Kiln

Free

Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

Model APIsFreeFree tier

Type

Open Source

Visit Website GitHub (4.8k stars)

About Kiln

Kiln is an open-source AI workbench designed for teams to build, evaluate, and optimize AI systems. It provides a unified platform covering the entire AI development lifecycle: building RAG pipelines, developing agents with tools and MCP, evaluating outputs via LLM-as-Judge and golden datasets, fine-tuning models, generating synthetic data, and optimizing prompts and agent designs. The desktop app (source-available) and Python library (MIT-licensed) support local dataset management with git auto-sync, enabling collaboration across engineers, data scientists, and subject matter experts. Kiln includes an AI Assistant that can run experiments and optimize configurations through conversation, and supports 190+ models across cloud and local environments. It is free for individuals, with Pro and Enterprise tiers offering enhanced model access, auto-optimization, and team features.

Key Features

Build: RAG indexing, chunking, retrieval; reusable skills; tool composition via MCP; sub-agents; structured output (JSON); reasoning; prompt generators

Evaluate: LLM-as-Judge evals, AI eval builder, golden datasets, human ratings, compare

Improve: Auto-optimize prompts, fine-tuning, synthetic data generation, filtering, labeling, AI Assistant for experiments

Collaborate: Git auto-sync for datasets, feedback/reviews, issue tracking, open-source Python library (MIT)

AI Assistant: Conversational AI that understands projects, datasets, and evals; can run experiments and optimize

190+ supported models across cloud and local environments

Automatic agent optimization (Kiln Optimizer) in Pro/Team tiers

Pros & Cons

Pros

Open-source core (MIT-licensed Python library, source-available desktop app)
Free for individuals with full feature access (local runs)
Data privacy: datasets and evals stored locally and synced via user-controlled git repos
Comprehensive platform covering building, evaluation, optimization, and deployment
AI Assistant aids experimentation and optimization through natural language
Supports wide range of models (190+) both cloud and local
Built by experienced team from Apple and Microsoft with production AI expertise

Cons

Advanced features (higher model limits, Kiln Optimizer, SSO) require paid Pro or Enterprise plans
No fully web-based interface; requires local installation of desktop app or Python library
Enterprise features (SSO, annual contracts, dedicated support) only available on custom plan
Dataset management relies on git, which may have a learning curve for non-technical users

Best For

Building and evaluating RAG systemsDeveloping AI agents with tool use and sub-agentsFine-tuning large language models on custom datasetsGenerating synthetic data for model training and evaluationOptimizing prompts and agent designs using automated evalsCollaborating on AI projects across engineering, data science, and domain expertsVersioning datasets and experiments with git for reproducibility

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FAQ

Is Kiln open-source?

Yes, the Python library is MIT-licensed and the desktop app is source-available on GitHub, allowing audit and self-hosting.

What platforms does Kiln support?

Kiln offers native desktop apps for macOS, Windows, and Linux, plus a Python library compatible with all major operating systems.

Can I use Kiln for free?

Yes, the Individual plan is free and includes the open-source Python library, desktop app, local datasets with git sync, and community support.

How does Kiln handle data privacy?

Datasets and evals live on your machine and sync through a git repo you control. Kiln does not host your data unless you opt into Kiln Pro features.

What models does Kiln support?

Kiln supports over 190 models, including both cloud APIs and local models, with tested capabilities listed in the model library.