Qwen 3 Agent Architectural spike.

This project demonstrates how to use the qwen-agent library to interact with a Qwen 3 language model, potentially leveraging MLX for local execution and incorporating tools like web search.

Description

The core example (agentic_search.py) sets up an AI assistant powered by a specified Qwen 3 model (e.g., qwen3:0.6B running locally via Ollama). It showcases how to:

• Configure the connection to the language model (local or API-based).
• Define and integrate tools (like code_interpreter and MCP-based tools like DuckDuckGo search).
• Run the agent with a user query and stream the responses.
• Example prompt included - it was difficult to get the model with agents to also output the hyperlinks.

The smaller try6b.py is meant to test that qwen models work locally. 0.6B is a very small model, so easy to download, and was actually a a lot of fun to use. Event working with tools, when used with agentic_search.py worked, up to a point.

Prerequisites

• Python 3.13 or higher
• uv package manager installed.
• A running Qwen 3 model endpoint (like one served by Ollama at http://localhost:11434) or appropriate API keys if using a hosted service.
• Optional: mise for managing development tools (like Node.js, though its specific use in this Python project isn't detailed in the provided context).

Installation

1. Clone the repository:

   git clone <your-repository-url>
   cd qwen3
   

2. Set up a virtual environment (recommended):

   # Using Python's built-in venv
   python -m venv .venv 
   source .venv/bin/activate # On Windows use `.venv\Scripts\activate`
   
   # Or using uv to create and activate
   uv venv .venv
   source .venv/bin/activate # On Windows use `.venv\Scripts\activate`
   

3. Install dependencies using uv: The project dependencies are listed in pyproject.toml. Install the project and its dependencies:

   uv pip install .
   

   Alternatively, install dependencies directly from the list:

   uv pip install mcp>=1.6.0 mlx>=0.25.1 mlx-lm>=0.24.0 python-dateutil>=2.9.0.post0 python-dotenv>=1.1.0 "qwen-agent[code-interpreter]>=0.0.20"
   

Usage

1. Configure the LLM:

   • Modify agentic_search.py to point to your Qwen 3 model endpoint or provide necessary API keys in the llm_cfg dictionary. The example is currently set up for a local Ollama endpoint.

2. Run the agent script:

   python agentic_search.py
   

   This will execute the predefined query in the script, run the agent, print progress dots (.) for each response chunk, and finally output the full structured response and the extracted content.

Dependencies

Key Python libraries used:

• qwen-agent: For creating and managing the AI agent.
• mlx / mlx-lm: Likely used for efficient model inference, especially on Apple Silicon.
• mcp: For integrating external tools via the Multi-Agent Collaboration Protocol.
• python-dotenv: For managing environment variables (e.g., API keys).
• rich: For beautiful terminal formatting and progress indicators.

See pyproject.toml for the full list of dependencies.

Contributing

This an architectural spike, I welcome your feedback through qwan.eu/contact.

License

Apache 2.0