LangChain AI Agents Hackathon — 2nd Place

The Challenge

Build an AI agent that solves a real developer workflow problem. Judged on innovation, technical execution, and practical usefulness.

Our Approach

We built ReviewBot — a multi-agent system that:

1. Analyzes PR diffs — understands code changes in context of the full repository
2. Identifies issues — security vulnerabilities, performance problems, style violations
3. Suggests fixes — generates corrected code with explanations
4. Writes tests — auto-generates unit tests covering the changed code paths

Architecture

The system uses a LangGraph state machine with four specialized agents:

• Analyst — parses diffs and builds dependency graphs
• Reviewer — identifies potential issues using RAG over best practices
• Fixer — generates code suggestions using few-shot examples
• Tester — creates test cases using mutation testing principles

What I Learned

Building under pressure forces you to make ruthless scope decisions. We cut three planned features on Day 1 evening to focus on making the core flow bulletproof — that focus is what won us the judges.