Trae Agent Project Details

Project Overview

Trae Agent is an open-source intelligent agent developed by ByteDance, based on Large Language Models (LLMs), and designed for general software engineering tasks. The project provides a powerful command-line interface capable of understanding natural language instructions and executing complex software engineering workflows.

GitHub Repository: https://github.com/bytedance/trae-agent

Core Features

🌊 Lakeview

• Provides concise summaries for agent steps
• Helps users quickly understand the agent's execution process

🤖 Multi-LLM Support

• Supports official OpenAI API
• Supports official Anthropic API
• Flexible model selection mechanism

🛠️ Rich Tool Ecosystem

• File editing capabilities
• Bash command execution
• Sequential Thinking
• More extensible tools

🎯 Interactive Mode

• Conversational interface supporting iterative development
• Real-time interaction to enhance the development experience

📊 Trajectory Logging

• Detailed recording of all agent operations
• Supports debugging and analysis
• Automatically generates execution reports

⚙️ Flexible Configuration

• JSON-based configuration system
• Supports environment variable configuration
• Multi-level configuration priority

🚀 Easy Installation

• Simple pip-based installation process
• Recommended to use UV for project setup

Installation Guide

Recommended Installation Method (using UV)

git clone https://github.com/bytedance/trae-agent.git
cd trae-agent
uv sync

Environment Variable Configuration

# OpenAI API Key
export OPENAI_API_KEY="your-openai-api-key"

# Anthropic API Key  
export ANTHROPIC_API_KEY="your-anthropic-api-key"

Usage

Basic Task Execution

# Simple task
trae-cli run "Create a hello world Python script"

# Specify provider and model
trae-cli run "Fix the bug in main.py" --provider anthropic --model claude-sonnet-4-20250514

# Specify working directory
trae-cli run "Add unit tests for the utils module" --working-dir /path/to/project

# Save trajectory file for debugging
trae-cli run "Refactor the database module" --trajectory-file debug_session.json

# Force patch generation
trae-cli run "Update the API endpoints" --must-patch

Interactive Mode

# Start an interactive session
trae-cli interactive

# Use custom configuration
trae-cli interactive --provider openai --model gpt-4o --max-steps 30

Interactive Mode Commands

• Enter task description to execute a task
• status - View agent information
• help - Display available commands
• clear - Clear screen
• exit or quit - End session

Configuration Management

# Display current configuration
trae-cli show-config

# Use custom configuration file
trae-cli show-config --config-file my_config.json

Configuration File

Trae Agent uses a JSON configuration file (trae_config.json) for settings:

{
  "default_provider": "anthropic",
  "max_steps": 20,
  "model_providers": {
    "openai": {
      "api_key": "your_openai_api_key",
      "model": "gpt-4o",
      "max_tokens": 128000,
      "temperature": 0.5,
      "top_p": 1
    },
    "anthropic": {
      "api_key": "your_anthropic_api_key",
      "model": "claude-sonnet-4-20250514",
      "max_tokens": 4096,
      "temperature": 0.5,
      "top_p": 1,
      "top_k": 0
    }
  }
}

Configuration Priority

1. Command-line arguments (highest priority)
2. Configuration file values
3. Environment variables
4. Default values (lowest priority)

Built-in Tools

str_replace_based_edit_tool - File Operation Tool

• view - Display file content or directory listing
• create - Create a new file
• str_replace - Replace text within a file
• insert - Insert text at a specified line

bash - Shell Command Execution

• Execute commands and scripts
• Handle persistent state
• Manage long-running processes
• Capture output and errors

sequential_thinking - Structured Problem Solving

• Decompose complex problems
• Iterative thinking and refinement capabilities
• Hypothesis generation and validation

task_done - Task Completion Signal

• Mark task as successfully completed
• Provide final results and summary

Trajectory Logging

Trae Agent automatically records detailed execution trajectories for debugging and analysis:

# Automatically generate trajectory file
trae-cli run "Debug the authentication module"
# Saved to: trajectory_20250612_220546.json

# Custom trajectory file
trae-cli run "Optimize the database queries" --trajectory-file optimization_debug.json

Trajectory File Content

• LLM Interactions: All messages, responses, and tool calls
• Agent Steps: State transitions and decision points
• Tool Usage: Tools invoked and their results
• Metadata: Timestamps, token usage, and execution metrics

Contribution Guide

Contribution Workflow

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new features
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Submit a Pull Request

Development Guidelines

• Follow PEP 8 style guide
• Add tests for new features
• Update documentation as needed
• Use type hints appropriately
• Ensure all tests pass before committing

System Requirements

• Python 3.12+
• OpenAI API key (for OpenAI models)
• Anthropic API key (for Anthropic models)

Troubleshooting

Import Errors

# Try setting PYTHONPATH
PYTHONPATH=. trae-cli run "your task"

API Key Issues

# Verify if API keys are set
echo $OPENAI_API_KEY
echo $ANTHROPIC_API_KEY

# Check configuration
trae show-config

Permission Errors

# Ensure appropriate permissions for file operations
chmod +x /path/to/your/project