An open-source AI agent that lives in your terminal.

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🎉 News

• 2026-04-15: Qwen OAuth free tier has been discontinued. To continue using Qwen Code, switch to Alibaba Cloud Coding Plan, OpenRouter, Fireworks AI, or bring your own API key. Run qwen auth to configure.

• 2026-04-13: Qwen OAuth free tier policy update: daily quota adjusted to 100 requests/day (from 1,000).

• 2026-04-02: Qwen3.6-Plus is now live! Get an API key from Alibaba Cloud ModelStudio to access it through the OpenAI-compatible API.

• 2026-02-16: Qwen3.5-Plus is now live!

Why Qwen Code?

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen series models. It helps you understand large codebases, automate tedious work, and ship faster.

• Multi-protocol, flexible providers: use OpenAI / Anthropic / Gemini-compatible APIs, Alibaba Cloud Coding Plan, OpenRouter, Fireworks AI, or bring your own API key.
• Open-source, co-evolving: both the framework and the Qwen3-Coder model are open-source—and they ship and evolve together.
• Agentic workflow, feature-rich: rich built-in tools (Skills, SubAgents) for a full agentic workflow and a Claude Code-like experience.
• Terminal-first, IDE-friendly: built for developers who live in the command line, with optional integration for VS Code, Zed, and JetBrains IDEs.

Installation

Quick Install (Recommended)

Linux / macOS

bash -c "$(curl -fsSL https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen.sh)"

Windows (Run as Administrator)

Works in both Command Prompt and PowerShell:

powershell -Command "Invoke-WebRequest 'https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen.bat' -OutFile (Join-Path $env:TEMP 'install-qwen.bat'); & (Join-Path $env:TEMP 'install-qwen.bat')"

Note

: It's recommended to restart your terminal after installation to ensure environment variables take effect.

Manual Installation

Prerequisites

Make sure you have Node.js 20 or later installed. Download it from nodejs.org.

NPM

npm install -g @qwen-code/qwen-code@latest

Homebrew (macOS, Linux)

brew install qwen-code

Quick Start

# Start Qwen Code (interactive)
qwen

# Then, in the session:
/help
/auth

On first use, you'll be prompted to sign in. You can run /auth anytime to switch authentication methods.

Example prompts:

What does this project do?
Explain the codebase structure.
Help me refactor this function.
Generate unit tests for this module.

Click to watch a demo video

🦞 Use Qwen Code for Coding Tasks in Claw

Copy the prompt below and paste it into your agent:

Use Qwen Code as a coding agent. First, fetch and read this skill reference:
https://raw.githubusercontent.com/QwenLM/qwen-code/refs/heads/main/.qwen/skills/qwen-code-claw/SKILL.md
Then follow the instructions inside to install, authenticate, and use Qwen Code via acpx for all coding tasks.

Authentication

Qwen Code supports the following authentication methods:

• API Key (recommended): use an API key from Alibaba Cloud Model Studio (Beijing / intl) or any supported provider (OpenAI, Anthropic, Google GenAI, and other compatible endpoints).
• Coding Plan: subscribe to the Alibaba Cloud Coding Plan (Beijing / intl) for a fixed monthly fee with higher quotas.

⚠️ Qwen OAuth was discontinued on April 15, 2026. If you were previously using Qwen OAuth, please switch to one of the methods above. Run qwen and then /auth to reconfigure.

API Key (recommended)

Use an API key to connect to Alibaba Cloud Model Studio or any supported provider. Supports multiple protocols:

• OpenAI-compatible: Alibaba Cloud ModelStudio, ModelScope, OpenAI, OpenRouter, and other OpenAI-compatible providers
• Anthropic: Claude models
• Google GenAI: Gemini models

The recommended way to configure models and providers is by editing ~/.qwen/settings.json (create it if it doesn't exist). This file lets you define all available models, API keys, and default settings in one place.

Quick Setup in 3 Steps

Step 1: Create or edit ~/.qwen/settings.json

Here is a complete example:

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "description": "Qwen3-Coder via Dashscope",
        "envKey": "DASHSCOPE_API_KEY"
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Step 2: Understand each field

Field	What it does
modelProviders	Declares which models are available and how to connect to them. Keys like openai, anthropic, gemini represent the API protocol.
modelProviders[].id	The model ID sent to the API (e.g. qwen3.6-plus, gpt-4o).
modelProviders[].envKey	The name of the environment variable that holds your API key.
modelProviders[].baseUrl	The API endpoint URL (required for non-default endpoints).
env	A fallback place to store API keys (lowest priority; prefer .env files or export for sensitive keys).
security.auth.selectedType	The protocol to use on startup (openai, anthropic, gemini, vertex-ai).
model.name	The default model to use when Qwen Code starts.

Step 3: Start Qwen Code — your configuration takes effect automatically:

qwen

Use the /model command at any time to switch between all configured models.

More Examples

Coding Plan (Alibaba Cloud ModelStudio) — fixed monthly fee, higher quotas

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.6-plus from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY"
      },
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.5-plus with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "glm-4.7",
        "name": "glm-4.7 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "glm-4.7 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "kimi-k2.5",
        "name": "kimi-k2.5 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "kimi-k2.5 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "BAILIAN_CODING_PLAN_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Subscribe to the Coding Plan and get your API key at Alibaba Cloud ModelStudio(Beijing) or Alibaba Cloud ModelStudio(intl).

Multiple providers (OpenAI + Anthropic + Gemini)

{
  "modelProviders": {
    "openai": [
      {
        "id": "gpt-4o",
        "name": "GPT-4o",
        "envKey": "OPENAI_API_KEY",
        "baseUrl": "https://api.openai.com/v1"
      }
    ],
    "anthropic": [
      {
        "id": "claude-sonnet-4-20250514",
        "name": "Claude Sonnet 4",
        "envKey": "ANTHROPIC_API_KEY"
      }
    ],
    "gemini": [
      {
        "id": "gemini-2.5-pro",
        "name": "Gemini 2.5 Pro",
        "envKey": "GEMINI_API_KEY"
      }
    ]
  },
  "env": {
    "OPENAI_API_KEY": "sk-xxxxxxxxxxxxx",
    "ANTHROPIC_API_KEY": "sk-ant-xxxxxxxxxxxxx",
    "GEMINI_API_KEY": "AIzaxxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "gpt-4o"
  }
}

Enable thinking mode (for supported models like qwen3.5-plus)

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (thinking)",
        "envKey": "DASHSCOPE_API_KEY",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.5-plus"
  }
}

Tip: You can also set API keys via export in your shell or .env files, which take higher priority than settings.json → env. See the authentication guide for full details.

Security note: Never commit API keys to version control. The ~/.qwen/settings.json file is in your home directory and should stay private.

Local Model Setup (Ollama / vLLM)

You can also run models locally — no API key or cloud account needed. This is not an authentication method; instead, configure your local model endpoint in ~/.qwen/settings.json using the modelProviders field.

Set generationConfig.contextWindowSize inside the matching provider entry and adjust it to the context length configured on your local server.

Ollama setup

1. Install Ollama from ollama.com
2. Pull a model: ollama pull qwen3:32b
3. Configure ~/.qwen/settings.json:

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3:32b",
        "name": "Qwen3 32B (Ollama)",
        "baseUrl": "http://localhost:11434/v1",
        "description": "Qwen3 32B running locally via Ollama",
        "generationConfig": {
          "contextWindowSize": 131072
        }
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3:32b"
  }
}

vLLM setup

1. Install vLLM: pip install vllm
2. Start the server: vllm serve Qwen/Qwen3-32B
3. Configure ~/.qwen/settings.json:

{
  "modelProviders": {
    "openai": [
      {
        "id": "Qwen/Qwen3-32B",
        "name": "Qwen3 32B (vLLM)",
        "baseUrl": "http://localhost:8000/v1",
        "description": "Qwen3 32B running locally via vLLM",
        "generationConfig": {
          "contextWindowSize": 131072
        }
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "Qwen/Qwen3-32B"
  }
}

Usage

As an open-source terminal agent, you can use Qwen Code in four primary ways:

1. Interactive mode (terminal UI)
2. Headless mode (scripts, CI)
3. IDE integration (VS Code, Zed)
4. SDKs (TypeScript, Python, Java)

Interactive mode

cd your-project/
qwen

Run qwen in your project folder to launch the interactive terminal UI. Use @ to reference local files (for example @src/main.ts).

Headless mode

cd your-project/
qwen -p "your question"

Use -p to run Qwen Code without the interactive UI—ideal for scripts, automation, and CI/CD. Learn more: Headless mode.

IDE integration

Use Qwen Code inside your editor (VS Code, Zed, and JetBrains IDEs):

• Use in VS Code
• Use in Zed
• Use in JetBrains IDEs

SDKs

Build on top of Qwen Code with the available SDKs:

• TypeScript: Use the Qwen Code SDK
• Python: Use the Python SDK
• Java: Use the Java SDK

Python SDK example:

import asyncio

from qwen_code_sdk import is_sdk_result_message, query


async def main() -> None:
    result = query(
        "Summarize the repository layout.",
        {
            "cwd": "/path/to/project",
            "path_to_qwen_executable": "qwen",
        },
    )

    async for message in result:
        if is_sdk_result_message(message):
            print(message["result"])


asyncio.run(main())

Commands & Shortcuts

Session Commands

• /help - Display available commands
• /clear - Clear conversation history
• /compress - Compress history to save tokens
• /stats - Show current session information
• /bug - Submit a bug report
• /exit or /quit - Exit Qwen Code

Keyboard Shortcuts

• Ctrl+C - Cancel current operation
• Ctrl+D - Exit (on empty line)
• Up/Down - Navigate command history

Learn more about Commands

Tip: In YOLO mode (--yolo), vision switching happens automatically without prompts when images are detected. Learn more about Approval Mode

Configuration

Qwen Code can be configured via settings.json, environment variables, and CLI flags.

File	Scope	Description
~/.qwen/settings.json	User (global)	Applies to all your Qwen Code sessions. Recommended for modelProviders and env.
.qwen/settings.json	Project	Applies only when running Qwen Code in this project. Overrides user settings.

The most commonly used top-level fields in settings.json:

Field	Description
modelProviders	Define available models per protocol (openai, anthropic, gemini, vertex-ai).
env	Fallback environment variables (e.g. API keys). Lower priority than shell export and .env files.
security.auth.selectedType	The protocol to use on startup (e.g. openai).
model.name	The default model to use when Qwen Code starts.

See the Authentication section above for complete settings.json examples, and the settings reference for all available options.

Benchmark Results

Terminal-Bench Performance

Agent	Model	Accuracy
Qwen Code	Qwen3-Coder-480A35	37.5%
Qwen Code	Qwen3-Coder-30BA3B	31.3%

Ecosystem

Looking for a graphical interface?

• AionUi A modern GUI for command-line AI tools including Qwen Code
• Gemini CLI Desktop A cross-platform desktop/web/mobile UI for Qwen Code

Troubleshooting

If you encounter issues, check the troubleshooting guide.

Common issues:

• Qwen OAuth free tier was discontinued on 2026-04-15: Qwen OAuth is no longer available. Run qwen → /auth and switch to API Key or Coding Plan. See the Authentication section above for setup instructions.

To report a bug from within the CLI, run /bug and include a short title and repro steps.

Connect with Us

• Discord: https://discord.gg/RN7tqZCeDK
• Dingtalk: https://qr.dingtalk.com/action/joingroup?code=v1,k1,+FX6Gf/ZDlTahTIRi8AEQhIaBlqykA0j+eBKKdhLeAE=&_dt_no_comment=1&origin=1

Acknowledgments

This project is based on Google Gemini CLI. We acknowledge and appreciate the excellent work of the Gemini CLI team. Our main contribution focuses on parser-level adaptations to better support Qwen-Coder models.