Agentes de IA
SWE-agent
Give it a GitHub issue and it tries to fix the code for you - the original SWE-bench agent
★ 20,5k estrellasMIT
Ver la guía →SWE-bench es un proyecto de agentes de IA de código abierto de SWE-bench (SWE-bench/SWE-bench en GitHub). Tiene 5.986 estrellas en GitHub y 1.004 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 18 de septiembre de 2026.
The benchmark that grades AI on real GitHub issues.
Pick one concrete task so you can judge whether the project fits.
Clone the repo and check the prerequisites listed in the README.
Install what it needs and configure credentials or model access.
Run a minimal example end to end before wiring it into your stack.
Built from genuine GitHub issues, not toy problems.
Widely cited for coding-agent evaluation.
Resolves are verified with real test suites.
Accompanied by published methodology.
Harness and datasets in Python.
Permissive licensing.
Install the SWE-bench package.
pip install swebenchPull the task instances you want to evaluate on.
# see docs for dataset optionsEvaluate a model per the documentation.
swebench --help
# see docs for eval commandsIt is maintained by the SWE-bench organization at SWE-bench/SWE-bench, with 1,004 forks.
Yes. It is released under the MIT license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
Running evals needs model access and compute; the benchmark itself is free.
It targets real-world software engineering; compare with AgentBench or GPQA for different agent capabilities.
Read the paper and site docs, install the harness, and run a small evaluation.
Give it a GitHub issue and it tries to fix the code for you - the original SWE-bench agent
A benchmark for tool-agent-user interaction in real-world domains.
A benchmark testing whether agents can solve long-horizon coding tasks.
GitHub's official MCP server — let AI tools work with repos, issues and pull requests
Self-hosted AI vulnerability research: agents find, validate, deduplicate, and rank real security issues.
Benchmark computer-use agents on long, real-world desktop tasks.
Los demás proyectos de Agentes de IA con más estrellas del catálogo de LibreAI: la lista realista de alternativas si SWE-bench no es lo que buscas.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Proyectos de Agentes de IA cercanos en el catálogo, los más próximos en estrellas a SWE-bench: sigue explorando a ambos lados.
A desktop-grade AI agent from NetEase Youdao that does real work — analysis, slides, docs, video and research.
An open-source auth gateway connecting 1,500+ SaaS providers to AI agents via SDK, CLI, MCP and HTTP.
Open-source AI coworkers, each with a computer of their own.
A framework for evaluating and improving AI agents.
An Agent OS with interview-gated, budgeted evolution — one MCP server across 14 agent runtimes.
A zero-code platform for auto-generating production-grade AI agents with unified tools, skills and memory.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.