chunkhound

Name: chunkhound
Author: chunkhound

by chunkhound

Verified

Local first codebase intelligence

1,355stars

108forks

Python

Added 12/27/2025

Installation

# Add to your Claude Code skills
git clone https://github.com/chunkhound/chunkhound

Getting Started

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Security ReportVerified

Last scanned: 4/29/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-04-29T06:26:32.261Z",
  "semgrepRan": false,
  "npmAuditRan": true,
  "pipAuditRan": false
}

README.md

Frequently Asked Questions

What is chunkhound?

chunkhound is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by chunkhound. Local first codebase intelligence. It has 1,355 GitHub stars.

Is chunkhound safe to use?

Yes. chunkhound passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install chunkhound?

Clone the repository with "git clone https://github.com/chunkhound/chunkhound" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is chunkhound written in?

chunkhound is primarily written in Python. It is open-source under chunkhound on GitHub, so you can review or fork the full source.

Are there alternatives to chunkhound?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh chunkhound against similar tools.

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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.

Features

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
32 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, Dart, Lua, 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, and explicit backend selection (watchdog, watchman, polling)

Documentation

Visit chunkhound.ai for documentation:

Configuration Guide

Requirements

Python 3.10+
uv package manager
API keys (optional - regex search works without any keys)
- Embeddings: VoyageAI (recommended) | OpenAI | Local with Ollama
- LLM (for Code Research): Claude Code CLI or Codex CLI (no API key needed) | Anthropic | OpenAI | Grok (xAI)

Installation

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

# Install ChunkHound
uv tool install chunkhound

Quick Start

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.

Index your codebase

chunkhound index

Search changed code in recent commits

# Last N commits
chunkhound search "authentication changes" --last-n 20

# Changes introduced by that commit (diff against its parent; root commits use empty tree)
chunkhound search "database migration" --commit-hash abc1234

# Custom git range
chunkhound search "API changes" --commit-range v2.0..HEAD

# Deep research over recent changes
chunkhound research "what changed in the auth module?" --last-n 50

--vector-source controls scope: diff (default, changed code only), both (merges diff + DB), db (ignore diff).

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

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

License

MIT