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 LangChain 10 tools — connect in under 2 minutes

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

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Langfuse (LLM Tracing & Evals) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "langfuse-llm-tracing-evals": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Langfuse (LLM Tracing & Evals), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

LangChain's ecosystem of 500+ components combines seamlessly with Langfuse (LLM Tracing & Evals) through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Langfuse (LLM Tracing & Evals) via MCP

Why Use LangChain with the Langfuse (LLM Tracing & Evals) MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Langfuse (LLM Tracing & Evals) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Langfuse (LLM Tracing & Evals) queries for multi-turn workflows

Langfuse (LLM Tracing & Evals) + LangChain Use Cases

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

RAG with live data: combine Langfuse (LLM Tracing & Evals) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Langfuse (LLM Tracing & Evals), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Langfuse (LLM Tracing & Evals) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Langfuse (LLM Tracing & Evals) tool call, measure latency, and optimize your agent's performance

Langfuse (LLM Tracing & Evals) MCP Tools for LangChain (10)

These 10 tools become available when you connect Langfuse (LLM Tracing & Evals) to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Langfuse (LLM Tracing & Evals) + LangChain FAQ

Common questions about integrating Langfuse (LLM Tracing & Evals) MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.