Codebase Retrieval for AI Agents
Token-efficient retrieval layer for large codebases with AST indexing, BM25 search, and fast symbol lookup.
- C#
- .NET
- Tree-sitter
- BM25
- AST Indexing
- AI Agents
The Challenge
AI agents need precise code context, but they cannot load arbitrary amounts of source code into a language model. In larger codebases, the real challenge is finding relevant files, symbols, and relationships quickly while keeping token usage low.
The Solution
code-explorer provides a retrieval layer for software projects. It indexes code structurally, makes it searchable, and prepares context so AI agents can focus on the most relevant parts of a codebase.
Architecture Highlights
- AST indexing: Source code is analyzed structurally rather than treated as plain text.
- BM25 search: Relevant files and code regions can be prioritized with proven search techniques.
- Symbol lookup: Fast symbol resolution reduces search effort and context size.
- Agent-ready retrieval: Output is optimized to give language models useful context with minimal token overhead.
The Result
The project demonstrates expertise at the intersection of code intelligence, search technology, and AI-assisted software development. It is a strong reference for tools that make large codebases easier to analyze and automate.