How to integrate Langfuse MCP with Pydantic AI

This guide walks you through connecting Langfuse to Pydantic AI 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 Pydantic AI 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 Pydantic AI 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 Pydantic AI 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 ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Langfuse
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Langfuse workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Langfuse
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Langfuse
  • MCPServerStreamableHTTP connects to the Langfuse MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Langfuse
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["langfuse"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
langfuse_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[langfuse_mcp],
    instructions=(
        "You are a Langfuse assistant. Use Langfuse tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Langfuse endpoint
  • The agent uses GPT-5 to interpret user commands and perform Langfuse operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Langfuse.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Langfuse API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Langfuse
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["langfuse"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    langfuse_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[langfuse_mcp],
        instructions=(
            "You are a Langfuse assistant. Use Langfuse tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Langfuse.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Langfuse through Composio's Tool Router. With this setup, your agent can perform real Langfuse actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Langfuse for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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

LangChain logo

LangChain

Use Langfuse MCP with LangChain

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. Pydantic AI 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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