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<a href="https://chunkhound.github.io">
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<strong>Local first codebase intelligence</strong>
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<a href="https://github.com/chunkhound/chunkhound/actions/workflows/smoke-tests.yml"><img src="https://github.com/chunkhound/chunkhound/actions/workflows/smoke-tests.yml/badge.svg" alt="Tests"></a>
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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](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
- **30 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, 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](https://spec.modelcontextprotocol.io/)** - Works with Claude, VS Code, Cursor, Windsurf, Zed, etc
- **Real-time indexing** - Automatic file watching, smart diffs, seamless branch switching
## 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 keys (optional - regex search works without any keys)
- **Embeddings**: [VoyageAI](https://dash.voyageai.com/) (recommended) | [OpenAI](https://platform.openai.com/api-keys) | [Local with Ollama](https://ollama.ai/)
- **LLM (for Code Research)**: Claude Code CLI or Codex CLI (no API key needed) | [Anthropic](https://console.anthropic.com/) | [OpenAI](https://platform.openai.com/api-keys)
## 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
```json
{
"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
```bash
chunkhound index
```
**For configuration, IDE setup, and advanced usage, see the [documentation](https://chunkhound.github.io).**
## Why ChunkHound?
| Approach | Capability | Scale | Maintenance |
|----------|------------|-------|-------------|
| Keyword Search | Exact matching | Fast | None |
| Traditional RAG | Semantic search | Scales | Re-index files |
| Knowledge Graphs | Relationship queries | Expensive | Continuous sync |
| **ChunkHound** | Semantic + Regex + Code Research | Automatic | Incremental + realtime |
**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.**
## License
MIT
