How to Use the Langfuse (LLM Tracing & Evals) MCP in AutoGen

Q: Can I evaluate AutoGen agent decisions with Langfuse (LLM Tracing & Evals)?

Yes, you use createscore to write human feedback or automated evaluation metrics directly to specific agent traces. This lets you track whether your AutoGen agents are converging on correct decisions over time.

Q: How do I monitor API costs for AutoGen using Langfuse (LLM Tracing & Evals)?

Run getdailymetrics to pull the total USD cost and latency statistics generated by your agent runs. This keeps you from blowing your budget when agents enter long debate loops.

Q: How do I pull prompt templates used by my agents?

Use listprompts to extract the active prompt versions managed in your central registry. This ensures your AutoGen agents always debate using the latest system instructions.

Track multi-agent debates and score consensus runs in AutoGen using Langfuse tracing tools.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Langfuse (LLM Tracing & Evals) MCP to AutoGen

Create your Vinkius account to connect Langfuse (LLM Tracing & Evals) 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.

GDPR Free for Subscribers

Setup Langfuse (LLM Tracing & Evals) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Trace multi-agent debates in AutoGen

`list_traces` is the tool your AutoGen agents use to map their entire debate history when negotiating a decision. When agents argue back and forth, they generate complex execution paths that are impossible to debug without structured telemetry. This tool exposes the exact sequence of agent handoffs, showing you who said what and when. You also get `get_trace` to pull the complete nested graph of a single run. Instead of guessing why a security agent blocked a performance agent, your code inspects the actual inputs and outputs of that specific negotiation step.

Audit multi-agent costs and latency

`get_daily_metrics` pulls the rolled-up USD cost and aggregated latency statistics generated by your AutoGen agents. Running multiple agents in a loop gets expensive fast, especially when they get stuck in debate loops. This tool gives you the exact financial and temporal cost of their conversations. To break down costs further, use `list_sessions` to group traces by user interaction. You will immediately see which agent-to-agent negotiations are burning your API budget and where latency spikes occur.

Score agent consensus via this MCP Server

`create_score` lets your AutoGen supervisor agent attach evaluation metrics directly to a trace or observation based on the final debate outcome. If the agents reach a bad consensus, you log a low score to flag the run for review. This creates a feedback loop for refining agent prompts. You can also use `list_scores` to audit past evaluations and find patterns in agent failures. It helps you track whether your prompt updates actually improve the quality of agent decisions over time.

Setup guide

Set up Langfuse (LLM Tracing & Evals) 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 Langfuse (LLM Tracing & Evals) 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="Langfuse (LLM Tracing & Evals)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Langfuse (LLM Tracing & Evals) 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="Langfuse (LLM Tracing & Evals)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Langfuse (LLM Tracing & Evals) 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 Langfuse. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Langfuse (LLM Tracing & Evals) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Langfuse (LLM Tracing & Evals) MCP in AutoGen

You configure the MCP Server in your AutoGen setup to automatically log every agent interaction. Use `list_traces` to inspect the flow of messages between debating agents and locate where consensus broke down.

Yes, you use `create_score` to write human feedback or automated evaluation metrics directly to specific agent traces. This lets you track whether your AutoGen agents are converging on correct decisions over time.

Run `get_daily_metrics` to pull the total USD cost and latency statistics generated by your agent runs. This keeps you from blowing your budget when agents enter long debate loops.

Use `list_prompts` to extract the active prompt versions managed in your central registry. This ensures your AutoGen agents always debate using the latest system instructions.

Your LLM traces and evaluation scores go directly to your self-hosted or cloud Langfuse instance. Vinkius runs the MCP Server in a secure, ephemeral sandbox that never stores your prompt templates or session entities.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript