Qwen-Agent

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 repository.

Installation

• Install the stable version from PyPI:

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.

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 repository.