How to Use the OpenPanel MCP in AutoGen

Q: How do AutoGen agents coordinate when calling OpenPanel tools?

You can designate a single agent to handle all calls to trackevent or identifyuser. This agent listens to the group discussion and only executes the tool when the group reaches a consensus. This prevents multiple agents from sending duplicate events for the same action.

Q: How do I initialize user profiles in an AutoGen workflow with OpenPanel?

Your coordinator agent should call identifyuser as soon as a new conversation starts. This sets up the user's profile and ensures all subsequent events tracked by other agents are linked to the correct person. It keeps your multi-agent tracking organized and accurate.

Let your AutoGen agents debate and coordinate user tracking decisions before sending them to OpenPanel.

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Setup OpenPanel with AutoGen

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Consensus-driven event tracking in AutoGen

In an AutoGen multi-agent system, you don't want every agent spamming your analytics. This MCP Server allows your AutoGen coordinator agent to manage when to call `track_event` based on group discussion. This prevents duplicate OpenPanel tracking calls from polluting your dashboards. An AutoGen validation agent can verify the event payload before the execution agent sends it.

Collaborative OpenPanel profile management

Your AutoGen agents can work together to build a complete picture of user behavior. An AutoGen profiling agent can analyze user messages, while an analytics agent uses `identify_user` to update OpenPanel profile traits. When users hit specific milestones, the AutoGen agents can coordinate to run `increment_property` or `decrement_property` in OpenPanel. This keeps your user profiles perfectly aligned across different business domains.

Multi-agent OpenPanel telemetry routing

You can assign a dedicated AutoGen telemetry agent to handle all communication with the OpenPanel MCP Server. This agent listens to the group chat, extracts key milestones, and calls `track_event` in the background. This architecture keeps your AutoGen conversations focused and clean. The telemetry agent acts as a buffer, translating complex multi-agent discussions into simple, structured OpenPanel events.

Setup guide

Set up OpenPanel 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 OpenPanel 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="OpenPanel_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="OpenPanel_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by OpenPanel. 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 OpenPanel MCP in AutoGen

You can designate a single agent to handle all calls to `track_event` or `identify_user`. This agent listens to the group discussion and only executes the tool when the group reaches a consensus. This prevents multiple agents from sending duplicate events for the same action.

Yes, you can track metrics like conversation length or agent response times. By calling `track_event` at the end of a group chat, you can log these performance metrics directly to OpenPanel. This helps you identify which agents are taking too long or costing too much.

Your coordinator agent should call `identify_user` as soon as a new conversation starts. This sets up the user's profile and ensures all subsequent events tracked by other agents are linked to the correct person. It keeps your multi-agent tracking organized and accurate.

You can set up a critic agent to review all proposed tool calls before they run. If an agent tries to use `increment_property` with invalid arguments, the critic can flag the error and suggest the correct payload. This ensures your MCP integration only sends clean data.

The server processes your user IDs and numeric properties entirely in memory, sending them directly to OpenPanel's API. It never writes this sensitive telemetry data to disk. This ephemeral design ensures your users' behavioral records remain private and secure throughout the entire agent conversation.

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