swe-bench

AI Agent that handles engineering tasks end-to-end: integrates with developers’ tools, plans, executes, and iterates until it achieves a successful result.

SE-Agent is a self-evolution framework for LLM Code agents. It enables trajectory-level evolution to exchange information across reasoning paths via Revision, Recombination, and Refinement, expanding the search space and escaping local optima. On SWE-bench Verified, it achieves SOTA performance

We track and analyze the activity and performance of autonomous code agents in the wild

This project explores how Large Language Models (LLMs) perform on real-world software engineering tasks, inspired by the SWE-Bench benchmark. Using locally hosted models like Llama 3 via Ollama, the tool evaluates code repair capabilities on Python repositories through custom test cases and a lightweight scoring framework.