State-Of-The-Art

Fine-Tuned Agents

Atlas outperforms competitors on the SWE-Bench by adopting a novel approach to Fine-Tuning Language Models

Fine-Tuned Agent

Atlas leverages a groundbreaking approach to fine-tuning language models. By training with trajectories from multiple tasks and various prompting methods, Atlas boasts superior long-term planning and reasoning capabilities compared to traditional language agents and other fine-tuned models.

Long-Term Planning

Atlas excels in long-term planning, enabling it help your team achieve milestones effectively. Unlike LMs, Atlas is trained to strategize and execute plans over extended periods. This advanced planning capability ensures that Atlas can navigate complex projects, adapt to changing requirements, and consistently meet critical goals.

Tool Usage

Thanks to its advanced long-term planning ability, Atlas is able to use the tools you use, such as GitHub, browsers, terminals, and more. With these tools, Atlas enhances your workflow without causing any disruption, allowing you to fully leverage its advanced capabilities with ease.

From Real-World Tasks; For Real-World Tasks

Atlas outperforms competitors on the SWE-Bench (May 2024) by adopting a novel approach to fine-tuning language models with trajectories from multiple tasks and prompting methods.

Trained on

8400

Real-World Task Trajectories From

  • GitHub Issues

  • GitLab Issues

  • LeetCode Questions

  • Stack overflow

  • CodeSearchNet

20.2%

SWE-Bench (On all 2294 Issues)

Supported Languages

Python

Java

TS

C++

Swift

GO

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