{"name":"Trae Agent: An LLM-Based Agent for General Software Engineering Tasks","description":"Trae Agent is an LLM-based agent designed for general-purpose software engineering tasks, offering a powerful CLI interface that understands natural language instructions. It enables complex software engineering workflows using various tools and LLM providers, featuring a transparent, modular, and research-friendly architecture. This project is ideal for studying AI agent architectures and developing novel agent capabilities.","github":"https://github.com/bytedance/trae-agent","url":"https://osrepos.com/repo/bytedance-trae-agent","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/bytedance-trae-agent","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/bytedance-trae-agent.md","json":"https://osrepos.com/repo/bytedance-trae-agent.json","topics":["agent","llm","software-engineering","Python","AI","Developer Tools","CLI","Automation"],"keywords":["agent","llm","software-engineering","Python","AI","Developer Tools","CLI","Automation"],"stars":null,"summary":"Trae Agent is an LLM-based agent designed for general-purpose software engineering tasks, offering a powerful CLI interface that understands natural language instructions. It enables complex software engineering workflows using various tools and LLM providers, featuring a transparent, modular, and research-friendly architecture. This project is ideal for studying AI agent architectures and developing novel agent capabilities.","content":"## Introduction\n\nTrae Agent, developed by Bytedance, is an innovative LLM-based agent tailored for a wide range of software engineering tasks. It provides a robust command-line interface (CLI) that can interpret natural language instructions and execute intricate software development workflows. Unlike many other CLI agents, Trae Agent boasts a transparent and modular architecture, making it an ideal platform for researchers and developers to study AI agent architectures, conduct ablation studies, and innovate new agent capabilities. Written in Python, it supports multiple LLM providers and offers a rich ecosystem of tools.\n\nKey features include Lakeview for concise summarization of agent steps, multi-LLM support (OpenAI, Anthropic, Google Gemini, OpenRouter, Ollama, Doubao), a rich tool ecosystem for file editing and bash execution, an interactive mode, and detailed trajectory recording for debugging and analysis.\n\n## Installation\n\nTo get started with Trae Agent, follow these simple steps:\n\n### Requirements\n\n*   [UV](https://docs.astral.sh/uv/) for dependency management.\n*   An API key for your chosen LLM provider (e.g., OpenAI, Anthropic, Google Gemini).\n\n### Setup\n\nbash\ngit clone https://github.com/bytedance/trae-agent.git\ncd trae-agent\nuv sync --all-extras\nsource .venv/bin/activate\n\n\n### Configuration\n\n**YAML Configuration (Recommended):**\n\n1.  Copy the example configuration file:\n    bash\n    cp trae_config.yaml.example trae_config.yaml\n    \n2.  Edit `trae_config.yaml` with your API credentials and preferences. This file is ignored by Git to protect your sensitive information.\n\n**Example `trae_config.yaml` snippet:**\n\nyaml\nagents:\n  trae_agent:\n    enable_lakeview: true\n    model: trae_agent_model\n    max_steps: 200\n    tools:\n      - bash\n      - str_replace_based_edit_tool\n      - sequentialthinking\n      - task_done\n\nmodel_providers:\n  anthropic:\n    api_key: your_anthropic_api_key\n    provider: anthropic\n  openai:\n    api_key: your_openai_api_key\n    provider: openai\n\nmodels:\n  trae_agent_model:\n    model_provider: anthropic\n    model: claude-sonnet-4-20250514\n    max_tokens: 4096\n    temperature: 0.5\n\n\n**Environment Variables (Alternative):**\n\nYou can also configure API keys using environment variables, typically stored in a `.env` file:\n\nbash\nexport OPENAI_API_KEY=\"your-openai-api-key\"\nexport ANTHROPIC_API_KEY=\"your-anthropic-api-key\"\n# ... and so on for other providers\n\n\n## Examples\n\nTrae Agent offers various ways to interact with it, from basic task execution to advanced Docker integration.\n\n### Basic Commands\n\nbash\n# Simple task execution\ntrae-cli run \"Create a hello world Python script\"\n\n# Check configuration\ntrae-cli show-config\n\n# Interactive mode\ntrae-cli interactive\n\n\n### Provider-Specific Examples\n\nbash\n# OpenAI\ntrae-cli run \"Fix the bug in main.py\" --provider openai --model gpt-4o\n\n# Anthropic\ntrae-cli run \"Add unit tests\" --provider anthropic --model claude-sonnet-4-20250514\n\n# OpenRouter (access to multiple providers)\ntrae-cli run \"Review this code\" --provider openrouter --model \"anthropic/claude-3-5-sonnet\"\n\n# Ollama (local models)\ntrae-cli run \"Comment this code\" --provider ollama --model qwen3\n\n\n### Docker Mode Commands\n\nTrae Agent can execute tasks within Docker containers, providing isolated environments.\n\nbash\n# Run a task in a new Docker container\ntrae-cli run \"Add tests for utils module\" --docker-image python:3.11\n\n# Attach to an existing Docker container\ntrae-cli run \"Update API endpoints\" --docker-container-id 91998a56056c\n\n\n### Interactive Mode Commands\n\nIn interactive mode, you can use:\n\n*   Type any task description to execute it\n*   `status` - Show agent information\n*   `help` - Show available commands\n*   `clear` - Clear the screen\n*   `exit` or `quit` - End the session\n\n## Why use Trae Agent?\n\nTrae Agent stands out as a powerful and flexible tool for software engineers and researchers alike. Its core strength lies in its ability to understand and act upon natural language instructions, automating complex development tasks. The project's emphasis on a transparent and modular architecture makes it an excellent choice for those looking to delve deeper into the mechanics of AI agents, allowing for easy modification, extension, and analysis of its framework. This research-friendly design fosters innovation and community contribution.\n\nBeyond its architectural advantages, Trae Agent offers practical benefits such as broad multi-LLM support, a comprehensive suite of tools for common engineering tasks, an interactive conversational interface for iterative development, and robust trajectory recording for detailed debugging. Its flexible YAML-based configuration and straightforward installation ensure a smooth developer experience, making it a valuable asset for enhancing productivity and exploring the future of AI-driven software development.\n\n## Links\n\n*   **GitHub Repository:** [https://github.com/bytedance/trae-agent](https://github.com/bytedance/trae-agent \"Trae Agent GitHub Repository\")\n*   **Technical Report (arXiv):** [https://arxiv.org/abs/2507.23370](https://arxiv.org/abs/2507.23370 \"Trae Agent Technical Report\")\n*   **Discord:** [https://discord.gg/VwaQ4ZBHvC](https://discord.gg/VwaQ4ZBHvC \"Join Trae Agent Discord\")\n*   **License:** [MIT License](https://github.com/bytedance/trae-agent/blob/main/LICENSE \"Trae Agent MIT License\")","metrics":{"detailViews":8,"githubClicks":3},"dates":{"published":null,"modified":"2025-11-30T20:00:38.000Z"}}