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Trae Agent

Trae Agent is an open-source Python command-line agent that uses large language models and tools to carry out general-purpose software engineering tasks. It accepts natural-language instructions and supports file editing, bash execution, interactive sessions and trajectory recording.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Trae Agent — OpenViST project profile in Coding Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Trae Agent when you want to delegate a software engineering task through a configurable command-line agent, or when you want a modifiable framework for studying agent architecture.

Who it is for

  • Developers who want a CLI agent to work on coding tasks such as bug fixes, tests, refactoring and documentation.
  • Researchers and developers interested in inspecting or modifying an agent framework.

Documented uses

  • Submit a coding task in natural language through the CLI.
  • Use an interactive conversation for iterative development.
  • Configure a supported LLM provider and model.
  • Record agent actions for debugging or analysis.
  • Inspect or extend a modular agent architecture.
Before you choose

Requirements & limits

The project is publicly available on GitHub under the MIT license. Setup requires Python 3.12 or later, UV and an API key for the chosen model provider.

  • The documented interface is a CLI, with both single-task and interactive modes.
  • Provider options include hosted services and Ollama for local models.
  • The repository documents file editing, bash execution and sequential thinking among its tools.
  • YAML configuration is recommended; environment variables are also supported.
  • Trajectory recording is available for debugging and analysis.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Trae Agent and prepare its configuration

Set up the project and create a YAML configuration for a selected model provider.

Before you start

  • Python 3.12 or later
  • UV
  • An API key for the chosen model provider

Steps

  1. Clone the Trae Agent repository and change into its directory.

  2. Run the command below and activate the resulting virtual environment.

    Documented command
    uv sync --all-extras

  3. Copy trae_config.yaml.example to trae_config.yaml.

  4. Edit the YAML configuration with the provider credentials and preferences.

Expected outcome

The project dependencies are installed and a provider configuration file is prepared.

Guide limitations

  • This setup sequence does not run an agent task.
  • The API key must come from the selected provider.
Documentation 1 ↗
Source-based practical guide

Run a software task from the CLI

Submit a natural-language software engineering task using a configured provider and model.

Before you start

  • Trae Agent installed and configured
  • A provider API key
  • A project directory for tasks that operate on existing code

Steps

  1. Open a terminal in the project directory.

  2. Run trae-cli run followed by a task instruction, such as Fix the bug in main.py.

  3. For an explicit provider and model, add the documented --provider and --model options.

  4. Use --working-dir to specify a custom project directory when needed.

Expected outcome

The CLI receives the task instruction and runs the agent with the selected configuration.

Guide limitations

  • The README describes example commands but does not guarantee task results.
  • Model access depends on the chosen provider and its credentials.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
7 October 2026
Original website title
bytedance/trae-agent · Official homepage title · retained in its source language
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