{"id":24067942,"url":"https://github.com/kiln-ai/kiln","last_synced_at":"2026-10-27T20:21:53.535Z","repository":{"id":251438856,"uuid":"832879402","full_name":"Kiln-AI/Kiln","owner":"Kiln-AI","description":"Build, Evaluate, and Optimize AI Systems. 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Fine-Tuning","Tools"],"sub_categories":["Training Frameworks","Training and Fine-tuning"],"readme":"\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://kiln.tech\"\u003e\n        \u003cpicture\u003e\n            \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://github.com/user-attachments/assets/faae475e-7ace-443b-91f3-0e3701f0c90d\"\u003e\n            \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://github.com/user-attachments/assets/4ca9b69f-1c90-43a4-8d2e-13de4eb2ee9c\"\u003e\n            \u003cimg width=\"205\" alt=\"Kiln AI Logo\" src=\"https://github.com/user-attachments/assets/4ca9b69f-1c90-43a4-8d2e-13de4eb2ee9c\"\u003e\n        \u003c/picture\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n\n\u003ch3 align=\"center\"\u003e\n  A free app and open-source library to build better AI products.\n\u003c/h3\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://kiln.tech#demo\"\u003e\n    \u003cimg width=\"420\" alt=\"Kiln AI Animated Preview\" src=\"https://github.com/user-attachments/assets/56ac04ea-010b-40bf-851c-ec5e05965336\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://kiln.tech/download\"\u003e\u003cimg width=\"180\" src=\"https://github.com/user-attachments/assets/a5d51b8b-b30a-4a16-a902-ab6ef1d58dc0\" alt=\"Download Kiln\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://docs.kiln.tech\"\u003e\u003cimg width=\"180\" src=\"https://github.com/user-attachments/assets/aff1b35f-72c0-4286-9b28-40a415558359\" alt=\"Read the Docs\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#highlights\"\u003e\u003cstrong\u003eHighlights\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/evaluations\"\u003e\u003cstrong\u003eEvals\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/prompts/automatic-prompt-optimizer\"\u003e\u003cstrong\u003eAuto-Optimize\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/documents-and-search-rag\"\u003e\u003cstrong\u003eRAG\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/agents\"\u003e\u003cstrong\u003eAgents\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/fine-tuning-guide\"\u003e\u003cstrong\u003eFine-Tuning\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech/docs/synthetic-data-generation\"\u003e\u003cstrong\u003eSynthetic Data\u003c/strong\u003e\u003c/a\u003e •\n  \u003ca href=\"https://docs.kiln.tech\"\u003e\u003cstrong\u003eAll Docs\u003c/strong\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/Kiln-AI/kiln/actions/workflows/build_and_test.yml\"\u003e\u003cimg src=\"https://github.com/Kiln-AI/kiln/actions/workflows/build_and_test.yml/badge.svg\" alt=\"Build and Test\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/kiln-ai/\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/kiln-ai.svg?logo=pypi\u0026label=PyPI\u0026logoColor=gold\" alt=\"PyPI\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://kiln.tech/discord\"\u003e\u003cimg src=\"https://img.shields.io/badge/Discord-Kiln_AI-blue?logo=Discord\u0026logoColor=white\" alt=\"Discord\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n## What is Kiln?\n\nKiln is a workbench for the full AI development loop: evals, optimization, prompts, RAG, fine-tuning, synthetic data, agents, and tools - all working together. The desktop app lets your whole team contribute (PMs, subject-experts, and QA can rate outputs and add data without writing code). The MIT-licensed Python library ships the same tasks to production. Runs locally - bring your own API keys, or go fully offline with Ollama.\n\n## Highlights\n\n### Iterate, optimize, and collaborate\n\n- 🖥️ [**Intuitive app**](https://kiln.tech/download) - Easy-to-use apps for Mac, Windows, and Linux. One-click install.\n- 📊 [**Eval Builder**](https://docs.kiln.tech/docs/evaluations) - Auto-generate evals (judge + synthetic eval dataset), and align to your preference in ~10 minutes.\n- 🚀 [**Auto-Optimize**](https://docs.kiln.tech/docs/prompts/automatic-prompt-optimizer) - Automatically find the best way to run your AI task, optimizing prompt, model selection, tools, skills, subagents, parameters, and more.\n- 💬 [**AI Assistant**](https://docs.kiln.tech) - Your AI data-science partner. Kiln Assistant proposes improvements, optimizes prompts, runs experiments, creates evals, and more.\n- 🤝 [**Git-native collaboration**](https://docs.kiln.tech/docs/collaboration) - The app syncs to Git automatically — even for teammates who don't know what Git is.\n\n### Build \u0026 ship agents\n\n- 🔍 [**RAG**](https://docs.kiln.tech/docs/documents-and-search-rag) - Drag-and-drop docs (PDF, image, video, audio) to create a RAG. Auto-generated RAG evals from your own documents.\n- 🤖 [**Subagents**](https://docs.kiln.tech/docs/agents) - Compose multi-agent hierarchies. Each runs in its own focused context window.\n- 🪄 [**Synthetic Data Generation**](https://docs.kiln.tech/docs/synthetic-data-generation) - Generate data for evals or fine-tuning in minutes.\n- 🎛️ [**Fine-Tuning**](https://docs.kiln.tech/docs/fine-tuning-guide) - Zero-code fine-tuning across 60+ models (Qwen, Llama, GPT, Gemini, …) on Fireworks, Together, and Vertex. Serverless deployment included.\n- 🐍 [**Open Python library**](https://docs.kiln.tech/developers/python-library-quickstart) - Agents built in the app can be deployed to production. MIT open-source.\n- 🧰 [**…and more**](https://docs.kiln.tech) - Tools \u0026 MCP, Skills, structured outputs, reasoning models, model library (190+ tested).\n\n## App Quickstart\n\nGet started in minutes - one-click install.\n\nDownload Kiln Desktop for macOS, Windows, or Linux, then follow the [5-minute quickstart](https://docs.kiln.tech/getting-started/quickstart) to run your first task.\n\n[![MacOS](https://img.shields.io/badge/MacOS-black?logo=apple)](https://kiln.tech/download) [![Windows](https://img.shields.io/badge/Windows-0067b8.svg?logo=data:image/svg%2bxml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0idXRmLTgiPz4KPHN2ZyBmaWxsPSIjZmZmIiB2aWV3Qm94PSIwIDAgMzIgMzIiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTE2Ljc0MiAxNi43NDJ2MTQuMjUzaDE0LjI1M3YtMTQuMjUzek0xLjAwNCAxNi43NDJ2MTQuMjUzaDE0LjI1NnYtMTQuMjUzek0xNi43NDIgMS4wMDR2MTQuMjU2aDE0LjI1M3YtMTQuMjU2ek0xLjAwNCAxLjAwNHYxNC4yNTZoMTQuMjU2di0xNC4yNTZ6Ij48L3BhdGg+Cjwvc3ZnPg==)](https://kiln.tech/download) [![Linux](https://img.shields.io/badge/Linux-444444?logo=linux\u0026logoColor=ffffff)](https://kiln.tech/download)\n\nPrefer to start in code? See the [Python library quickstart](https://docs.kiln.tech/developers/python-library-quickstart).\n\n## Demo\n\n[Watch a 2-minute overview](https://kiln.tech#demo), or our [end-to-end project demo (20 minutes)](https://docs.kiln.tech/docs/end-to-end-project-demo).\n\n## Why Kiln?\n\nMost AI tooling forces a tradeoff: a code-only framework that covers one slice (orchestration *or* evals *or* RAG), or a paid SaaS that locks in your data and can't be extended. Kiln is a free, local-first workbench where a single task and dataset flow through evals, prompt optimization, fine-tuning, RAG, agents, and synthetic data — all in one tool.\n\n- **One dataset, every technique.** Define a task once. Eval it, optimize the prompt, fine-tune a model, generate synthetic data, add RAG — all against the same dataset, with results that compound across stages.\n\n- **Track every axis. Move fast. Don't regress.** Keeping agents running well is hard — a prompt change quietly regresses behavior three steps downstream; a model upgrade improves five things and breaks two. Kiln tracks quality across every dimension you care about, so you iterate without breaking what already works.\n\n  \u003cp align=\"center\"\u003e\n    \u003cimg width=\"600\" alt=\"Kiln optimization across iterations\" src=\"https://github.com/user-attachments/assets/5517b33b-74dd-444a-9f40-6a9c6d8a1ffc\"\u003e\n  \u003c/p\u003e\n\n- **Optimization, not just evaluation.** Other tools tell you how a prompt scores, but not how to fix it. Kiln's Auto-Optimize searches across hundreds of prompt mutations and models to find what works best for every eval dimension.\n\n- **GUI for the whole team, library for engineers.** Kiln's desktop app lets PMs rate outputs, SMEs add training examples, and QA flag regressions — without a terminal. Engineers ship the same tasks via an MIT-licensed Python library. Data scientists can use the library in notebooks and experiments.\n\n- **Local-first.** Most AI platforms are SaaS-only. Kiln runs entirely on your machine. Bring your own API keys, or go fully offline with Ollama. Your data never leaves your control. Team-sync is provided via Git infrastructure you already own.\n\n- **190+ models tested across every provider.** Skip the guesswork — we've tested every model's capabilities across all major providers. OpenAI, Anthropic, Gemini, Bedrock, Ollama, OpenRouter, Fireworks, Groq, any OpenAI-compatible endpoint, and more. Swap models with confidence.\n\n## Open-source Python Library\n\nBuild AI tasks in the app. Deploy with the open-source library. Same engine, same project files, no rewrite. The MIT-licensed `kiln-ai` library is the same library used in the app. Load Kiln projects, run tasks, build fine-tunes, work in notebooks, integrate Pandas/Polars dataframes, and more.\n\n```bash\npip install kiln-ai\n```\n\n[📚 Library docs](https://docs.kiln.tech/developers/python-library-quickstart) · [REST API](https://docs.kiln.tech/developers/rest-api) · [PyPI](https://pypi.org/project/kiln-ai/)\n\n## Docs\n\nFull docs at [docs.kiln.tech](https://docs.kiln.tech). Common starting points:\n\n- [Quickstart](https://docs.kiln.tech/getting-started/quickstart) — run your first task in 5 minutes\n- [Evals](https://docs.kiln.tech/docs/evaluations)\n- [Auto-Optimize](https://docs.kiln.tech/docs/prompts/automatic-prompt-optimizer)\n- [RAG](https://docs.kiln.tech/docs/documents-and-search-rag)\n- [Agents](https://docs.kiln.tech/docs/agents)\n- [Fine-Tuning](https://docs.kiln.tech/docs/fine-tuning-guide)\n- [Python Library](https://docs.kiln.tech/developers/python-library-quickstart)\n- [End-to-end project demo](https://docs.kiln.tech/docs/end-to-end-project-demo) (20-min video)\n\n## Community\n\n- Chat with the community on [Discord](https://kiln.tech/discord).\n- Subscribe to the [newsletter](https://kiln.tech/blog) for new features.\n- File issues, request features, or open a discussion on [GitHub](https://github.com/Kiln-AI/Kiln/issues).\n\n## Contributing\n\nSee [CONTRIBUTING.md](.github/CONTRIBUTING.md) for development setup and contribution guidelines.\n\n## License \u0026 Trademarks\n\nKiln's core Python library and REST server are [MIT-licensed](libs/core/LICENSE.txt). The desktop app is [source-available](app), free to use, and built on the [fair-code](https://faircode.io) model — so Kiln stays free for individuals while remaining sustainable.\n\nDatasets are open JSON. You own and control your datasets.\n\n[Kiln Pro](https://kiln.tech/pricing) is our service that adds the AI Assistant, Auto-Optimize, and the Eval Builder. It's opt-in, and the core Kiln app remains fully functional without it.\n\nThe Kiln name and logos are trademarks of Chesterfield Laboratories Inc.\n\nCopyright 2024 — Chesterfield Laboratories Inc.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkiln-ai%2Fkiln","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkiln-ai%2Fkiln","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkiln-ai%2Fkiln/lists"}