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67 lines
2.6 KiB
Markdown
67 lines
2.6 KiB
Markdown
# High-code Deployment of a Routing Agent
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This example demonstrates how to deploy a multi-agent system using AgentScope. The system is composed of a main
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routing agent equipped with a tool function named `create_worker` to dispatch tasks to specialized worker agents.
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Specifically, the routing agent is deployed as a chat endpoint in server hold by the `Quart` library.
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Once receiving an input request, we
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- set up a routing agent
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- load the session state if any
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- invoke the routing agent to handle the request, and return the streaming response
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- save the session state
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# Example Structure
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```
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planning_agent/
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├── main.py # Entry point to start the Quart server with routing agent
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├── tool.py # Tool function to create worker agents
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└── test_post.py # Preset test script to send requests to the server
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```
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## Note
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1. The printing messages from sub-agent/worker agents is converted to the streaming response of the tool
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function `create_worker`, meaning the sub-agent won't be exposing to the user directly.
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2. The sub-agent in `tool.py` is equipped with the following tools. For GitHub and AMap tools, they will be activated only
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if the corresponding environment variables are set.
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You can customize the toolset by modifying the `tool.py` file.
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| Tool | Description | Required Environment Variable |
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|-----------------------|-----------------------------------------------------|-------------------------------|
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| write/view text files | Read and write text files | - |
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| Playwright MCP server | Automate browser actions using Microsoft Playwright | - |
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| GitHub MCP server | Access GitHub repositories and data | GITHUB_TOKEN |
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| AMap MCP server | Access AMap services for location-based tasks | GAODE_API_KEY |
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3. Optionally, you can also expose the sub-agent's response to the user by modifying the `tool.py` file.
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## Quick Start
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Install the latest agentscope and Quart packages:
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```bash
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pip install agentscope quart
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```
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Ensure you have set `DASHSCOPE_API_KEY` in your environment for DashScope LLM API, or change the used model in
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both `main.py` and `tool.py` (Remember to change the formatter correspondingly).
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Set the environment variables for GitHub and AMap tools if needed.
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Run the Quart server:
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```bash
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python main.py
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```
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In another terminal, run the test script to send a request to the server:
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```bash
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python test_post.py
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```
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