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Contributing to LeanKG

First off, thank you for considering contributing to LeanKG! It’s people like you who make LeanKG a powerful tool for the AI-assisted development ecosystem.

As a project focused on Lightweight Knowledge Graphs for AI, we value contributions that improve indexing accuracy, reduce token overhead, and expand MCP capabilities.

🛠 Tech Stack

  • Language: Rust (Latest Stable)
  • Database: CozoDB (Graph engine)
  • Parsers: tree-sitter (for Go, Rust, TS, Python, etc.)
  • Protocol: Model Context Protocol (MCP)

🚀 How to Get Started

1. Setup Your Environment

Clone the repository and ensure you have the Rust toolchain installed:

git clone https://github.com/FreePeak/LeanKG.git
cd LeanKG
cargo build

2. Local Development & Testing

We use a Makefile to simplify common development tasks:

  • Run tests: cargo test
  • Build release: cargo build --release
  • Local MCP Testing: Use the mcp-stdio command to test changes with your local AI tools (Cursor, Claude Code, etc.).

3. Project Structure

  • /src: Core logic, graph schema, and indexing engine.
  • /npm-package: Wrappers for distribution.
  • /examples: Sample codebases used for benchmarking.
  • /instructions: Agent-specific instructions (CLAUDE.md, AGENTS.md).

📈 Contribution Areas

Adding Language Support

LeanKG uses tree-sitter for parsing. If you want to add a new language:

  1. Add the corresponding tree-sitter dependency in Cargo.toml.
  2. Implement the parser logic in src/indexer/.
  3. Define how code elements (functions, classes, imports) map to the graph schema.

Improving MCP Tools

We are constantly expanding the tools available to AI agents. If you have an idea for a new tool (e.g., get_complexity_score or find_dead_code):

  1. Define the tool in the MCP server module.
  2. Ensure the output is token-optimized (we aim for high signal-to-noise ratios).

Benchmarking

Performance is a core feature. If you contribute a feature, please run the benchmarks in the benchmark/ folder to ensure no significant regression in indexing speed or token usage.

📋 Pull Request Process

  1. Check Issues: Look for existing issues or create a new one to discuss your idea.
  2. Branching: Create a feature branch (feat/your-feature or fix/your-fix).
  3. Commit Messages: We follow Conventional Commits (e.g., feat: add support for Ruby, fix: handle circular dependencies).
  4. Documentation: If you add a new CLI command or MCP tool, update the README.md and the relevant agent instruction files in /instructions.
  5. Review: Once submitted, a maintainer will review your code. We prioritize performance, code safety (it is Rust, after all!), and documentation.

🤖 AI-Assisted Contributions

Since LeanKG is built for AI agents:

  • Feel free to use LeanKG itself while developing!
  • If you find that an AI agent (like Claude or Cursor) struggles to understand a part of this repo, please submit a PR to improve our CLAUDE.md or AGENTS.md instructions.

⚖️ License

By contributing, you agree that your contributions will be licensed under the MIT License.

Tips for success:

  • Keep it Lean: Every byte of data sent via MCP costs tokens. Always look for ways to compress the graph context.
  • Stay Local-First: We avoid cloud dependencies. Any new feature should work entirely on the user's local machine.