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.

Please note that this project is still in the alpha stage and being actively developed. We welcome various contributions from the community.

• Unit tests
• Richer CLI support
• Migrate to Rust

• 🌊 Lakeview: Provides short and concise summarisation for agent steps
• 🤖 Multi-LLM Support: Works with OpenAI and Anthropic official 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: JSON-based configuration with environment variable support
• 🚀 Easy Installation: Simple pip-based installation

We strongly recommend using UV to setup the project.

git clone <repository-url>
cd trae-agent
uv sync

We recommand to configure Trae Agent using the config file.

You can also set your API keys as environment variables:

# For OpenAI
export OPENAI_API_KEY="your-openai-api-key"

# For Anthropic
export ANTHROPIC_API_KEY="your-anthropic-api-key"

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

The main entry point is the trae command with several subcommands:

# Basic task execution
trae-cli run "Create a Python script that calculates fibonacci numbers"

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

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

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

# Force to generate patches
trae-cli run "Update the API endpoints" --must-patch

# Start interactive session
trae-cli interactive

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

In interactive mode, you can:

• Type any task description to execute it
• Use status to see agent information
• Use help for available commands
• Use clear to clear the screen
• Use exit or quit to end the session

trae-cli show-config

# With custom config file
trae-cli show-config --config-file my_config.json

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)
2. Configuration file values
3. Environment variables
4. Default values (lowest)

• OPENAI_API_KEY - OpenAI API key
• ANTHROPIC_API_KEY - Anthropic API key

Trae Agent comes with several built-in tools:

• str_replace_based_edit_tool: Create, edit, view, and manipulate files

  • view - Display file contents or directory listings
  • create - Create new files
  • str_replace - Replace text in files
  • insert - Insert text at specific lines

• bash: Execute shell commands and scripts

  • Run commands with persistent state
  • Handle long-running processes
  • Capture output and errors

• sequential_thinking: Structured problem-solving and analysis

  • Break down complex problems
  • Iterative thinking with revision capabilities
  • Hypothesis generation and verification

• task_done: Signal task completion

  • Mark tasks as successfully completed
  • Provide final results and summaries

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

# Auto-generated trajectory file
trae-cli run "Debug the authentication module"
# Saves to: trajectory_20250612_220546.json

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

Trajectory files contain:

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

For more details, see TRAJECTORY_RECORDING.md.

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

• Follow PEP 8 style guidelines
• Add tests for new features
• Update documentation as needed
• Use type hints where appropriate
• Ensure all tests pass before submitting

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

Import Errors:

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

API Key Issues:

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

# Check configuration
trae show-config

Permission Errors:

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

This project is licensed under the MIT License - see the LICENSE file for details.

We thank Anthropic for building the anthropic-quickstart project that served as a valuable reference for the tool ecosystem.