# 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:**
    ```bash
    git clone <your-repository-url>
    cd qwen3
    ```

2.  **Set up a virtual environment (recommended):**
    ```bash
    # 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:
    ```bash
    uv pip install .
    ```
    Alternatively, install dependencies directly from the list:
    ```bash
    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:**
    ```bash
    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