Qwen-Agent

Qwen-Agent <https://github.com/QwenLM/Qwen-Agent>__ is a framework for developing LLM applications based on the instruction following, tool usage, planning, and memory capabilities of Qwen.

This is a simple tutorial on using Qwen-Agent to quickly experience the agentic capabilities of Qwen3. For more detailed information, please refer to Qwen-Agent <https://github.com/QwenLM/Qwen-Agent>__ repository.

Installation

• Install the stable version from PyPI:

.. code:: bash

pip install -U "qwen-agent[gui,rag,code_interpreter,mcp]"

Or use pip install -U qwen-agent for the minimal requirements.

The optional requirements, specified in double brackets, are:

[gui] for Gradio-based GUI support;

[rag] for RAG support;

[code_interpreter] for Code Interpreter support;

[mcp] for MCP support.

Developing Your Own Agent

Qwen3 excels in tool calling capabilities. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

.. code:: python

import os from qwen_agent.agents import Assistant

Define LLM

llm_cfg = { # Use a custom endpoint compatible with OpenAI API by vLLM/SGLang: 'model': 'Qwen/Qwen3-32B', 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY',

   # 'generate_cfg': {
   #     # When using vLLM/SGLang OAI API, pass the parameter of whether to enable thinking mode in this way
   #     'extra_body': {
   #         'chat_template_kwargs': {'enable_thinking': False}
   #     },
   #
   #     # Add: When the content is `<think>this is the thought</think>this is the answer`
   #     # Do not add: When the response has been separated by reasoning_content and content
   #     # This parameter will affect the parsing strategy of tool call
   #     # 'thought_in_content': True,
   # },

}

llm_cfg = {

# Use the model service provided by DashScope:

'model': 'qwen3-235b-a22b',

'model_type': 'qwen_dashscope',

# 'generate_cfg': {

# # When using the Dash Scope API, pass the parameter of whether to enable thinking mode in this way

# 'enable_thinking': False,

# },

}

llm_cfg = {

# Use the OpenAI-compatible model service provided by DashScope:

'model': 'qwen3-235b-a22b',

'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1',

'api_key': os.getenv('DASHSCOPE_API_KEY'),

# 'generate_cfg': {

# # When using Dash Scope OAI API, pass the parameter of whether to enable thinking mode in this way

# 'extra_body': {

# 'enable_thinking': False

# },

# },

}

Define Tools

tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ]

Define Agent

bot = Assistant(llm=llm_cfg, function_list=tools)

Streaming generation

messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses)

For more detailed examples and MCP cookbooks, please refer to Qwen-Agent <https://github.com/QwenLM/Qwen-Agent>__ repository.