// agents · SWE-Gym/SWE-Gym

SWE-Gym

SWE-Gym é um projeto de agentes de IA de código aberto de SWE-Gym (SWE-Gym/SWE-Gym no GitHub). Tem 748 estrelas no GitHub e 46 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 29 de julho de 2025.

SWE-Gym: the first training environment for real-world software engineering agents and verifiers (ICML 2025).

★ 748 estrelas⑂ 46 forksApache-2.0atualizado jul 2025
▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.
// dados do projeto

Dados do projeto

Repositório
SWE-Gym/SWE-Gym
Estrelas no GitHub
748
Forks
46
Licença
Apache-2.0
Última atualização no GitHub
29 de julho de 2025
Categoria
Agentes de IA
Tipo de ficha
Guia de instalação completo
Site oficial
Official site
// como funciona

Como funciona

  1. Clarify your goal

    Decide your goal: training an agent, training a verifier for inference-time scaling, or reproducing the paper's results.

  2. Get the project

    Clone the repository and review the scaffold-specific instructions for OpenHands or MoatlessTools.

  3. Install and configure

    Access the dataset from Hugging Face and pull the pre-built Docker image for the task instances you will train on.

  4. Run your first task

    Generate agent trajectories, fine-tune or rejection-sample your model, and evaluate on SWE-Bench splits using the included verifier approach.

// recursos

Por que as pessoas gostam tanto

Real tasks with verification

Thousands of real-world tasks drawn from 11 Python repositories, each with repository context, an executable environment, and test-based verification, plus a smaller Lite split.

Agent training trajectories

Documented generation of agent-environment interaction trajectories with OpenHands for fine-tuning models as SWE agents.

Verifier training for inference-time scaling

Verifiers trained on trajectories select the most promising solution among samples, improving results through best-of-n selection.

Two scaffold paths

Reproduction instructions cover both the OpenHands and MoatlessTools agent scaffolds.

Pre-built instance images

Docker images for each task instance are published on Docker Hub, removing environment setup friction.

Published open baselines

The project reports open state-of-the-art results for its time on SWE-Bench Verified and Lite, with scaling analyses in the paper.

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// execute você mesmo

Execute você mesmo

  1. Clone the repository

    Start from the code and documentation for the training and reproduction workflows.

    git clone https://github.com/SWE-Gym/SWE-Gym.git
    cd SWE-Gym
  2. Get the dataset and environment images

    Download the dataset and models from the project's Hugging Face page and pull instance images from Docker Hub. # see README reproducing results section

    # dataset and models: https://huggingface.co/SWE-Gym (see README)
  3. Follow a scaffold guide

    Reproduce training or inference-time scaling with the documented scaffold instructions.

    # see docs/OpenHands.md and docs/MoatlessTools.md in the repository
// casos de uso

O que você pode criar

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// perguntas frequentes

Perguntas frequentes

Who maintains it?

SWE-Gym was created by researchers from UC Berkeley, UIUC, CMU, and Apple, and is maintained under the SWE-Gym GitHub organisation.

Is it free to use?

Yes, the project is Apache-2.0 licensed and its dataset and models are public on Hugging Face; training itself requires substantial compute that you provide.

Does it need a paid API key?

No API key is required for the environment itself, though the documented trajectory generation used hosted models such as GPT-4o and Claude, which need those providers' access if you follow the same recipe.

How does it compare to a well-known alternative?

SWE-Bench is an evaluation benchmark; SWE-Gym is a training environment with executable tasks and verification designed to improve performance on benchmarks like SWE-Bench.

How do I get started?

Clone the repo, download the dataset from Hugging Face, and follow docs/OpenHands.md or docs/MoatlessTools.md for the workflow you want.

// continue explorando

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// alternativas

Alternativas em Agentes de IA

Os outros projetos de Agentes de IA com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se SWE-Gym não for o ideal para você.

// mais em agents

Mais em Agentes de IA

Projetos de Agentes de IA próximos no catálogo, os mais próximos em estrelas de SWE-Gym: continue explorando dos dois lados dele.

Baixe o SWE-Gym

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.