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How to integrate Mezmo MCP with Pydantic AI

Connect Pydantic AI to Mezmo MCP. Send application error logs to mezmo, delete outdated pipeline alert for a component, and more using natural language, with authentication handled for you.

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Mezmo is a unified platform for log management and telemetry data processing. It helps you collect, analyze, and manage log data for better operational visibility.

36 Tools

Introduction

This guide walks you through connecting Mezmo to Pydantic AI using the Composio tool router. By the end, you'll have a working Mezmo agent that can ingest logs, create views and preset alerts, manage ingestion exclusion rules, list telemetry pipelines, and manage API keys through natural language commands.

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

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

Also integrate Mezmo with

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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 Mezmo
  • 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 Mezmo 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 Mezmo MCP server, and what's possible with it?

The Mezmo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to your Mezmo account. It provides structured and secure access to your log management and telemetry pipelines, so your agent can ingest logs, manage pipeline alerts, streamline monitoring, and automate log-driven workflows on your behalf.

  • Automated log ingestion: Seamlessly send structured log events from any host or service to Mezmo for real-time analysis and monitoring.
  • Pipeline alert deletion: Direct your agent to remove specific alerts tied to components in your pipelines, helping manage noise and maintain alert hygiene.
  • Streamlined alert management: Enable your agent to clean up outdated or redundant alerts, keeping your pipeline monitoring focused and actionable.
  • Real-time telemetry processing: Let your agent push telemetry data instantly for advanced analytics, troubleshooting, and observability workflows.

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 Mezmo
  • 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 Mezmo
  • MCPServerStreamableHTTP connects to the Mezmo 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 Mezmo
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mezmo"],
    )
    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 Mezmo 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
mezmo_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[mezmo_mcp],
    instructions=(
        "You are a Mezmo assistant. Use Mezmo tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Mezmo endpoint
  • The agent uses GPT-5 to interpret user commands and perform Mezmo 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 Mezmo.\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
  • Mezmo 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 Mezmo 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 Mezmo
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["mezmo"],
    )
    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
    mezmo_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[mezmo_mcp],
        instructions=(
            "You are a Mezmo assistant. Use Mezmo 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 Mezmo.\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 Mezmo through Composio's Tool Router. With this setup, your agent can perform real Mezmo 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 + Mezmo 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 Mezmo action and event your agent gets out of the box.

Create Category

Tool to create a new category for views, boards, or screens in Mezmo.

Create Ingestion Exclusion Rule

Tool to create an exclusion rule for log ingestion to control costs.

Create API Key

Create a new Mezmo ingestion API key for log ingestion.

Create Member Invitation

Tool to invite a new member to the Mezmo organization with a specified role.

Create Preset Alert

Tool to create a new preset alert in Mezmo with specified name and notification channels.

Create View

Tool to create a new Mezmo view with filtering and alert configuration.

Delete Category

Tool to delete a category by its type and ID.

Delete Ingestion Exclusion

Tool to remove an ingestion exclusion rule by its ID.

Delete API Key

Tool to delete an API key by its unique identifier.

Delete Organization Member

Tool to remove a member from the organization by their email address.

Delete Pipeline Alert

Tool to delete an alert for a specific component within a pipeline.

Delete Preset Alert

Tool to delete a preset alert by its ID.

Delete View

Tool to delete a view by its ID.

Get Preset Alert

Tool to retrieve details of a specific preset alert by its ID.

Get Category

Tool to retrieve a category configuration by its type and ID.

Get Index Rate Alert Configuration

Tool to retrieve current index rate alert settings for the Mezmo account.

Get Ingestion Exclusion Rule

Tool to retrieve an ingestion exclusion rule by its ID.

Get Ingestion Status

Tool to get the current ingestion status for the Mezmo account.

Get API Key

Tool to retrieve an API key configuration by its ID.

Get Member

Tool to retrieve member information by their ID.

Get Stream Configuration

Tool to retrieve the current event streaming configuration for the Mezmo account.

Get View Details

Tool to retrieve details of a specific view by its ID.

Ingest Logs to Mezmo

Send structured log lines from a host, application, or service to Mezmo Log Analysis for centralized logging, analysis, and alerting.

List Preset Alerts

Tool to list all preset alerts configured for the Mezmo account.

List API Keys

Tool to list all API keys and ingestion keys configured for the account.

List Members

Tool to list all team members in the Mezmo account configuration.

List Telemetry Pipelines

List telemetry pipelines configured for the account to view information about existing pipelines.

List Views

Tool to list all views configured for the account.

Resume Log Ingestion

Tool to resume log ingestion for the account after it has been stopped.

Update Category

Tool to update a category name by its type and ID.

Update Index Rate Alert Configuration

Configure index rate alerting, including thresholds and email notification channels, for unusual log ingestion rates.

Update Ingestion Exclusion Rule

Tool to update an existing exclusion rule by its ID.

Update API Key

Tool to update an API key name by its ID.

Update Member Role and Groups

Update a member's role and group assignments by email address.

Update Preset Alert

Tool to update an existing preset alert by ID.

Update Mezmo View

Update an existing Mezmo view by ID, supplying its name and optionally its search query.

FRAMEWORKS

How to build Mezmo MCP Agent with another framework

Claude Code logo

Claude Code

Use Mezmo MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Mezmo MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Mezmo MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Mezmo MCP with Claude Agent SDK

Codex logo

Codex

Use Mezmo MCP with Codex

OpenClaw logo

OpenClaw

Use Mezmo MCP with OpenClaw

Hermes logo

Hermes

Use Mezmo MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Mezmo MCP with Atomic Agent

Google ADK logo

Google ADK

Use Mezmo MCP with Google ADK

LangChain logo

LangChain

Use Mezmo MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Mezmo MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Mezmo MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Mezmo MCP with LlamaIndex

CrewAI logo

CrewAI

Use Mezmo MCP with CrewAI

AutoGen logo

AutoGen

Use Mezmo MCP with AutoGen

FAQ

Frequently asked questions

With a standalone Mezmo MCP server, the agents and LLMs can only access a fixed set of Mezmo tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Mezmo 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 Mezmo tools.

Yes, absolutely. You can configure which Mezmo 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 Mezmo data and credentials are handled as safely as possible.

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