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examples/deployment/planning_agent/README.md

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+# 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.
+
+| Tool                  | Description                                         | Required Environment Variable |
+|-----------------------|-----------------------------------------------------|-------------------------------|
+| write/view text files | Read and write text files                           | -                             |
+| Playwright MCP server | Automate browser actions using Microsoft Playwright | -                             |
+| GitHub MCP server     | Access GitHub repositories and data                 | GITHUB_TOKEN                  |
+| AMap MCP server       | Access AMap services for location-based tasks       | GAODE_API_KEY                 |
+
+
+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:
+
+```bash
+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:
+
+```bash
+python main.py
+```
+
+In another terminal, run the test script to send a request to the server:
+
+```bash
+python test_post.py
+```