AutoPR

Inspiration

Managing GitHub issues and pull requests can be tedious and time-consuming, especially when working on fast-moving projects. I wanted to explore whether LLMs could take over that grunt work entirely — automatically triaging issues, generating fixes, and merging PRs without manual intervention. The idea of a fully autonomous code maintenance pipeline felt like a natural and exciting application of modern AI capabilities.

What it does

AutoPR automatically resolves GitHub issues and merges pull requests using LLMs. It reads open issues, generates code fixes, and handles the PR lifecycle end-to-end with minimal human involvement.

How we built it

We built AutoPR using Claude Code as the core reasoning engine, combined with custom manually written code to handle GitHub API interactions, pipeline orchestration, and decision logic.

Challenges we ran into

Building reliable agentic pipelines is harder than it looks. We ran into numerous bugs stemming from unexpected LLM outputs, edge cases in code generation, and keeping the pipeline state consistent across steps. Getting the system to fail gracefully rather than silently was a particular challenge.

Accomplishments that we're proud of

We successfully built a fully automatic pipeline that can take a GitHub issue from open to resolved without human input. Seeing it work end-to-end for the first time was a genuinely satisfying moment.

What we learned

Building this project gave us a deep appreciation for the complexity of agentic systems — how much careful prompt engineering, error handling, and state management goes into making LLM-driven workflows reliable. It also highlighted how powerful these tools can be when the pieces come together.

What's next for AutoPR

We want to build a dedicated merging agent capable of intelligently resolving merge conflicts, making AutoPR even more robust for real-world repositories with active, overlapping development.