ChunkHound
About
A local-first semantic code search tool with vector and regex capabilities, designed for AI assistants.
Details
- Author
- ofriw
- Categories
- Search, Developer Tools, Other, Knowledge Base
Jump to
Setup
Install ChunkHound in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/ofriw/chunkhound
Follow the installation instructions in the repository README, then restart your MCP client.
Your entire engineering context, deeply understood.
Open-source codebase intelligence that gives agents and teams cited context across current code, git history, and technical web research.
Local-first · Dozens of languages & file types · Cited answers · Git history research · Pinpoint web research
Getting Started·Configuration·CLI Reference
- Python 3.10+
- uv— install viacurl -LsSf https://astral.sh/uv/install.sh | sh
- API keys(optional — regex search works without any):
- Embeddings:VoyageAI(recommended) |OpenAI|Ollama(local)
- LLM: Claude Code CLI or Codex CLI (no key needed) |Anthropic|OpenAI|Grok
Agents can generate code, but they still miss the context that makes software safe to change: how behavior flows across files, what changed across a branch or release, and which external constraints matter.
Reviewers, support, and product teams hit the same wall when large PRs, merge conflicts, bugs, and release notes need implementation-backed explanation instead of guesses.
ChunkHound turns current code, git history, and technical web research into cited context before anyone edits, reviews, debugs, or explains software.
Deep understanding for four context-heavy jobs
ChunkHound applies codebase understanding to the workflows where missing context hurts most.
Give coding agents grounded architecture context, relevant files, recent changes, and external constraints before they write code.
Turn branch diffs, commit ranges, tags, and specific commits into cited engineering briefs for review, release notes, and changelog drafts.
Turn symptoms, stack traces, and customer reports into likely code paths, recent changes, and external constraints.
Pinpoint the technical docs, APIs, issues, and articles your implementation depends on, then connect that external evidence to local code research.
chunkhound research "How does authentication work?" chunkhound search "JWT refresh token validation" chunkhound research "What changed in auth recently?" --last-n 20
chunkhound research "Summarize the behavior changes on this branch for reviewers" --commit-range main..HEAD chunkhound research "Draft changelog bullets for billing since v2.4" --commit-range v2.4..HEAD chunkhound search "database migration" --commit-hash abc1234
chunkhound research "Why did auth session handling change on each side?" --commit-range main..feature/auth chunkhound search "session refresh conflict" --last-n 50
chunkhound research "why would webhook retries fail?" chunkhound research "what changed in webhook handling this week?" --last-n 30 chunkhound websearch "Stripe webhook retry schedule"
chunkhound research "What happens when a user cancels a subscription?" chunkhound research "What changed in billing since v2.4?" --commit-range v2.4..HEAD
- Semantic code search— find relevant code by meaning, not only exact text
- Cited code research— explain behavior across files with source citations
- Git history research— ask by last N commits, commit hash, tag, branch, or range to understand large PRs and releases
- Pinpoint web research— bring cited external docs, APIs, issues, and articles into the same workflow as local code research
- Autodoc— generate shareable docs from code-backed research
- Local-first indexing— keep code search and indexing under your control
- Python, JavaScript, TypeScript, Java, Go, Rust, C/C++, and morevia Tree-sitter
chunkhound index . chunkhound research "How does authentication work?"
Index once, ask a real architecture question, and get a grounded answer with citations. Regex search works without providers. Semantic search requires an embedding provider. Deep research requires an LLM provider and an embedding provider with reranking support; web research uses the same provider stack. Choose local providers for zero-code-egress setups.
For a full configurable setup, create.chunkhound.jsonin your project root:
{ "embedding": { "provider": "voyageai", "api_key": "your-key" }, "llm": { "provider": "claude-code-cli" } }
For editor integration, all provider options, and advanced configuration:
In addition to searching your indexed codebase, ChunkHound can search code changes across git history — useful for understanding what changed in a PR, a release, or since a specific commit.
# Last N commits chunkhound search "authentication changes" --last-n 20 # Changes introduced by a specific commit 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-sourcecontrols scope:diff(default, changed code only),both(merges diff + DB),db(ignore diff).
- large repos and monorepos
- multi-language codebases
- legacy systems
- local-only or security-sensitive environments
- engineering teams that want agents, support, and product questions grounded in the same code index
ChunkHound is MIT licensed, open source, and community built.
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