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
An LLM council that reviews your coding agent's every move
Benchmark suite for evaluating LLMs and SLMs on coding and SE tasks. Features HumanEval, MBPP, SWE-bench, and BigCodeBench with an interactive Streamlit UI. Supports cloud APIs (OpenAI, Anthropic, Google) and local models via Ollama. Tracks pass rates, latency, token usage, and costs.
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.
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