bytedance

Trae Agent

Name: Trae Agent
Author: bytedance

AI community

SOURCE https://github.com/bytedance/trae-agent

Description

Trae Agent is an LLM-based agent for general purpose software engineering tasks.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

Trae Agent

[![arXiv:2507.23370](https://img.shields.io/badge/TechReport-arXiv%3A2507.23370-b31a1b)](https://arxiv.org/abs/2507.23370) [![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Pre-commit](https://github.com/bytedance/trae-agent/actions/workflows/pre-commit.yml/badge.svg)](https://github.com/bytedance/trae-agent/actions/workflows/pre-commit.yml) [![Unit Tests](https://github.com/bytedance/trae-agent/actions/workflows/unit-test.yml/badge.svg)](https://github.com/bytedance/trae-agent/actions/workflows/unit-test.yml) [![Discord](https://img.shields.io/discord/1320998163615846420?label=Join%20Discord&color=7289DA)](https://discord.gg/VwaQ4ZBHvC)

**Trae Agent** is an LLM-based agent for general purpose software engineering tasks. It provides a powerful CLI interface that can understand natural language instructions and execute complex software engineering workflows using various tools and LLM providers.

For technical details please refer to [our technical report](https://arxiv.org/abs/2507.23370).

**Project Status:** The project is still being actively developed. Please refer to [docs/roadmap.md](docs/roadmap.md) and [CONTRIBUTING](CONTRIBUTING.md) if you are willing to help us improve Trae Agent.

**Difference with Other CLI Agents:** Trae Agent offers a transparent, modular architecture that researchers and developers can easily modify, extend, and analyze, making it an ideal platform for **studying AI agent architectures, conducting ablation studies, and developing novel agent capabilities**. This **_research-friendly design_** enables the academic and open-source communities to contribute to and build upon the foundational agent framework, fostering innovation in the rapidly evolving field of AI agents.