---
title: "trae-agent"
type: "tool"
slug: "bytedance-trae-agent"
canonical_url: "https://www.graphcanon.com/tools/bytedance-trae-agent"
github_url: "https://github.com/bytedance/trae-agent"
homepage_url: "https://www.trae.ai/"
stars: 11802
forks: 1300
primary_language: "Python"
license: "MIT"
categories: ["developer-tools", "ai-agents"]
tags: ["llm-frameworks", "software-engineering"]
updated_at: "2026-07-07T18:32:45.858135+00:00"
---

# trae-agent

> LLM-based software engineering tasks agent

Trae Agent is a Python-based, LLM-driven tool for executing general-purpose software engineering workflows. It supports multiple LLM providers and offers features like interactive mode, trajectory recording, and flexible configuration.

## Facts

- Repository: https://github.com/bytedance/trae-agent
- Homepage: https://www.trae.ai/
- Stars: 11,802 · Forks: 1,300 · Open issues: 139 · Watchers: 63
- Primary language: Python
- License: MIT
- Last pushed: 2026-02-05T11:21:00+00:00

## Categories

- [Developer Tools](/categories/developer-tools.md)
- [AI Agents](/categories/ai-agents.md)

## Tags

llm-frameworks, software-engineering

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## README (excerpt)

```text
# Trae Agent


 




**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.

## ✨ Features

- 🌊 **Lakeview**: Provides short and concise summarisation for agent steps
- 🤖 **Multi-LLM Support**: Works with OpenAI, Anthropic, Doubao, Azure, OpenRouter, Ollama and Google Gemini APIs
- 🛠️ **Rich Tool Ecosystem**: File editing, bash execution, sequential thinking, and more
- 🎯 **Interactive Mode**: Conversational interface for iterative development
- 📊 **Trajectory Recording**: Detailed logging of all agent actions for debugging and analysis
- ⚙️ **Flexible Configuration**: YAML-based configuration with environment variable support
- 🚀 **Easy Installation**: Simple pip-based installation

## 🚀 Installation

### Requirements
- UV (https://docs.astral.sh/uv/)
- API key for your chosen provider (OpenAI, Anthropic, Google Gemini, OpenRouter, etc.)

### Setup

```bash
git clone https://github.com/bytedance/trae-agent.git
cd trae-agent
uv sync --all-extras
source .venv/bin/activate
```

## ⚙️ Configuration

### YAML Configuration (Recommended)

1. Copy the example configuration file:
   ```bash
   cp trae_config.yaml.example trae_config.yaml
   ```

2. Edit `trae_config.yaml` with your API credentials and preferences:

```yaml
agents:
  trae_agent:
    enable_lakeview: true
    model: trae_agent_model  # the model configuration name for Trae Agent
    max_steps: 200  # max number of agent steps
    tools:  # tools used with Trae Agent
      - bash
      - str_replace_based_edit_tool
      - sequentialthinking
      - task_done

model_providers:  # model providers configuration
  anthropic:
    api_key: your_anthropic_api_key
    provider: anthropic
  openai:
    api_key: your_openai_api_key
    provider: openai

models:
  trae_agent_model:
    model_provider: anthropic
    model: claude-sonnet-4-20250514
    max_tokens: 4096
    temperature: 0.5
```

**Note:** The `trae_config.yaml` file is ignored by git to protect your API keys.

### Using Base URL
In some cases, we need to use a custom URL for the api. Just add the `base_url` field after `provider`, take the following config as an example:

```
openai:
    api_key: your_openrouter_api_key
    provider: openai
    base_url: https://openrouter.ai/api/v1
```
**Note:** For field formatting, use spaces only. Tabs (\t) are not allowed.

### Environment Variables (Alternative)

You can also configure API keys using environment variables and store them in the .env file:

```bash
export OPENAI_API_KEY="your-openai-api-key"
export OPENAI_BASE_URL="your-openai-base-url"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export ANTHROPIC_BASE_URL="your-anthropic-base-url"
export GOOGLE_API_KEY="your-google-api-key"
export GOOGLE_BASE_URL="your-google-base-url"
export OPENROUTER_API_KEY="your-openrouter-api-key"
export OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"
export DOUBAO_API_KEY="your-doubao-api-key"
export DOUBAO_BASE_URL="https://ark.cn-beijing.volces.com/api/v3/"
```
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/bytedance-trae-agent`](/api/graphcanon/tools/bytedance-trae-agent)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_