Today, every AI coding tool uses either grep (exact string matching) or vector RAG (approximate similarity). Either way, they have no understanding of your code's call graph, types, or imports. If you ask about charge(), it's going to dredge up every other "charge" in your codebase and potentially miss the one you want if it gets renamed. This is why we built: Travsr! Travsr is a deterministic graph of your code that is exposed to your AI agents through the MCP (Model Context Protocol) , allowing them to reason much more accurately and precisely about your code. The short version of how it works is: Fast Parsing: tree-sitter parses each file once, without needing to rebuild the entire AST. Then, we perform a deeper pass to resolve all of the interesting semantics (call targets, types, data flow) to a fast local sqlite database (.travsr/graph.db). Finally, when an AI agent wants to know about something, we can traverse the graph, score nodes by page rank, and get back the exact code the user needs to answer their question, perfectly tokenized for their model. We also have a git hook that keeps your index fresh, only rebuilding files when they're changed, so it's fast enough to be used on every commit.

Travsr

Code Graph that lives next to git.

Code Graph that lives next to git.