chunkhound/chunkhound
Local first codebase intelligence
What it solves
ChunkHound addresses the "blind spot" coding agents and engineering teams face when they lack the broader context of a codebase. It prevents errors caused by missing information on how behavior flows across files, how the git history has evolved, and how external technical constraints or documentation impact the current implementation.
How it works
ChunkHound creates a local-first index of a codebase using Tree-sitter to support multiple languages. It then provides several layers of retrieval and analysis:
- Semantic Search: Finds relevant code based on meaning rather than just keyword matching.
- Cited Research: Uses an LLM to explain behavior across files, providing source citations for every answer.
- Git History Integration: Allows users to query specific commit ranges, tags, or the last N commits to understand changes in PRs or releases.
- Web Research: Integrates external technical documentation, APIs, and articles into the local research workflow.
- Flexible Providers: Supports various embedding and LLM providers, including local options (like Ollama) for security-sensitive environments.
Who it’s for
It is designed for developers and engineering teams working with large repositories, monorepos, multi-language codebases, or legacy systems. It is also intended for those who want to ground AI coding agents in actual codebase architecture before they write code.
Highlights
- Local-first indexing for better control and security.
- Git-aware research to analyze diffs, commit ranges, and branch changes.
- Multi-language support for Python, JS/TS, Java, Go, Rust, C/C++, and more.
- Cited answers that link research results back to the source code.
- Integrated web research to connect local code to external technical docs.