How to Use the AgentOps (Agent Telemetry and Monitoring) MCP in AutoGen

Get real-time performance insights for your AutoGen multi-agent debates and conversations.

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Connect AgentOps (Agent Telemetry and Monitoring) MCP to AutoGen

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Setup AgentOps (Agent Telemetry and Monitoring) with AutoGen

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Track AutoGen agent debate performance

Use `get_project` to fetch current health metrics for your AutoGen agents. You see if your agents are stuck in a loop or actually making progress. This gives you a bird's-eye view of your multi-agent system. You catch deadlocks before they drain your token budget.

Audit AutoGen conversation spans

Call `get_span` to review what each AutoGen agent said during a debate. It lets you see the exact chain of reasoning that led to a specific decision. You can identify which agent is causing the bottleneck. It makes tuning your agent personalities much faster.

Analyze AutoGen trace outcomes

Run `get_trace` to see the full resolution path of your AutoGen agents. It shows you how they converged on a final answer. Your system's decision-making becomes transparent. You see the debate unfold in clear, actionable data points.

Setup guide

Set up AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) 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="AgentOps (Agent Telemetry and Monitoring)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent AgentOps (Agent Telemetry and Monitoring) 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="AgentOps (Agent Telemetry and Monitoring)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent AgentOps (Agent Telemetry and Monitoring) 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 AgentOps. 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 AgentOps (Agent Telemetry and Monitoring) MCP in AutoGen

Assign the MCP tools to your AssistantAgents. They can then call these tools to report or check their own performance during a debate.

Yes, they can query their own recent trace metrics. This helps them decide if they need to change their approach to reach a conclusion.

It logs the trace data you provide. You decide which parts of the conversation are sent to the telemetry server.

The tools are available to all agents in your group chat. They can share performance data to better coordinate their work.

Vinkius uses isolated sandboxes for every session. Your agent logs are encrypted in transit and never mixed with other users' data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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