bytedance/trae-agent

★ 11,540PythonAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((trae-agent))
    What it does
      Writes code
      Edits files
      Runs commands
      Multi-step workflows
    How to use
      CLI interface
      Interactive mode
      Docker isolation
    AI providers
      OpenAI
      Anthropic
      Google Gemini
    Features
      Lakeview summaries
      Action replay logs
      Modular design

mindmap root((trae-agent)) What it does Writes code Edits files Runs commands Multi-step workflows How to use CLI interface Interactive mode Docker isolation AI providers OpenAI Anthropic Google Gemini Features Lakeview summaries Action replay logs Modular design

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Things people build with this

USE CASE 1

Describe a coding task in plain English and let the agent write and edit the code for you.

USE CASE 2

Run coding experiments inside a Docker container to keep your main machine unaffected.

USE CASE 3

Study how an AI agent plans and executes multi-step software tasks by replaying its action logs.

Tech stack

PythonOpenAI APIAnthropic APIGoogle GeminiDockerYAML

Getting it running

Difficulty · moderate Time to first run · 30min

Requires API keys for at least one AI model provider (OpenAI, Anthropic, or Gemini) and a YAML config file before you can run any task.

License terms were not described in the explanation.

In plain English

Trae Agent is an open-source software engineering agent from ByteDance. It takes natural language instructions from a developer and carries out coding tasks autonomously: writing code, editing files, running commands, and working through multi-step workflows. You describe what you want done in plain English and the agent figures out the steps. The agent runs through a command-line interface and can be connected to several AI model providers, including OpenAI, Anthropic, Google Gemini, and others. You configure which model to use through a YAML file or environment variables, then point the agent at a project directory and give it a task. An interactive mode is also available for back-and-forth conversation with the agent as it works. One feature called Lakeview generates short summaries of what the agent is doing at each step, making it easier to follow along without reading every action in detail. The agent also records a full log of all actions it takes, which can be replayed or analyzed afterward. Tasks can optionally run inside a Docker container for isolation, which is useful when you want the agent to have access to a specific environment without affecting your main machine. The project describes itself as research-friendly. The codebase is designed to be modular and easy to modify, making it suitable for people studying how AI agents work or experimenting with new agent capabilities. It is actively developed and accepts contributions. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Using trae-agent, set up the YAML config to use Anthropic Claude as the model and point it at my project directory, then give it the task: add input validation to the login form.

Prompt 2

Configure trae-agent with the OpenAI provider and run it in Docker isolation mode to refactor my Python service to use async functions throughout.

Prompt 3

Use trae-agent's interactive mode to iteratively fix failing unit tests in my repo, start with the test output and have it suggest and apply fixes one by one.

Prompt 4

Set up trae-agent with the Lakeview feature enabled so I can follow what it is doing step by step while it adds a REST endpoint to my Flask app.

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