How to Use the Lingyi Wanwu MCP in AutoGen

Q: How do I register the Lingyi Wanwu MCP server tools with AutoGen agents?

Install autogen-extmcp and use mcpservertools with your Vinkius server parameters. You can then pass the resulting tool list directly to your AssistantAgent constructor.

Q: Can AutoGen agents use Lingyi Wanwu to moderate their own chats using this MCP server?

Yes, you can assign the checkmoderation tool to a specific supervisor agent. That agent will run checks on every message exchange to keep the entire conversation compliant with your safety rules.

Q: How do AutoGen agents select the right Lingyi Wanwu model dynamically?

Agents can run the listmodels tool to see what endpoints are currently active. Based on the complexity of the task, the coordinating agent can route the next sub-task to the most efficient model.

Q: Is the chat history processed by this MCP server kept private?

Chat history strings used in chatcompletions are processed purely in-memory within zero-trust V8 isolates. No logs, prompts, or conversation states are ever written to persistent storage, guaranteeing complete confidentiality for your agent-to-agent dialogues.

Let AutoGen agents debate and coordinate using Yi models via this high-performance MCP server.

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Connect Lingyi Wanwu MCP to AutoGen

Create your Vinkius account to connect Lingyi Wanwu to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lingyi Wanwu with AutoGen

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Run multi-agent debates with Yi models

This MCP Server allows your AutoGen `AssistantAgent` instances to execute `chat_completions` during multi-agent conversations. Multiple agents can discuss a problem, challenge each other's reasoning, and refine their output using Yi's language generation capabilities. Complex tasks that stump a single model are solved easily here. By dividing the work among specialized agents, you get highly deliberate and accurate results.

Implement automated moderation in agent conversations

The `check_moderation` tool acts as a dedicated safety agent within your AutoGen group chat. Before any agent's response is broadcast to the rest of the group, this tool inspects the text for policy violations. This prevents toxic or out-of-bounds content from derailing the debate. You can configure the group chat manager to immediately mute or redirect agents that trigger a moderation flag.

Track collective token burn across agent groups

You can monitor the resource consumption of your entire agent swarm by calling the `get_usage` tool. Because multi-agent loops can easily spiral into endless back-and-forth discussions, checking these statistics is vital. This tool gives your coordinator agent the data it needs to shut down conversations that exceed a defined token threshold. Your budget stays protected without you having to manually watch the terminal.

Setup guide

Set up Lingyi Wanwu MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Lingyi Wanwu tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Lingyi Wanwu_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Lingyi Wanwu data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Lingyi Wanwu_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Lingyi Wanwu data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lingyi Wanwu. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Lingyi Wanwu MCP in AutoGen

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your Vinkius server parameters. You can then pass the resulting tool list directly to your `AssistantAgent` constructor.

Yes, you can assign the `check_moderation` tool to a specific supervisor agent. That agent will run checks on every message exchange to keep the entire conversation compliant with your safety rules.

Agents can run the `list_models` tool to see what endpoints are currently active. Based on the complexity of the task, the coordinating agent can route the next sub-task to the most efficient model.

This server supports both standard stdio and Streamable HTTP transports. AutoGen's adapter handles the schema conversion automatically so you can use whichever transport fits your infrastructure.

Chat history strings used in `chat_completions` are processed purely in-memory within zero-trust V8 isolates. No logs, prompts, or conversation states are ever written to persistent storage, guaranteeing complete confidentiality for your agent-to-agent dialogues.

Use it with your favorite AI tools

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