<img src="https://github.com/chunkhound/chunkhound/raw/v5.1.0a1/public/wordmark-centered.svg" alt="ChunkHound" width="400">
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.
watchdog, watchman, polling)Visit chunkhound.github.io for complete guides: - Quickstart - Configuration Guide - Architecture Deep Dive
# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install ChunkHound
uv tool install chunkhound
.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
chunkhound index
For configuration, IDE setup, and advanced usage, see the documentation.
| 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
MIT
$ claude mcp add chunkhound \
-- python -m otcore.mcp_server <graph>