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<a href="https://chunkhound.github.io">
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<strong>Deep Research for Code & Files</strong>
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Transform your codebase into a searchable knowledge base for AI assistants using [semantic search via cAST algorithm](https://arxiv.org/pdf/2506.15655) and regex search. Integrates with AI assistants via the [Model Context Protocol (MCP)](https://spec.modelcontextprotocol.io/).
## Features
- **[cAST Algorithm](https://arxiv.org/pdf/2506.15655)** - Research-backed semantic code chunking
- **[Multi-Hop Semantic Search](https://chunkhound.github.io/under-the-hood/#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
- **24 languages** with structured parsing
- **Programming** (via [Tree-sitter](https://tree-sitter.github.io/tree-sitter/)): Python, JavaScript, TypeScript, JSX, TSX, Java, Kotlin, Groovy, C, C++, C#, Go, Rust, Bash, MATLAB, Makefile, PHP, Vue
- **Configuration** (via Tree-sitter): JSON, YAML, TOML, Markdown
- **Text-based** (custom parsers): Text files, PDF
- **[MCP integration](https://spec.modelcontextprotocol.io/)** - Works with Claude, VS Code, Cursor, Windsurf, Zed, etc
## Documentation
**Visit [chunkhound.github.io](https://chunkhound.github.io) for complete guides:**
- [Tutorial](https://chunkhound.github.io/tutorial/)
- [Configuration Guide](https://chunkhound.github.io/configuration/)
- [Architecture Deep Dive](https://chunkhound.github.io/under-the-hood/)
## Requirements
- Python 3.10+
- [uv package manager](https://docs.astral.sh/uv/)
- API key for semantic search (optional - regex search works without any keys)
- [OpenAI](https://platform.openai.com/api-keys) | [VoyageAI](https://dash.voyageai.com/) | [Local with Ollama](https://ollama.ai/)
## Installation
```bash
# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install ChunkHound
uv tool install chunkhound
```
## Quick Start
1. Create `.chunkhound.json` in project root file
```json
{
"embedding": {
"provider": "openai",
"api_key": "your-api-key-here"
}
}
```
2. Index your codebase
```bash
chunkhound index
```
**For configuration, IDE setup, and advanced usage, see the [documentation](https://chunkhound.github.io).**
## Real-Time Indexing
**Automatic File Watching**: MCP servers monitor your codebase and update the index automatically as you edit files. No manual re-indexing required.
**Smart Content Diffs**: Only changed code chunks get re-processed. Unchanged chunks keep their existing embeddings, making updates efficient even for large codebases.
**Seamless Branch Switching**: When you switch git branches, ChunkHound automatically detects and re-indexes only the files that actually changed between branches.
**Live Memory Systems**: Index markdown notes or documentation that updates in real-time while you work, creating a dynamic knowledge base.
## Why ChunkHound?
**Research Foundation**: Built on the [cAST (Chunking via Abstract Syntax Trees)](https://arxiv.org/pdf/2506.15655) algorithm from Carnegie Mellon University, providing:
- **4.3 point gain** in Recall@5 on RepoEval retrieval
- **2.67 point gain** in Pass@1 on SWE-bench generation
- **Structure-aware chunking** that preserves code meaning
**Local-First Architecture**:
- Your code never leaves your machine
- Works offline with [Ollama](https://ollama.ai/) local models
- No per-token charges for large codebases
**Universal Language Support**:
- Structured parsing for 24 languages (Tree-sitter + custom parsers)
- Same semantic concepts across all programming languages
**Intelligent Code Discovery**:
- Multi-hop search follows semantic relationships to find related implementations
- Automatically discovers complete feature patterns: find "authentication" to get password hashing, token validation, session management
- Convergence detection prevents semantic drift while maximizing discovery
## License
MIT
