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.

Project Status: The project is still being actively developed. Please refer to docs/roadmap.md and CONTRIBUTING 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

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:

   cp trae_config.yaml.example trae_config.yaml
   

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

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:

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/"