TRAE Agent – A smart assistant open-sourced by ByteDance, specifically designed for software engineering tasks

What is TRAE Agent？

TRAE Agent is an open-source, large language model (LLM)-based intelligent assistant developed by ByteDance, specifically designed for software engineering tasks. It operates autonomously like a seasoned software engineer, capable of handling complex tasks such as bug reproduction, proposing fixes, understanding codebases, and writing high-quality code. On the recent SWE-bench Verified leaderboard, TRAE Agent achieved a leading solve rate of 75.2%, successfully completing 376 out of 500 real-world tasks. It interacts with environments through a variety of tools—such as file editing, Bash execution, and structured reasoning—and supports multiple LLMs (e.g., Claude, Gemini) via a unified and flexible interface. Its patch selection strategy combines syntax voting and multi-agent validation, significantly improving accuracy. TRAE Agent provides full observability, logs every action, supports real-time terminal output, and enables users to easily create custom agents via its modular architecture.

Key Features of TRAE Agent

• Autonomous Operation and Codebase Exploration
  Capable of independently navigating codebases, identifying relevant files, and making necessary modifications.

• Multi-Model Support
  Compatible with multiple LLMs such as OpenAI and Anthropic. Easy to configure and flexible for various development needs.

• Powerful Tool Integration
  Equipped with built-in tools like file editors and script executors. Supports multi-turn interaction for complex programming scenarios.

• Multi-Step Task Planning
  Decomposes complex tasks into actionable steps and processes them sequentially.

• Robust Context Understanding

  • Web Context: Automatically fetches and extracts web content through online search.

  • Document Context: Supports uploading or linking up to 1000 .md or .txt files.

  • Multi-Type Inputs: Accepts diverse inputs like Figma links, product prototype documents, etc.

• Custom Agent Creation
  Users can configure custom prompts and toolsets to build personalized AI assistants for tasks like system design and code refactoring.

• Security and Audit Mechanism
  Includes full-chain auditing, recording all operations for easy debugging and traceability.

• Task Orchestration and Automation
  Integrated with MCP (Multi-tool Collaboration Protocol), enabling automated workflows including task decomposition, resource allocation, execution monitoring, and result aggregation.

• Code Generation and Optimization
  Supports generating code from natural language descriptions, auto-completion, and real-time preview and refinement.

Technical Architecture of TRAE Agent

• LLM as the Core Brain
  TRAE Agent relies on large language models for logical reasoning, task planning, and natural language understanding.

• Task Decomposition and Planning
  Uses the Chain of Thought (CoT) capabilities of LLMs to break down complex tasks into subtasks with executable steps.

• Step-by-Step Reasoning and Execution
  Follows the ReACT (Reasoning and Acting) framework—planning during the reasoning phase, executing sub-tasks via tools, and refining plans in response to execution feedback.

Project Repository

• GitHub: https://github.com/bytedance/TRAE-agent

Application Scenarios for TRAE Agent

• Code Generation and Auto-Completion
  Generates code snippets from natural language prompts and supports multiple programming languages.

• Code Optimization and Refactoring
  Identifies performance bottlenecks and suggests improvements using built-in code analysis tools.

• Test Case Generation
  Automatically creates test cases covering normal, edge, and error conditions.

• Automated Testing Pipeline
  Leverages the MCP protocol to automate the testing pipeline, including dynamic test generation and parallel execution.

• Code Quality Inspection
  Uses the CodeAnalysis Agent to check code quality, integrates security scanning tools via MCP, and auto-generates review reports.

• Real-Time Code Analysis
  Analyzes code in real-time to identify potential bugs and style issues, helping developers write more robust and compliant code.