The part most code search misses is time: asking what changed on a branch, in a release, or since a commit — and getting that back as an explanation with sources rather than a diff you still have to read.
A local-first codebase intelligence tool with an MCP entry point. It indexes your repository, answers questions with source citations, searches code changes across git history, and can pull in external technical documentation as part of the same research.
- Semantic code search that finds relevant code by meaning rather than exact text, and regex search that works with no provider configured at all
- Cited code research that explains behaviour across files and points back at the sources it used
- Git history research addressed by last N commits, a commit hash, a tag, a branch or a range — which is how a large pull request or a release turns into a reviewable brief
- Pinpoint web research bringing cited external docs, APIs, issues and articles into the same workflow as the local code research
- Autodoc: shareable docs generated from code-backed research
- Tree-sitter parsing for Python, JavaScript, TypeScript, Java, Go, Rust, C and C++, among others
- `--vector-source` to control scope: `diff` for changed code only (the default), `both` to merge diff and database, `db` to ignore the diff
Python 3.10+ and `uv`, then `uv tool install chunkhound` and `chunkhound index .`. API keys are optional and decide how far you get: regex search works with no provider, semantic search needs an embedding provider (VoyageAI, OpenAI or a local Ollama), and deep research needs an LLM provider plus an embedding provider with reranking support. Web research uses the same provider stack. Choose local providers for a setup where no code leaves the machine. A `.chunkhound.json` in the project root holds the full configuration.
One command — uv tool install chunkhound