ChunkHound is a tool designed to enhance codebase intelligence by leveraging AI to understand and analyze complex code structures. It extracts architectural patterns and institutional knowledge, allowing developers to navigate their code more effectively. The tool integrates seamlessly with existing environments through the MCP (Multi-Cloud Platform), supporting over 30 programming languages and various configuration formats. The system employs a range of search methods, including semantic search for natural language queries and regex search for pattern matching without the need for API keys. Real-time indexing keeps the codebase updated automatically, adapting to changes and allowing quick branch switching. Notable features include the cAST algorithm for semantic code chunking and multi-hop semantic search, which uncovers connections between code elements that go beyond simple keyword matches. This makes it particularly useful for large monorepos, security-sensitive environments, and projects that utilize multiple programming languages. Setting up ChunkHound involves installing the `uv` package manager and creating a configuration file in the project root to specify your preferred embedding provider and LLM (Large Language Model). The tool supports easy integration with options like VoyageAI and OpenAI, and both Claude Code CLI and Codex CLI can be used for code research without requiring API keys. The approach combines semantic search with regex and code research capabilities, offering a more comprehensive solution for understanding and navigating codebases over traditional search methods.