Qwen-Agent
VerifiedOpen-source framework for building Qwen-based LLM agents with tool calling and planning.
What is Qwen-Agent?
Qwen-Agent is a Python framework that turns Qwen language models into capable agents. It supplies built-in support for function calling, multi-step planning, retrieval-augmented generation, and persistent memory so agents can complete complex tasks.
Developers connect the framework to either DashScope or a self-hosted OpenAI-compatible endpoint. The library then handles prompt construction, tool execution loops, and optional GUI or MCP integrations.
It is intended for Python users who want to prototype or deploy agents quickly without writing low-level orchestration code from scratch.
Capabilities
What you can build with Qwen-Agent
Browser Assistant
An agent that can search the web, open pages, and summarize findings using built-in browsing tools.
Code Interpreter
Run and debug Python code in an interactive session while the agent plans and iterates on solutions.
Custom Assistant
Build domain-specific assistants by registering your own tools and memory stores on top of any Qwen model.
Install Qwen-Agent
pip install -U "qwen-agent[gui,rag,code_interpreter,mcp]"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.- 1Install via pip with optional extras for GUI, RAG, code execution, and MCP support.
- 2Set DASHSCOPE_API_KEY or start a local vLLM/Ollama server exposing an OpenAI-compatible endpoint.
- 3Import Agent and LLM classes from qwen_agent and configure them with your model settings.
- 4Register tools or use the provided examples such as assistant_qwen3.py.
- 5Run the agent in a Python script or launch the Gradio GUI for interactive testing.
Works with
Qwen-Agent: pros & cons
Pros
- +Native support for the latest Qwen models including vision and reasoning variants
- +Clean abstractions for tool calling, planning, and memory reduce boilerplate
- +Active development with frequent model and benchmark updates
- +Includes both CLI examples and a Gradio-based GUI
Cons
- –Code interpreter is not sandboxed and is meant only for local testing
- –GUI requires Python 3.10 or newer
- –Best performance still depends on access to strong Qwen endpoints or GPUs
Frequently asked questions
No. You can run it with a free or self-hosted Qwen model, though DashScope offers a convenient paid option.
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