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Travsr
Code Graph that lives next to git.
AI coding tools rely on grep or vector RAG they don't understand your code's call graph or types, so asking about charge() surfaces every unrelated match. Travsr builds a deterministic code graph (tree-sitter + semantic resolution, local SQLite) exposed to AI agents via MCP for precise, ranked answers instead of guesses. 26 first-party tools, 16 languages, VS Code graph view. Open source, Apache-2.0.
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.
About Travsr on Product Hunt
“Code Graph that lives next to git.”
Travsr was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #110 on the daily leaderboard. AI coding tools rely on grep or vector RAG they don't understand your code's call graph or types, so asking about charge() surfaces every unrelated match. Travsr builds a deterministic code graph (tree-sitter + semantic resolution, local SQLite) exposed to AI agents via MCP for precise, ranked answers instead of guesses. 26 first-party tools, 16 languages, VS Code graph view. Open source, Apache-2.0.
On the analytics side, Travsr competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how Travsr performed against the three products that launched closest to it on the same day.
Who hunted Travsr?
Travsr was hunted by Raj Kumar Vishwakarma. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Travsr including community comment highlights and product details, visit the product overview.
Raj Kumar Vishwakarma