Launched this week
GitHits beta 0.9
Give your AI coding agent access to open-source code
267 followers
Give your AI coding agent access to open-source code
267 followers
GitHits gives coding agents access to the open-source code your app depends on. Get real implementation examples, dependency source navigation, package inspection and documentation. Agents can grep and read your codebase. They can't grep and read the open-source code your app depends on. That's where they start guessing, retrying, and looping. GitHits builds a version-aware index on demand. Agents can search, navigate, and inspect the code behind their dependencies. CLI: npx githits@latest init
Interactive
Free
Launch Team / Built With
Works great, I've been using Githits to explore implementation details from different libraries to ground my coding agent with real world examples. It's cool to point your coding agent towards an open source reference implementation that you know has already implemented what you want to implement.
GitHits beta 0.9
Thanks,@matti_ryttylainen Appreciate your kind words and all feedback you've provided along the way!
Giving a coding agent access to real open-source code is a smart idea. How does GitHits pick which repos or snippets are most relevant?
GitHits beta 0.9
@doganakbulut For snippets it's actually pretty involved. We run our own code index, so on top of standard ranking like BM25 we can use a bunch of code-specific signals for scoring. We also keep a separate document index for docs from canonical documentation sites. That lets us pull a balanced mix of function definitions, real usage examples, tests and related documentation, so the agent gets proper grounding, with each snippet linking back to the source it came from.
For picking which repos to pull from, right now we lean on a wide range of GitHub searches plus our own reranking. We're also building out a dedicated index for that part.
This app is seriously helpful and turns your coding agent in a top tier senior engineer. When I am introducing a new pattern or library in my code, I always think to find real battle tested implementations first from GitHits. Eventually you realize just letting a coding agent vibe out consequential and high leverage code is just plain dumb and irresponsible.
GitHits beta 0.9
Thanks,@lukeotwell! Let us know how we could make GitHits even better!
giving the agent access to open source code is the easy half. the hard half is helping it pick the right code. github has incredible code and also a long tail of broken half written experiments. how do you weight signals like maintained recently, used by many, tests pass, vs raw similarity to what the agent is trying to write?
Toolhouse
Congrats!
GitHits beta 0.9
Thanks@orliesaurus! Appreciate your support!
GitHits beta 0.9
Here's a press story worth checking out: Finnish Startup Raises €1.5M to Build the “Google for Code”
Selected quotes:
"Analysis of 16 widely used code-generating LLMs across 576,000 generated code samples found that 21.7% of the package names referenced in AI coding outputs were hallucinations — meaning no such packages existed in npm or PyPI. That’s nearly one in four dependencies simply invented by the model. GitHits is betting that plugging this specific gap — open-source code context for AI agents — is the infrastructure play that the entire coding-agent ecosystem has been missing."
"The origin story is refreshingly unglamorous."
"The GitHits Google for code framing isn’t just clever marketing — it’s a deliberate competitive positioning. As Heinisuo puts it: 'OpenAI, Anthropic, and Google have left a gap in the market. GitHits doesn’t compete with Codex, Claude Code, or Cursor, but complements them by bringing open-source code as context for agents to end retry loops and reduce token consumption.'"
"GitHits raised €1.5 million in a pre-seed round led by Vendep Capital, with participation from Trind VC and angel investors including Peter Sarlin, Zach Shelby, and Jerry Liu. That last name is notable — Jerry Liu is the co-founder of LlamaIndex, one of the most widely used frameworks for building LLM-powered applications. His backing signals more than capital; it signals conviction from someone who lives inside the AI agent stack daily."