactive Data Visualization in the browser, from  Python  \n   🔗 [bokeh.org](https://bokeh.org)  \n\n8. \u003ca href=\"https://github.com/plotly/plotly.py\"\u003eplotly/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/plotly/plotly.py\"\u003eplotly.py\u003c/a\u003e\u003c/b\u003e ⭐ 18,208    \n   The interactive graphing library for Python ✨  \n   🔗 [plotly.com/python](https://plotly.com/python/)  \n\n9. \u003ca href=\"https://github.com/microsoft/data-formulator\"\u003emicrosoft/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/microsoft/data-formulator\"\u003edata-formulator\u003c/a\u003e\u003c/b\u003e ⭐ 14,772    \n   Transform data and create rich visualizations iteratively with AI  \n   🔗 [arxiv.org/abs/2408.16119](https://arxiv.org/abs/2408.16119)  \n\n10. \u003ca href=\"https://github.com/visgl/deck.gl\"\u003evisgl/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/visgl/deck.gl\"\u003edeck.gl\u003c/a\u003e\u003c/b\u003e ⭐ 13,775    \n   WebGL2 powered visualization framework  \n   🔗 [deck.gl](https://deck.gl)  \n\n11. \u003ca href=\"https://github.com/mwaskom/seaborn\"\u003emwaskom/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mwaskom/seaborn\"\u003eseaborn\u003c/a\u003e\u003c/b\u003e ⭐ 13,696    \n   Statistical data visualization in Python  \n   🔗 [seaborn.pydata.org](https://seaborn.pydata.org)  \n\n12. \u003ca href=\"https://github.com/nvidia/tensorrt-llm\"\u003envidia/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nvidia/tensorrt-llm\"\u003eTensorRT-LLM\u003c/a\u003e\u003c/b\u003e ⭐ 12,718    \n   TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performa...  \n   🔗 [nvidia.github.io/tensorrt-llm](https://nvidia.github.io/TensorRT-LLM)  \n\n13. \u003ca href=\"https://github.com/marceloprates/prettymaps\"\u003emarceloprates/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/marceloprates/prettymaps\"\u003eprettymaps\u003c/a\u003e\u003c/b\u003e ⭐ 12,109    \n   Draw pretty maps from OpenStreetMap data! Built with osmnx +matplotlib + shapely  \n   🔗 [prettymaps.streamlit.app](https://prettymaps.streamlit.app/)  \n\n14. \u003ca href=\"https://github.com/altair-viz/altair\"\u003ealtair-viz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/altair-viz/altair\"\u003ealtair\u003c/a\u003e\u003c/b\u003e ⭐ 10,222    \n   Declarative visualization library for Python  \n   🔗 [altair-viz.github.io](https://altair-viz.github.io/)  \n\n15. \u003ca href=\"https://github.com/renpy/renpy\"\u003erenpy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/renpy/renpy\"\u003erenpy\u003c/a\u003e\u003c/b\u003e ⭐ 6,169    \n   The Ren'Py Visual Novel Engine  \n   🔗 [www.renpy.org](http://www.renpy.org/)  \n\n16. \u003ca href=\"https://github.com/holoviz/panel\"\u003eholoviz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/holoviz/panel\"\u003epanel\u003c/a\u003e\u003c/b\u003e ⭐ 5,577    \n   Panel: The powerful data exploration \u0026 web app framework for Python  \n   🔗 [panel.holoviz.org](https://panel.holoviz.org)  \n\n17. \u003ca href=\"https://github.com/lux-org/lux\"\u003elux-org/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/lux-org/lux\"\u003elux\u003c/a\u003e\u003c/b\u003e ⭐ 5,369    \n   Automatically visualize your pandas dataframe via a single print! 📊 💡  \n\n18. \u003ca href=\"https://github.com/man-group/dtale\"\u003eman-group/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/man-group/dtale\"\u003edtale\u003c/a\u003e\u003c/b\u003e ⭐ 5,045    \n   Visualizer for pandas data structures  \n   🔗 [alphatechadmin.pythonanywhere.com](http://alphatechadmin.pythonanywhere.com)  \n\n19. \u003ca href=\"https://github.com/has2k1/plotnine\"\u003ehas2k1/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/has2k1/plotnine\"\u003eplotnine\u003c/a\u003e\u003c/b\u003e ⭐ 4,490    \n   A Grammar of Graphics for Python  \n   🔗 [plotnine.org](https://plotnine.org)  \n\n20. \u003ca href=\"https://github.com/pyqtgraph/pyqtgraph\"\u003epyqtgraph/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyqtgraph/pyqtgraph\"\u003epyqtgraph\u003c/a\u003e\u003c/b\u003e ⭐ 4,282    \n   Fast data visualization and GUI tools for scientific / engineering applications  \n   🔗 [www.pyqtgraph.org](https://www.pyqtgraph.org)  \n\n21. \u003ca href=\"https://github.com/residentmario/missingno\"\u003eresidentmario/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/residentmario/missingno\"\u003emissingno\u003c/a\u003e\u003c/b\u003e ⭐ 4,183    \n   missingno provides a small toolset of flexible and easy-to-use missing data visualizations and utilities that allows you to get a quick visual summary of the completeness (or lack thereof) of your dataset.  \n\n22. \u003ca href=\"https://github.com/mckinsey/vizro\"\u003emckinsey/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/mckinsey/vizro\"\u003evizro\u003c/a\u003e\u003c/b\u003e ⭐ 3,561    \n   Vizro is a low-code toolkit for building high-quality data visualization apps.  \n   🔗 [vizro.readthedocs.io/en/stable](https://vizro.readthedocs.io/en/stable/)  \n\n23. \u003ca href=\"https://github.com/pyvista/pyvista\"\u003epyvista/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyvista/pyvista\"\u003epyvista\u003c/a\u003e\u003c/b\u003e ⭐ 3,483    \n   3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)  \n   🔗 [docs.pyvista.org](https://docs.pyvista.org)  \n\n24. \u003ca href=\"https://github.com/ml-tooling/opyrator\"\u003eml-tooling/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ml-tooling/opyrator\"\u003eopyrator\u003c/a\u003e\u003c/b\u003e ⭐ 3,135    \n   🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.  \n   🔗 [opyrator-playground.mltooling.org](https://opyrator-playground.mltooling.org)  \n\n25. \u003ca href=\"https://github.com/netflix/flamescope\"\u003enetflix/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/netflix/flamescope\"\u003eflamescope\u003c/a\u003e\u003c/b\u003e ⭐ 3,097    \n   FlameScope is a visualization tool for exploring different time ranges as Flame Graphs.  \n\n26. \u003ca href=\"https://github.com/facebookresearch/hiplot\"\u003efacebookresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/facebookresearch/hiplot\"\u003ehiplot\u003c/a\u003e\u003c/b\u003e ⭐ 2,800    \n   HiPlot makes understanding high dimensional data easy  \n   🔗 [facebookresearch.github.io/hiplot](https://facebookresearch.github.io/hiplot/)  \n\n27. \u003ca href=\"https://github.com/napari/napari\"\u003enapari/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/napari/napari\"\u003enapari\u003c/a\u003e\u003c/b\u003e ⭐ 2,584    \n   A fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images.  \n   🔗 [napari.org](https://napari.org)  \n\n28. \u003ca href=\"https://github.com/holoviz/holoviz\"\u003eholoviz/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/holoviz/holoviz\"\u003eholoviz\u003c/a\u003e\u003c/b\u003e ⭐ 906    \n   High-level tools to simplify visualization in Python.  \n   🔗 [holoviz.org](https://holoviz.org/)  \n\n29. \u003ca href=\"https://github.com/hazyresearch/meerkat\"\u003ehazyresearch/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/hazyresearch/meerkat\"\u003emeerkat\u003c/a\u003e\u003c/b\u003e ⭐ 852    \n   Explore and understand your training and validation data.  \n\n30. \u003ca href=\"https://github.com/anvaka/word2vec-graph\"\u003eanvaka/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/anvaka/word2vec-graph\"\u003eword2vec-graph\u003c/a\u003e\u003c/b\u003e ⭐ 711    \n   Exploring word2vec embeddings as a graph of nearest neighbors  \n   🔗 [anvaka.github.io/pm/#/galaxy/word2vec-wiki?cx=-4651\u0026cy=4492\u0026cz=-1988\u0026lx=-0.0915\u0026ly=-0.9746\u0026lz=-0.2030\u0026lw=0.0237\u0026ml=300\u0026s=1.75\u0026l=1\u0026v=d50_clean_small](https://anvaka.github.io/pm/#/galaxy/word2vec-wiki?cx=-4651\u0026cy=4492\u0026cz=-1988\u0026lx=-0.0915\u0026ly=-0.9746\u0026lz=-0.2030\u0026lw=0.0237\u0026ml=300\u0026s=1.75\u0026l=1\u0026v=d50_clean_small)  \n\n## Web\n\nWeb related frameworks and libraries: webapp servers, WSGI, ASGI, asyncio, HTTP, REST, user management.  \n\n1. \u003ca href=\"https://github.com/tiangolo/fastapi\"\u003etiangolo/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tiangolo/fastapi\"\u003efastapi\u003c/a\u003e\u003c/b\u003e ⭐ 94,400    \n   FastAPI framework, high performance, easy to learn, fast to code, ready for production  \n   🔗 [fastapi.tiangolo.com](https://fastapi.tiangolo.com/)  \n\n2. \u003ca href=\"https://github.com/django/django\"\u003edjango/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/django/django\"\u003edjango\u003c/a\u003e\u003c/b\u003e ⭐ 86,553    \n   The Web framework for perfectionists with deadlines.  \n   🔗 [www.djangoproject.com](https://www.djangoproject.com/)  \n\n3. \u003ca href=\"https://github.com/sherlock-project/sherlock\"\u003esherlock-project/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/sherlock-project/sherlock\"\u003esherlock\u003c/a\u003e\u003c/b\u003e ⭐ 72,113    \n   Hunt down social media accounts by username across social networks  \n   🔗 [sherlockproject.xyz](https://sherlockproject.xyz)  \n\n4. \u003ca href=\"https://github.com/pallets/flask\"\u003epallets/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pallets/flask\"\u003eflask\u003c/a\u003e\u003c/b\u003e ⭐ 71,080    \n   The Python micro framework for building web applications.  \n   🔗 [flask.palletsprojects.com](https://flask.palletsprojects.com)  \n\n5. \u003ca href=\"https://github.com/psf/requests\"\u003epsf/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/psf/requests\"\u003erequests\u003c/a\u003e\u003c/b\u003e ⭐ 53,675    \n   A simple, yet elegant, HTTP library.  \n   🔗 [requests.readthedocs.io/en/latest](https://requests.readthedocs.io/en/latest/)  \n\n6. \u003ca href=\"https://github.com/reflex-dev/reflex\"\u003ereflex-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/reflex-dev/reflex\"\u003ereflex\u003c/a\u003e\u003c/b\u003e ⭐ 28,008    \n   🕸️ Web apps in pure Python 🐍  \n   🔗 [reflex.dev](https://reflex.dev)  \n\n7. \u003ca href=\"https://github.com/tornadoweb/tornado\"\u003etornadoweb/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tornadoweb/tornado\"\u003etornado\u003c/a\u003e\u003c/b\u003e ⭐ 22,432    \n   Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.  \n   🔗 [www.tornadoweb.org](http://www.tornadoweb.org/)  \n\n8. \u003ca href=\"https://github.com/vincigit00/scrapegraph-ai\"\u003evincigit00/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vincigit00/scrapegraph-ai\"\u003eScrapegraph-ai\u003c/a\u003e\u003c/b\u003e ⭐ 22,365    \n   ScrapeGraphAI is a web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents  \n   🔗 [scrapegraphai.com](https://scrapegraphai.com)  \n\n9. \u003ca href=\"https://github.com/wagtail/wagtail\"\u003ewagtail/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/wagtail/wagtail\"\u003ewagtail\u003c/a\u003e\u003c/b\u003e ⭐ 20,076    \n   A Django content management system focused on flexibility and user experience  \n   🔗 [wagtail.org](https://wagtail.org)  \n\n10. \u003ca href=\"https://github.com/pyscript/pyscript\"\u003epyscript/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyscript/pyscript\"\u003epyscript\u003c/a\u003e\u003c/b\u003e ⭐ 18,691    \n   A framework that allows users to create rich Python applications in the browser using HTML's interface and the power of Pyodide, WASM, and modern web technologies.  \n   🔗 [pyscript.net](https://pyscript.net/)  \n\n11. \u003ca href=\"https://github.com/huge-success/sanic\"\u003ehuge-success/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/huge-success/sanic\"\u003esanic\u003c/a\u003e\u003c/b\u003e ⭐ 18,630    \n    Accelerate your web app development  | Build fast. Run fast.  \n   🔗 [sanic.dev](https://sanic.dev)  \n\n12. \u003ca href=\"https://github.com/aio-libs/aiohttp\"\u003eaio-libs/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aio-libs/aiohttp\"\u003eaiohttp\u003c/a\u003e\u003c/b\u003e ⭐ 16,222    \n   Asynchronous HTTP client/server framework for asyncio and Python  \n   🔗 [docs.aiohttp.org](https://docs.aiohttp.org)  \n\n13. \u003ca href=\"https://github.com/flet-dev/flet\"\u003eflet-dev/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/flet-dev/flet\"\u003eflet\u003c/a\u003e\u003c/b\u003e ⭐ 15,387    \n   Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.  \n   🔗 [flet.dev](https://flet.dev)  \n\n14. \u003ca href=\"https://github.com/zauberzeug/nicegui\"\u003ezauberzeug/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/zauberzeug/nicegui\"\u003enicegui\u003c/a\u003e\u003c/b\u003e ⭐ 15,172    \n   Create web-based user interfaces with Python. The nice way.  \n   🔗 [nicegui.io](https://nicegui.io)  \n\n15. \u003ca href=\"https://github.com/encode/httpx\"\u003eencode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/encode/httpx\"\u003ehttpx\u003c/a\u003e\u003c/b\u003e ⭐ 14,932    \n   A next generation HTTP client for Python. 🦋  \n   🔗 [www.python-httpx.org](https://www.python-httpx.org/)  \n\n16. \u003ca href=\"https://github.com/getpelican/pelican\"\u003egetpelican/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/getpelican/pelican\"\u003epelican\u003c/a\u003e\u003c/b\u003e ⭐ 13,195    \n   Static site generator that supports Markdown and reST syntax. Powered by Python.  \n   🔗 [getpelican.com](https://getpelican.com)  \n\n17. \u003ca href=\"https://github.com/encode/starlette\"\u003eencode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/encode/starlette\"\u003estarlette\u003c/a\u003e\u003c/b\u003e ⭐ 11,863    \n   The little ASGI framework that shines. 🌟  \n   🔗 [starlette.dev](https://starlette.dev)  \n\n18. \u003ca href=\"https://github.com/aws/chalice\"\u003eaws/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aws/chalice\"\u003echalice\u003c/a\u003e\u003c/b\u003e ⭐ 11,053    \n   Python Serverless Microframework for AWS  \n\n19. \u003ca href=\"https://github.com/benoitc/gunicorn\"\u003ebenoitc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/benoitc/gunicorn\"\u003egunicorn\u003c/a\u003e\u003c/b\u003e ⭐ 10,404    \n   gunicorn 'Green Unicorn' is a WSGI HTTP Server for UNIX, fast clients and sleepy applications.  \n   🔗 [www.gunicorn.org](http://www.gunicorn.org)  \n\n20. \u003ca href=\"https://github.com/encode/uvicorn\"\u003eencode/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/encode/uvicorn\"\u003euvicorn\u003c/a\u003e\u003c/b\u003e ⭐ 10,325    \n   An ASGI web server, for Python. 🦄  \n   🔗 [uvicorn.dev](https://uvicorn.dev)  \n\n21. \u003ca href=\"https://github.com/falconry/falcon\"\u003efalconry/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/falconry/falcon\"\u003efalcon\u003c/a\u003e\u003c/b\u003e ⭐ 9,780    \n   The no-magic web API and microservices framework for Python developers, with a focus on reliability and performance at scale.  \n   🔗 [falcon.readthedocs.io](https://falcon.readthedocs.io)  \n\n22. \u003ca href=\"https://github.com/vitalik/django-ninja\"\u003evitalik/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/vitalik/django-ninja\"\u003edjango-ninja\u003c/a\u003e\u003c/b\u003e ⭐ 8,856    \n   💨  Fast, Async-ready, Openapi, type hints based framework for building APIs  \n   🔗 [django-ninja.dev](https://django-ninja.dev)  \n\n23. \u003ca href=\"https://github.com/bottlepy/bottle\"\u003ebottlepy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/bottlepy/bottle\"\u003ebottle\u003c/a\u003e\u003c/b\u003e ⭐ 8,734    \n   bottle.py is a fast and simple micro-framework for python web-applications.  \n   🔗 [bottlepy.org](http://bottlepy.org/)  \n\n24. \u003ca href=\"https://github.com/graphql-python/graphene\"\u003egraphql-python/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/graphql-python/graphene\"\u003egraphene\u003c/a\u003e\u003c/b\u003e ⭐ 8,244    \n   GraphQL framework for Python  \n   🔗 [graphene-python.org](http://graphene-python.org/)  \n\n25. \u003ca href=\"https://github.com/reactive-python/reactpy\"\u003ereactive-python/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/reactive-python/reactpy\"\u003ereactpy\u003c/a\u003e\u003c/b\u003e ⭐ 8,154    \n   ReactPy is a library for building user interfaces in Python without Javascript  \n   🔗 [reactpy.dev](https://reactpy.dev)  \n\n26. \u003ca href=\"https://github.com/starlite-api/starlite\"\u003estarlite-api/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/starlite-api/starlite\"\u003elitestar\u003c/a\u003e\u003c/b\u003e ⭐ 7,934    \n   Light, flexible and extensible ASGI framework | Built to scale  \n   🔗 [docs.litestar.dev](https://docs.litestar.dev/)  \n\n27. \u003ca href=\"https://github.com/pallets/werkzeug\"\u003epallets/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pallets/werkzeug\"\u003ewerkzeug\u003c/a\u003e\u003c/b\u003e ⭐ 6,830    \n   The comprehensive WSGI web application library.  \n   🔗 [werkzeug.palletsprojects.com](https://werkzeug.palletsprojects.com)  \n\n28. \u003ca href=\"https://github.com/pyeve/eve\"\u003epyeve/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pyeve/eve\"\u003eeve\u003c/a\u003e\u003c/b\u003e ⭐ 6,744    \n   REST API framework designed for human beings  \n   🔗 [python-eve.org](https://python-eve.org)  \n\n29. \u003ca href=\"https://github.com/fastapi-users/fastapi-users\"\u003efastapi-users/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fastapi-users/fastapi-users\"\u003efastapi-users\u003c/a\u003e\u003c/b\u003e ⭐ 5,948    \n   Ready-to-use and customizable users management for FastAPI  \n   🔗 [fastapi-users.github.io/fastapi-users](https://fastapi-users.github.io/fastapi-users/)  \n\n30. \u003ca href=\"https://github.com/webpy/webpy\"\u003ewebpy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/webpy/webpy\"\u003ewebpy\u003c/a\u003e\u003c/b\u003e ⭐ 5,933    \n   web.py is a web framework for python that is as simple as it is powerful.   \n   🔗 [webpy.org](http://webpy.org)  \n\n31. \u003ca href=\"https://github.com/pywebio/pywebio\"\u003epywebio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pywebio/pywebio\"\u003ePyWebIO\u003c/a\u003e\u003c/b\u003e ⭐ 4,822    \n   Write interactive web app in script way.  \n   🔗 [pywebio.readthedocs.io](https://pywebio.readthedocs.io)  \n\n32. \u003ca href=\"https://github.com/nameko/nameko\"\u003enameko/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nameko/nameko\"\u003enameko\u003c/a\u003e\u003c/b\u003e ⭐ 4,765    \n   A microservices framework for Python that lets service developers concentrate on application logic and encourages testability.  \n   🔗 [www.nameko.io](https://www.nameko.io)  \n\n33. \u003ca href=\"https://github.com/strawberry-graphql/strawberry\"\u003estrawberry-graphql/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/strawberry-graphql/strawberry\"\u003estrawberry\u003c/a\u003e\u003c/b\u003e ⭐ 4,590    \n   A GraphQL library for Python that leverages type annotations 🍓  \n   🔗 [strawberry.rocks](https://strawberry.rocks)  \n\n34. \u003ca href=\"https://github.com/freddyaboulton/fastrtc\"\u003efreddyaboulton/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/freddyaboulton/fastrtc\"\u003efastrtc\u003c/a\u003e\u003c/b\u003e ⭐ 4,500    \n   Turn any python function into a real-time audio and video stream over WebRTC or WebSockets.  \n   🔗 [fastrtc.org](https://fastrtc.org/)  \n\n35. \u003ca href=\"https://github.com/h2oai/wave\"\u003eh2oai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/h2oai/wave\"\u003ewave\u003c/a\u003e\u003c/b\u003e ⭐ 4,223    \n   H2O Wave is a software stack for building beautiful, low-latency, realtime, browser-based applications and dashboards entirely in Python/R without using HTML, Javascript, or CSS.  \n   🔗 [wave.h2o.ai](https://wave.h2o.ai)  \n\n36. \u003ca href=\"https://github.com/fastapi-admin/fastapi-admin\"\u003efastapi-admin/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/fastapi-admin/fastapi-admin\"\u003efastapi-admin\u003c/a\u003e\u003c/b\u003e ⭐ 3,684    \n   A fast admin dashboard based on FastAPI and TortoiseORM with tabler ui, inspired by Django admin  \n   🔗 [fastapi-admin-docs.long2ice.io](https://fastapi-admin-docs.long2ice.io)  \n\n37. \u003ca href=\"https://github.com/pallets/quart\"\u003epallets/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/pallets/quart\"\u003equart\u003c/a\u003e\u003c/b\u003e ⭐ 3,577    \n   An async Python micro framework for building web applications.   \n   🔗 [quart.palletsprojects.com](https://quart.palletsprojects.com)  \n\n38. \u003ca href=\"https://github.com/s3rius/fastapi-template\"\u003es3rius/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/s3rius/fastapi-template\"\u003eFastAPI-template\u003c/a\u003e\u003c/b\u003e ⭐ 2,716    \n   Feature rich robust FastAPI template.  \n\n39. \u003ca href=\"https://github.com/flipkart-incubator/astra\"\u003eflipkart-incubator/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/flipkart-incubator/astra\"\u003eAstra\u003c/a\u003e\u003c/b\u003e ⭐ 2,629    \n   Automated Security Testing For REST API's  \n\n40. \u003ca href=\"https://github.com/dot-agent/nextpy\"\u003edot-agent/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dot-agent/nextpy\"\u003enextpy\u003c/a\u003e\u003c/b\u003e ⭐ 2,335    \n   🤖Self-Modifying Framework from the Future 🔮 World's First AMS  \n   🔗 [dotagent.ai](https://dotagent.ai)  \n\n41. \u003ca href=\"https://github.com/neoteroi/blacksheep\"\u003eneoteroi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/neoteroi/blacksheep\"\u003eBlackSheep\u003c/a\u003e\u003c/b\u003e ⭐ 2,302    \n   Fast ASGI web framework for Python  \n   🔗 [www.neoteroi.dev/blacksheep](https://www.neoteroi.dev/blacksheep/)  \n\n42. \u003ca href=\"https://github.com/dmontagu/fastapi-utils\"\u003edmontagu/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dmontagu/fastapi-utils\"\u003efastapi-utils\u003c/a\u003e\u003c/b\u003e ⭐ 2,297    \n   Reusable utilities for FastAPI: a number of utilities to help reduce boilerplate and reuse common functionality across projects  \n   🔗 [fastapiutils.github.io/fastapi-utils](https://fastapiutils.github.io/fastapi-utils/)  \n\n43. \u003ca href=\"https://github.com/python-restx/flask-restx\"\u003epython-restx/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/python-restx/flask-restx\"\u003eflask-restx\u003c/a\u003e\u003c/b\u003e ⭐ 2,240    \n   Fork of Flask-RESTPlus: Fully featured framework for fast, easy and documented API development with Flask  \n   🔗 [flask-restx.readthedocs.io/en/latest](https://flask-restx.readthedocs.io/en/latest/)  \n\n44. \u003ca href=\"https://github.com/jordaneremieff/mangum\"\u003ejordaneremieff/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/jordaneremieff/mangum\"\u003emangum\u003c/a\u003e\u003c/b\u003e ⭐ 2,059    \n   An adapter for running ASGI applications in AWS Lambda to handle Function URL, API Gateway, ALB, and Lambda@Edge events  \n   🔗 [mangum.fastapiexpert.com](http://mangum.fastapiexpert.com/)  \n\n45. \u003ca href=\"https://github.com/long2ice/fastapi-cache\"\u003elong2ice/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/long2ice/fastapi-cache\"\u003efastapi-cache\u003c/a\u003e\u003c/b\u003e ⭐ 1,812    \n   fastapi-cache is a tool to cache fastapi response and function result, with backends support redis and memcached.  \n   🔗 [github.com/long2ice/fastapi-cache](https://github.com/long2ice/fastapi-cache)  \n\n46. \u003ca href=\"https://github.com/rstudio/py-shiny\"\u003erstudio/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rstudio/py-shiny\"\u003epy-shiny\u003c/a\u003e\u003c/b\u003e ⭐ 1,678    \n   Shiny for Python  \n   🔗 [shiny.posit.co/py](https://shiny.posit.co/py/)  \n\n47. \u003ca href=\"https://github.com/awtkns/fastapi-crudrouter\"\u003eawtkns/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/awtkns/fastapi-crudrouter\"\u003efastapi-crudrouter\u003c/a\u003e\u003c/b\u003e ⭐ 1,676    \n   A dynamic FastAPI router that automatically creates CRUD routes for your models  \n   🔗 [fastapi-crudrouter.awtkns.com](https://fastapi-crudrouter.awtkns.com)  \n\n48. \u003ca href=\"https://github.com/whitphx/stlite\"\u003ewhitphx/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/whitphx/stlite\"\u003estlite\u003c/a\u003e\u003c/b\u003e ⭐ 1,583    \n   A port of Streamlit to WebAssembly, powered by Pyodide.  \n   🔗 [edit.share.stlite.net](https://edit.share.stlite.net)  \n\n---  \n\nInteractive version: [www.awesomepython.org](https://www.awesomepython.org/), Hugging Face Dataset: [awesome-python](https://huggingface.co/datasets/dylanhogg/awesome-python)  \n\n\nPlease raise \u003ca href=\"https://github.com/dylanhogg/awesome-python/issues\"\u003ea new issue\u003c/a\u003e to suggest a Python repo that you would like to see added.  \n\n\n1,553 hand-picked awesome Python libraries and frameworks, updated 11 Feb 2026","created_at":"2024-01-14T14:30:55.068Z","updated_at":"2026-04-07T02:00:22.907Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Data","Game Development","Utility","Performance","Machine Learning - Ops","Web","Vizualisation","Typing","LLMs and ChatGPT","Machine Learning - General","Machine Learning - Deep Learning","Agentic AI","Simulation","Machine Learning - Interpretability","Code Quality","Diffusion Text to Image","Security","Profiling","Natural Language Processing","Study","Finance","Terminal","Packaging","Testing","Machine Learning - Reinforcement","Crypto and Blockchain","Pandas","Debugging","GIS","Graph","GUI","Jupyter","Math and Science","Template","Machine Learning - Time Series","Newly Created Repositories"],"sub_categories":[],"projects_url":"https://awesome.ecosyste.ms/api/v1/lists/dylanhogg%2Fawesome-python/projects"}