{"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":1013,"last_synced_at":"2026-06-11T04:00:27.416Z","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":"2026-04-20T12:35:01.000Z","size":104914,"stargazers_count":460,"open_issues_count":12,"forks_count":40,"subscribers_count":14,"default_branch":"main","last_synced_at":"2026-05-25T13:03:32.959Z","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":"2026-05-24T10:58:02.000Z","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":34181555,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-11T02:00:06.485Z","response_time":57,"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"}},"created_at":"2024-01-14T14:30:55.068Z","updated_at":"2026-06-11T04:00:27.417Z","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":[],"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 19 Apr 2026 with 1,556 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 (119 repos)\n- [Code Quality](#code-quality) - Code quality tooling: linters, formatters, pre-commit hooks, unused code removal (15 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 (80 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 (39 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 (18 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 (347 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 (142 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 (49 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 (73 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 (18 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 (14 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 (33 repos)\n- [Study](#study) - Miscellaneous study resources: algorithms, general resources, system design, code repos for textbooks, best practices, tutorials (62 repos)\n- [Template](#template) - Template tools and libraries: cookiecutter repos, generators, quick-starts (10 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 (135 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 (47 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/karpathy/autoresearch\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/autoresearch\"\u003eautoresearch\u003c/a\u003e\u003c/b\u003e ⭐ 70,645    \n   AI agents running research on single-GPU nanochat training automatically  \n\n2. \u003ca href=\"https://github.com/safishamsi/graphify\"\u003esafishamsi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/safishamsi/graphify\"\u003egraphify\u003c/a\u003e\u003c/b\u003e ⭐ 22,501    \n   AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, OpenClaw, Factory Droid, Trae). Turn any folder of code, docs, papers, images, videos, or YouTube links into a queryable knowledge graph  \n   🔗 [safishamsi.github.io/penpax.ai](https://safishamsi.github.io/penpax.ai)  \n\n3. \u003ca href=\"https://github.com/aiming-lab/autoresearchclaw\"\u003eaiming-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aiming-lab/autoresearchclaw\"\u003eAutoResearchClaw\u003c/a\u003e\u003c/b\u003e ⭐ 10,973    \n   Fully autonomous \u0026 self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞  \n\n4. \u003ca href=\"https://github.com/nicobailon/visual-explainer\"\u003enicobailon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nicobailon/visual-explainer\"\u003evisual-explainer\u003c/a\u003e\u003c/b\u003e ⭐ 7,469    \n   Agent skill that generates rich HTML pages or slide decks for diagrams, diff reviews, plan audits, data tables, and project recaps  \n\n5. \u003ca href=\"https://github.com/tw93/waza\"\u003etw93/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tw93/waza\"\u003eWaza\u003c/a\u003e\u003c/b\u003e ⭐ 2,633    \n   🥷 Engineering habits you already know, turned into skills Claude can run.  \n   🔗 [x.com/hitw93/status/2041312649510822103](https://x.com/HiTw93/status/2041312649510822103)  \n\n6. \u003ca href=\"https://github.com/miolini/autoresearch-macos\"\u003emiolini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/miolini/autoresearch-macos\"\u003eautoresearch-macos\u003c/a\u003e\u003c/b\u003e ⭐ 1,929    \n   AI agents running research on single-GPU nanochat training automatically adopted for MacOS  \n\n7. \u003ca href=\"https://github.com/rightnow-ai/autokernel\"\u003erightnow-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rightnow-ai/autokernel\"\u003eautokernel\u003c/a\u003e\u003c/b\u003e ⭐ 1,185    \n   Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.  \n   🔗 [www.rightnowai.co/forge](https://www.rightnowai.co/forge)  \n\n8. \u003ca href=\"https://github.com/math-inc/opengauss\"\u003emath-inc/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/math-inc/opengauss\"\u003eOpenGauss\u003c/a\u003e\u003c/b\u003e ⭐ 1,167    \n   A project-scoped Lean workflow orchestrator from Math, Inc. It gives gauss a multi-agent frontend for the lean4-skills prove, draft, review, checkpoint, refactor, golf, autoprove, formalize, and autoformalize workflows  \n\n9. \u003ca href=\"https://github.com/ddickmann/vllm-factory\"\u003eddickmann/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/ddickmann/vllm-factory\"\u003evllm-factory\u003c/a\u003e\u003c/b\u003e ⭐ 31    \n   Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment  \n\n10. \u003ca href=\"https://github.com/slavadubrov/ner-field-guide\"\u003eslavadubrov/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/slavadubrov/ner-field-guide\"\u003ener-field-guide\u003c/a\u003e\u003c/b\u003e ⭐ 3    \n   Companion code for The Definitive Guide to NER in 2026  \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/karpathy/autoresearch\"\u003ekarpathy/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/karpathy/autoresearch\"\u003eautoresearch\u003c/a\u003e\u003c/b\u003e ⭐ 70,645    \n   AI agents running research on single-GPU nanochat training automatically  \n\n6. \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\n7. \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\n8. \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\n9. \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\n10. \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\n11. \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\n12. \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\n13. \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\n14. \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\n15. \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\n16. \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\n17. \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\n18. \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\n19. \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\n20. \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\n21. \u003ca href=\"https://github.com/safishamsi/graphify\"\u003esafishamsi/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/safishamsi/graphify\"\u003egraphify\u003c/a\u003e\u003c/b\u003e ⭐ 22,501    \n   AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, OpenClaw, Factory Droid, Trae). Turn any folder of code, docs, papers, images, videos, or YouTube links into a queryable knowledge graph  \n   🔗 [safishamsi.github.io/penpax.ai](https://safishamsi.github.io/penpax.ai)  \n\n22. \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\n23. \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\n24. \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\n25. \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\n26. \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\n27. \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\n28. \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\n29. \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\n30. \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\n31. \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\n32. \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\n33. \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\n34. \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\n35. \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\n36. \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\n37. \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\n38. \u003ca href=\"https://github.com/voltagent/awesome-agent-skills\"\u003evoltagent/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/voltagent/awesome-agent-skills\"\u003eawesome-agent-skills\u003c/a\u003e\u003c/b\u003e ⭐ 15,232    \n   A curated collection of 1000+ agent skills from official dev teams and the community, compatible with Claude Code, Codex, Gemini CLI, Cursor, and more.  \n   🔗 [officialskills.sh](https://officialskills.sh/)  \n\n39. \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\n40. \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\n41. \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\n42. \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\n43. \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\n44. \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\n45. \u003ca href=\"https://github.com/aiming-lab/autoresearchclaw\"\u003eaiming-lab/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/aiming-lab/autoresearchclaw\"\u003eAutoResearchClaw\u003c/a\u003e\u003c/b\u003e ⭐ 10,973    \n   Fully autonomous \u0026 self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞  \n\n46. \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\n47. \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\n48. \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\n49. \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\n50. \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\n51. \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\n52. \u003ca href=\"https://github.com/nicobailon/visual-explainer\"\u003enicobailon/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/nicobailon/visual-explainer\"\u003evisual-explainer\u003c/a\u003e\u003c/b\u003e ⭐ 7,469    \n   Agent skill that generates rich HTML pages or slide decks for diagrams, diff reviews, plan audits, data tables, and project recaps  \n\n53. \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\n54. \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\n55. \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\n56. \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\n57. \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\n58. \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\n59. \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\n60. \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\n61. \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\n62. \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\n63. \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\n64. \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\n65. \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\n66. \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\n67. \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\n68. \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\n69. \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\n70. \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\n71. \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\n72. \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\n73. \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\n74. \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\n75. \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\n76. \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\n77. \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\n78. \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\n79. \u003ca href=\"https://github.com/tw93/waza\"\u003etw93/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/tw93/waza\"\u003eWaza\u003c/a\u003e\u003c/b\u003e ⭐ 2,633    \n   🥷 Engineering habits you already know, turned into skills Claude can run.  \n   🔗 [x.com/hitw93/status/2041312649510822103](https://x.com/HiTw93/status/2041312649510822103)  \n\n80. \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\n81. \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\n82. \u003ca href=\"https://github.com/dimensionalos/dimos\"\u003edimensionalos/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/dimensionalos/dimos\"\u003edimos\u003c/a\u003e\u003c/b\u003e ⭐ 2,588    \n   Dimensional is the agentic operating system for physical space. Vibecode humanoids, quadrupeds, drones, and other hardware platforms in natural language and build multi-agent systems that work seamlessly with physical input (cameras, lidar, actuators).  \n   🔗 [dimensionalos.com](https://dimensionalos.com/)  \n\n83. \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\n84. \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\n85. \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\n86. \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\n87. \u003ca href=\"https://github.com/researai/deepscientist\"\u003eresearai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/researai/deepscientist\"\u003eDeepScientist\u003c/a\u003e\u003c/b\u003e ⭐ 2,077    \n   Now, Stronger AI Pushes Frontiers, Stronger Our Shared Future.  \n\n88. \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\n89. \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\n90. \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\n91. \u003ca href=\"https://github.com/miolini/autoresearch-macos\"\u003emiolini/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/miolini/autoresearch-macos\"\u003eautoresearch-macos\u003c/a\u003e\u003c/b\u003e ⭐ 1,929    \n   AI agents running research on single-GPU nanochat training automatically adopted for MacOS  \n\n92. \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\n93. \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\n94. \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\n95. \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\n96. \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\n97. \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\n98. \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\n99. \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\n100. \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\n101. \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\n102. \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\n103. \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\n104. \u003ca href=\"https://github.com/rightnow-ai/autokernel\"\u003erightnow-ai/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/rightnow-ai/autokernel\"\u003eautokernel\u003c/a\u003e\u003c/b\u003e ⭐ 1,185    \n   Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels.  \n   🔗 [www.rightnowai.co/forge](https://www.rightnowai.co/forge)  \n\n105. \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\n106. \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\n107. \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\n108. \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\n109. \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\n110. \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\n111. \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\n112. \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\n113. \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\n114. \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\n115. \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\n116. \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\n117. \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\n118. \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\n119. \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/peteromallet/desloppify\"\u003epeteromallet/\u003c/a\u003e\u003cb\u003e\u003ca href=\"https://github.com/peteromallet/desloppify\"\u003edesloppify\u003c/a\u003e\u003c/b\u003e ⭐ 2,717    \n   Agent harness to make your slop code well-engineered and beautiful.  \n   🔗 [desloppify.it](https://desloppify.it/)  \n\n14. \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\n15. \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/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\n66. \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\n67. \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\n68. \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\n69. \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\n70. \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\n71. \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\n72. \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\n73. \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\n74. \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\n75. \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\n76. \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\n77. \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\n78. \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\n79. \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\n80. \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/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 ","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/dylanhogg%2Fawesome-python/projects"}