How to Use the Helicone (LLM Observability) MCP in AutoGen

Q: Can AutoGen agents manage their own budgets using Helicone (LLM Observability)?

Yes, they can. Agents use querycosts to check their token spend in real time and adjust their conversation length accordingly.

Q: How does Helicone (LLM Observability) help debug multi-agent loops in AutoGen?

By running querysessions, you can trace the exact sequence of messages between agents. This helps you identify and break infinite loops before they waste tokens.

Q: Can I log user feedback on agent decisions?

Yes, you can use logfeedback to store user ratings on any agent's final decision. This data can be queried later using queryfeedback to improve agent prompts.

Give your AutoGen agents the ability to debate, monitor, and optimize their own LLM costs using this MCP Server.

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Connect Helicone (LLM Observability) MCP to AutoGen

Create your Vinkius account to connect Helicone (LLM Observability) 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 Helicone (LLM Observability) with AutoGen

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Let AutoGen agents debate token costs

Invoking `query_costs` gives your AutoGen agents the ability to debate their own token usage and monitor collective spend. You don't want to find out an agent got stuck in an infinite loop after it drained your API balance. If the cost exceeds a threshold, the agent can halt the debate or suggest a cheaper model. It keeps your autonomous workflows financially sustainable.

Track agent performance with MCP Server

Calling `query_latency` lets you track individual agent performance during multi-agent discussions. When multiple agents collaborate, identifying the bottleneck is incredibly difficult. Your coordinator agent can then run `query_sessions` to map out the entire conversation flow. This makes it easy to see which agent is slowing down the group.

Retrieve prompt histories during debates

Running `get_prompt_versions` lets your agents refer back to previous system prompts to maintain consistency during a debate. Using this MCP Server to call this tool lets them inspect how instructions have evolved over time. They can also run `query_prompts` to review the exact inputs sent by other agents. It ensures all participants in the debate are aligned on the current context.

Setup guide

Set up Helicone (LLM Observability) 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 Helicone (LLM Observability) 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="Helicone (LLM Observability)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Helicone (LLM Observability) 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="Helicone (LLM Observability)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Helicone (LLM Observability) MCP in AutoGen

You register the MCP tools with your AssistantAgent. Once connected, the agents can autonomously call tools like `query_requests` to analyze their own API usage.

Yes, they can. Agents use `query_costs` to check their token spend in real time and adjust their conversation length accordingly.

By running `query_sessions`, you can trace the exact sequence of messages between agents. This helps you identify and break infinite loops before they waste tokens.

Yes, you can use `log_feedback` to store user ratings on any agent's final decision. This data can be queried later using `query_feedback` to improve agent prompts.

Your agent conversation logs, prompts, and cost metrics are processed inside an ephemeral V8 sandbox. No persistent storage of your sensitive LLM payloads occurs on our end.

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