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Architecture of the Repo
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Description to understand the Repo
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Security Check
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Rebuild Prompt
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Issue in the Repo
Inspiration
Developers often spend hours understanding unfamiliar repositories, tracing bugs, figuring out where changes belong, and repeatedly running tests. Existing AI coding tools can help generate code, but we wanted to build an agent that follows a more complete engineering workflow.
What it does
RepoPilot is an AI-powered coding agent designed to understand, diagnose, and improve software repositories. It analyzes project structure and source code, identifies issues, creates a plan, proposes code changes, runs tests, and uses the results to help refine its fixes.
How we built it
We designed RepoPilot around a repository-aware workflow, combining codebase analysis, AI-assisted reasoning, issue diagnosis, code editing, and test execution. Our goal is to make the backend the core of the product, with a modular architecture that can support different AI models and development environments.
Challenges we faced
One of our biggest challenges was moving beyond basic AI-generated suggestions toward a reliable workflow. Repository analysis, model connectivity, execution errors, and test results all need to work together. We focused on making the process more transparent so developers can understand proposed changes and verify results instead of blindly trusting generated code.
What we learned
We learned that a useful coding agent needs more than a language model. It needs repository context, structured planning, controlled code execution, meaningful test results, and clear feedback when something fails.
What's next
We aim to improve autonomous issue resolution, strengthen sandboxed testing, support local and cloud models, and provide reliable, reviewable code changes. Our vision is to make RepoPilot an open-source engineering assistant that helps developers move from a reported issue to a tested solution faster.
