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

MCP Servers4.9k stars372 forksPythonNOASSERTIONUpdated today
Editor's note

Kiln is a desktop workbench and open-source Python library covering the full AI development cycle, including an Eval Builder that auto-generates judge models and synthetic evaluation datasets, an Auto-Optimize feature that tests prompt variants, model selection, and agent configurations to find the best-performing setup, drag-and-drop RAG creation from PDFs, images, video, and audio, zero-code fine-tuning across 60-plus models on Fireworks, Together, and Vertex with serverless deployment, and multi-agent composition via subagents each running in isolated context windows. It exposes an MCP server so Claude Code and other MCP-compatible clients can interact with Kiln tasks and tools directly. The desktop app runs on Mac, Windows, and Linux with one-click installation, stores project data in a Git-native format so non-technical teammates like product managers and domain experts can rate outputs and contribute datasets without writing code, and supports fully offline use via Ollama. The MIT-licensed Python library lets agents built in the app ship straight to production.

ClaudeWave Trust Score
100/100
Verified
Passed
  • License: NOASSERTION
  • Actively maintained (<30d)
  • Healthy fork ratio
  • Clear description
  • Topics declared
  • Mature repo (>1y old)
Last scanned: 6/11/2026
Install in Claude Code / Claude Desktop
Method: UVX (Python) · kiln
Claude Code CLI
claude mcp add kiln -- uvx kiln
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "kiln": {
      "command": "uvx",
      "args": ["kiln"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/Kiln-AI/Kiln and follow its README.
Use cases

MCP Servers overview

README preview not available. Visit the repo on GitHub for full documentation.
aichain-of-thoughtcollaborationdataset-generationevalsevaluationevaluation-frameworkfine-tuningmachine-learningmacosmcpmlollamaopenaipromptprompt-engineeringpythonrlhfsynthetic-datawindows

What people ask about Kiln

What is Kiln-AI/Kiln?

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Kiln-AI/Kiln is mcp servers for the Claude AI ecosystem. Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more. It has 4.9k GitHub stars and was last updated today.

How do I install Kiln?

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You can install Kiln by cloning the repository (https://github.com/Kiln-AI/Kiln) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.

Is Kiln-AI/Kiln safe to use?

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Our security agent has analyzed Kiln-AI/Kiln and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.

Who maintains Kiln-AI/Kiln?

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Kiln-AI/Kiln is maintained by Kiln-AI. The last recorded GitHub activity is from today, with 67 open issues.

Are there alternatives to Kiln?

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Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.

Deploy Kiln to your cloud

Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.

Maintain this repo? Add a badge to your README

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Featured on ClaudeWave: Kiln-AI/Kiln
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