Trae Agent is an LLM-based agent for general purpose software engineering tasks. # Trae Agent [](https://arxiv.org/abs/2507.23370) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/bytedance/trae-agent/actions/workflows/pre-commit.yml) [](https://github.com/bytedance/trae-agent/actions/workflows/unit-test.yml) [](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. ## ✨ 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