Your agent can only see part of the system

AI coding agents can read your local repository.

Modern software depends on far more than that.

Under every application sits an open-source stack made of frameworks, SDKs, APIs, infrastructure tooling, dependency internals, and version-specific behavior spread across thousands of repositories.

That’s where agents lose visibility.

Agents loop instead of converging

When an agent encounters undocumented behavior, dependency edge cases, or unfamiliar integrations, it keeps generating variations instead of grounding itself in working implementations.

It retries. It rewrites. It improvises. The output changes. The uncertainty remains.

What looks like progress is often just activity hiding missing context.

Agents produce fragile systems

AI coding agents can generate code that appears correct while missing the implementation patterns real systems actually depend on.

Without grounded operational context, integrations become brittle, abstractions drift from ecosystem conventions, and edge cases surface later under real production pressure.

The code compiles. The system still fails.

Missing context becomes token burn

Modern coding agents don’t fail instantly.

They retry, explore alternatives, spawn sub-agents, and keep searching for a path that still fails to converge.

Without grounded context, output tokens get spent generating variations instead of reaching working solutions.

• More retries.
• More patching.
• More investigation.
• Less actual progress.
• The output keeps changing.
• The loop keeps burning tokens.

GitHits gives AI coding agents version-aware access to real open-source examples, dependency source code, docs, and package metadata so they can retrieve and navigate the actual code running in your stack.

The result is less retry churn, better ecosystem fit, and a shorter path from uncertainty to a working implementation.

Every modern application is built on open source. AI coding agents need to understand that software to write correct code. GitHits gives them access to real open-source examples, dependency source code, docs, and package metadata across the stack your product is actually built on.

Developers love GitHits

I’m sharing how I got the idea for GitHits, why we decided to build it, and who it is for.

By Olli-Pekka Heinisuo, GitHits Co-Founder and CTO

The GitHits origin story: where the idea came from

When documentation didn’t explain something clearly, I had a simple fallback: search GitHub.

If a problem has been solved before, there is usually code somewhere showing how it works. GitHub search often surfaces it, but finding the right example still requires digging through files, issues, and discussions.

One day, Softlandia’s co-founder Mikko asked in Slack:

Who can find the definition for TranscribeDefinition?

He was building a transcription pipeline using Azure’s Speech SDK and couldn’t figure out how to initialize the object.

The official documentation didn’t show it yet, but the definition was already present in Microsoft’s GitHub repository inside documentation files prepared for a future release.

After finding it, I wrote in Slack:

GitHub search solves a surprising number of problems if the project is open source.

Then I added:

You could probably build a coding assistant that answers questions by searching GitHub with some heuristics.

That message started a discussion inside Softlandia Venture Studio, and we decided to explore the idea. Jaakko also managed to buy githits.com for $9.

Why we built GitHits

Much of the practical knowledge about how libraries are used already exists in open-source repositories.

Across millions of repositories, developers have already solved integration problems, handled edge cases, and figured out how libraries behave in practice. The difficulty is locating the relevant implementation.

Documentation is often incomplete. Search returns raw results. AI coding agents generate plausible answers but struggle with unfamiliar libraries and long-tail edge cases.

Developers usually resolve these situations by looking at how the problem was implemented in existing repositories. We believe AI coding agents should be able to learn from those implementations the same way developers do.

GitHits turns those examples into implementation guidance and source links that agents and developers can use directly. Over time, this creates a map of how software is actually built across the open-source ecosystem.

How GitHits finds the implementation

GitHits searches open-source repositories and distills implementation guidance from existing code.

Instead of returning a long list of search results, it analyzes code, issues, discussions, pull requests, and repository context to produce an example relevant to the problem being investigated.

This gives developers and AI coding agents a concrete starting point based on how similar problems were solved in open source.

Who it helps

Developers using AI coding tools

Engineers working with tools such as Claude Code, Cursor, or Copilot who encounter problems that models cannot resolve.

Helping Claude Code find undocumented C++ APIs directly from the code.

— Onni Hakala, GitHits user

Developers working on ecosystems where AI is less reliable

Engineers building in languages like Go, Rust, Kotlin, or C++, where documentation is thinner and training-data coverage is weaker.

I swapped my ritual of 20 browser tabs and stale Stack Overflow answers for a tool that actually understands what I’m trying to do.

— Atharv Singh, GitHits user

Meet the GitHits team