How to integrate Langfuse MCP with LangChain

This guide walks you through connecting Langfuse to LangChain using the Composio tool router. By the end, you'll have a working Langfuse agent that can review your traces, compare prompt versions, list your datasets, and query your metrics through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Langfuse account through Composio's Langfuse MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Langfuse is an open source LLM engineering platform for traces, evals, prompt management, and metrics. It helps teams debug, monitor, and improve LLM applications with clear observability data.

10 Tools

Introduction

This guide walks you through connecting Langfuse to LangChain using the Composio tool router. By the end, you'll have a working Langfuse agent that can review your traces, compare prompt versions, list your datasets, and query your metrics through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Langfuse account through Composio's Langfuse MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Langfuse with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Langfuse project to Composio
  • Create a Tool Router MCP session for Langfuse
  • Initialize an MCP client and retrieve Langfuse tools
  • Build a LangChain agent that can interact with Langfuse
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Langfuse MCP server, and what's possible with it?

The Langfuse MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Langfuse account. It provides structured and secure access to your prompts, traces, evaluations, datasets, experiments, and metrics, so your agent can retrieve prompts, inspect observations, review scores, explore datasets, and analyze performance on your behalf.

  • Prompt retrieval: Have your agent fetch text or chat prompts by name, deployment label, or version, with related prompt dependencies when needed.
  • Trace and observation analysis: Instruct your agent to search generations, spans, events, agent activity, and tool calls to investigate application behavior.
  • Evaluation score management: Let the agent review existing evaluation results or attach new scores to traces, sessions, dataset runs, and specific observations.
  • Dataset and experiment review: Direct your agent to explore available datasets and their items, then review experiments from a chosen time range with metadata and scores.
  • Metrics and evaluation resources: Have your agent query aggregate performance metrics, inspect model pricing definitions, and list annotation queues used for human evaluation.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Langfuse functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Langfuse tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['langfuse']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Langfuse tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Langfuse tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "langfuse-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Langfuse MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Langfuse tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Langfuse related question or task to the agent.\n");

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Langfuse and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['langfuse']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "langfuse-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Langfuse related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Langfuse through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

Every Langfuse action and event your agent gets out of the box.

Create Score

Attach an evaluation score to exactly one Langfuse trace, session, or dataset run, optionally narrowing a trace score to an observation.

Get Prompt

Retrieve a Langfuse text or chat prompt by name, deployment label, or exact version, with optional dependency resolution.

List Annotation Queues

List annotation queues configured for human evaluation in the connected Langfuse project.

List Dataset Items

List dataset items, optionally scoped to a dataset, source trace or observation, or historical dataset version.

List Datasets

List datasets available to the connected Langfuse project.

List Experiments

List Langfuse experiments active in a required time range, optionally including metadata and directly attached scores.

List Models

List Langfuse-managed and project-custom model pricing definitions used for usage and cost calculation.

List Observations

Search Langfuse observations such as generations, spans, events, agents, and tool calls, selecting only the field groups needed.

List Scores

Search numeric, boolean, categorical, text, and correction scores with subject and annotation context.

Query Metrics

Run an aggregate Langfuse metrics query over observations or numeric, boolean, or categorical scores.

FRAMEWORKS

How to build Langfuse MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Langfuse MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Langfuse MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Langfuse MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Langfuse MCP with Claude Cowork

Codex logo

Codex

Use Langfuse MCP with Codex

OpenClaw logo

OpenClaw

Use Langfuse MCP with OpenClaw

Hermes logo

Hermes

Use Langfuse MCP with Hermes

CLI logo

CLI

Use Langfuse MCP with CLI

Google ADK logo

Google ADK

Use Langfuse MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Langfuse MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Langfuse MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Langfuse MCP with LlamaIndex

CrewAI logo

CrewAI

Use Langfuse MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Langfuse MCP server, the agents and LLMs can only access a fixed set of Langfuse tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Langfuse and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Langfuse tools.

Yes, absolutely. You can configure which Langfuse scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Langfuse data and credentials are handled as safely as possible.

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