Can I see the exact system instruction for a specific prompt version?

Yes. Use the `list_prompts` tool to browse your managed templates. Your agent can retrieve the exact text and variables for any deployed prompt version, making it easy to audit AI logic through natural conversation.

How do I log human feedback for a specific trace?

Use the `create_score` tool by providing the Trace ID and a JSON payload defining the score name (e.g. 'user-satisfaction') and value. Your agent will attach this structured data directly to the Langfuse record.

Can my agent report on my LLM spending for the current day?

Absolutely. The `get_daily_metrics` tool retrieves aggregated USD costs and average latency metrics from Langfuse. Your agent can summarize these statistics to help you monitor your infrastructure budget in real-time.

Langfuse (LLM Tracing & Evals) MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: Langfuse (LLM Tracing & Evals)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Langfuse (LLM Tracing & Evals) through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Langfuse (LLM Tracing & Evals) Assistant",
            instructions=(
                "You help users interact with Langfuse (LLM Tracing & Evals). "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Langfuse (LLM Tracing & Evals)"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Langfuse (LLM Tracing & Evals) MCP Server

Connect your Langfuse account to any AI agent and take full control of your LLM observability, prompt management, and quality evaluation through natural conversation.

The OpenAI Agents SDK auto-discovers all 10 tools from Langfuse (LLM Tracing & Evals) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Langfuse (LLM Tracing & Evals), another analyzes results, and a third generates reports, all orchestrated through the Vinkius.

What you can do

Trace Orchestration — List and retrieve detailed traces of LLM API sessions, exposing latencies, token counts, and exact chained payloads directly from your agent
Prompt Vault Access — Query actively managed prompt templates and versions to inspect system instructions and expected input variables
Observation Analysis — Deep-dive into individual spans, events, and generations within a trace to pinpoint failures or performance bottlenecks securely
Evaluation & Scoring — Attach structured human feedback or automated evaluation metrics to specific traces to monitor model grounding and accuracy
Usage Metrics — Generate aggregated daily reports on USD costs and average latency to track your AI infrastructure spending in real-time
Session Monitoring — Extract correlated user sessions to understand multi-turn interaction boundaries and improve long-term agentic workflows

The Langfuse (LLM Tracing & Evals) MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Langfuse (LLM Tracing & Evals) to OpenAI Agents SDK via MCP

Follow these steps to integrate the Langfuse (LLM Tracing & Evals) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from Langfuse (LLM Tracing & Evals)

Why Use OpenAI Agents SDK with the Langfuse (LLM Tracing & Evals) MCP Server

OpenAI Agents SDK provides unique advantages when paired with Langfuse (LLM Tracing & Evals) through the Model Context Protocol.

Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Langfuse (LLM Tracing & Evals) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Langfuse (LLM Tracing & Evals) MCP Server delivers measurable value.

Automated workflows: build agents that query Langfuse (LLM Tracing & Evals), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents — one queries Langfuse (LLM Tracing & Evals), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Langfuse (LLM Tracing & Evals) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Langfuse (LLM Tracing & Evals) to resolve tickets, look up records, and update statuses without human intervention

Langfuse (LLM Tracing & Evals) MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to OpenAI Agents SDK via MCP:

create_observation

Create a new LLM observation (span, event, generation) inside a trace

create_score

g. 1-5 stars) or automated pipeline metrics bounding exactly onto the specified Trace or Observation. Attach human feedback or evaluation metrics to a trace/observation

get_daily_metrics

Generate rolled-up USD cost and aggregated latency statistics

get_observation

Retrieve explicit span or generation context within a trace

get_trace

Get complete telemetry and nested graph for a single trace

list_observations

List raw observation objects spanning across traces

list_prompts

Extract actively managed prompt templates and versions

list_scores

List all explicit scores mapping quality or cost algorithms

list_sessions

List high-level user session entities encapsulating multiple traces

list_traces

List all traces tracking LLM API sessions

Example Prompts for Langfuse (LLM Tracing & Evals) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Langfuse (LLM Tracing & Evals) immediately.

"List the last 5 traces in my Langfuse project"

"Show me the instructions for the 'customer-support-v3' prompt"

"What was our total LLM spending for today?"

Troubleshooting Langfuse (LLM Tracing & Evals) MCP Server with OpenAI Agents SDK

Common issues when connecting Langfuse (LLM Tracing & Evals) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Langfuse (LLM Tracing & Evals) + OpenAI Agents SDK FAQ

Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.