High-code Deployment of a Routing Agent

This example demonstrates how to deploy a multi-agent system using AgentScope. The system is composed of a main routing agent equipped with a tool function named create_worker to dispatch tasks to specialized worker agents.

Specifically, the routing agent is deployed as a chat endpoint in server hold by the Quart library. Once receiving an input request, we

• set up a routing agent
• load the session state if any
• invoke the routing agent to handle the request, and return the streaming response
• save the session state

Example Structure

planning_agent/
    ├── main.py              # Entry point to start the Quart server with routing agent
    ├── tool.py              # Tool function to create worker agents
    └── test_post.py         # Preset test script to send requests to the server

Note

1. The printing messages from sub-agent/worker agents is converted to the streaming response of the tool function create_worker, meaning the sub-agent won't be exposing to the user directly.

2. The sub-agent in tool.py is equipped with the following tools. For GitHub and AMap tools, they will be activated only if the corresponding environment variables are set. You can customize the toolset by modifying the tool.py file.

3. Optionally, you can also expose the sub-agent's response to the user by modifying the tool.py file.

Quick Start

Install the latest agentscope and Quart packages:

pip install agentscope quart

Ensure you have set DASHSCOPE_API_KEY in your environment for DashScope LLM API, or change the used model in both main.py and tool.py (Remember to change the formatter correspondingly).

Set the environment variables for GitHub and AMap tools if needed.

Run the Quart server:

python main.py

In another terminal, run the test script to send a request to the server:

python test_post.py