Local first codebase intelligence

Your AI assistant searches code but doesn't understand it. ChunkHound researches your codebase—extracting architecture, patterns, and institutional knowledge at any scale. Integrates via MCP.

• cAST Algorithm - Research-backed semantic code chunking
• Multi-Hop Semantic Search - Discovers interconnected code relationships beyond direct matches
• Semantic search - Natural language queries like "find authentication code"
• Regex search - Pattern matching without API keys
• Local-first - Your code stays on your machine
• 30 languages with structured parsing
  • Programming (via Tree-sitter): Python, JavaScript, TypeScript, JSX, TSX, Java, Kotlin, Groovy, C, C++, C#, Go, Rust, Haskell, Swift, Bash, MATLAB, Makefile, Objective-C, PHP, Vue, Svelte, Zig
  • Configuration: JSON, YAML, TOML, HCL, Markdown
  • Text-based (custom parsers): Text files, PDF
• MCP integration - Works with Claude, VS Code, Cursor, Windsurf, Zed, etc
• Real-time indexing - Automatic file watching, smart diffs, seamless branch switching

Visit chunkhound.github.io for complete guides:

# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install ChunkHound
uv tool install chunkhound

1. Create .chunkhound.json in project root

{
  "embedding": {
    "provider": "voyageai",
    "api_key": "your-voyageai-key"
  },
  "llm": {
    "provider": "claude-code-cli"
  }
}

Note: Use "codex-cli" instead if you prefer Codex. Both work equally well and require no API key.

2. Index your codebase

For configuration, IDE setup, and advanced usage, see the documentation.

Ideal for:

• Large monorepos with cross-team dependencies
• Security-sensitive codebases (local-only, no cloud)
• Multi-language projects needing consistent search
• Offline/air-gapped development environments

Stop recreating code. Start with deep understanding.

MIT