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Basic Information

Trae Agent is an open-source, LLM-driven command-line agent designed to perform general-purpose software engineering tasks and to serve as a research-friendly platform for studying agent behavior. It provides a CLI that accepts natural language instructions and executes multi-step workflows using a modular toolset. The project emphasizes a transparent, extensible architecture so researchers and developers can modify, extend, and analyze agent components, conduct ablation studies, and develop new capabilities. It supports multiple LLM providers and models, records detailed execution trajectories for analysis, and can be configured via YAML or environment variables. Optional MCP services and local model support are provided to integrate browser automation or alternative runtimes. The repo includes installation, configuration, usage examples, and development guidance for contributors.

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App Details

Features
Trae Agent includes several highlighted capabilities: Lakeview short-step summarization for concise step outputs, multi-LLM support for OpenAI, Anthropic, Doubao, Azure, OpenRouter, Ollama and Google Gemini, and a rich tool ecosystem that includes file editing, bash execution, structured/sequential thinking tools, and task completion utilities. It offers an interactive conversational mode for iterative development, trajectory recording that logs LLM interactions and tool usage for debugging and research, YAML-based configuration with environment variable fallbacks, optional MCP service integration for external helpers, and simple pip-based installation. Examples and provider-specific invocation patterns are provided to demonstrate model and provider flexibility.
Use Cases
Trae Agent helps developers and researchers automate and accelerate common software engineering activities by translating natural language tasks into executable agent workflows. End users can run commands like creating boilerplate code, fixing bugs, adding unit tests, refactoring modules, reviewing code, and generating documentation using different LLM providers and models. Researchers benefit from the modular, transparent design and trajectory recording for reproducible experiments, ablation studies, and toolchain analysis. Configurability via YAML or environment variables plus interactive mode and trajectory files makes it practical for debugging, reproducing sessions, and integrating into development environments. Optional MCP and local model support broaden deployment and testing options.

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