How to integrate Mezmo MCP with LangChain

This guide walks you through connecting Mezmo to LangChain using the Composio tool router. By the end, you'll have a working Mezmo agent that can send application error logs to mezmo, delete outdated pipeline alert for a component, ingest security event logs from last hour through natural language commands. This guide will help you understand how to give your LangChain 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.

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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.

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Introduction

This guide walks you through connecting Mezmo to LangChain using the Composio tool router. By the end, you'll have a working Mezmo agent that can send application error logs to mezmo, delete outdated pipeline alert for a component, ingest security event logs from last hour through natural language commands.

This guide will help you understand how to give your LangChain 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

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 Mezmo project to Composio
  • Create a Tool Router MCP session for Mezmo
  • Initialize an MCP client and retrieve Mezmo tools
  • Build a LangChain agent that can interact with Mezmo
  • 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 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 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 Mezmo 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 Mezmo 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: ['mezmo']
    }
);

const url = session.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
  • This approach allows the agent to dynamically load and use Mezmo tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "mezmo-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 Mezmo MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Mezmo 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 Mezmo 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 Mezmo 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: ['mezmo']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "mezmo-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 Mezmo 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 Mezmo 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 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

Tool to create a new API key (ingestion or service key) in Mezmo.

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

Ingest log lines into Mezmo Log Analysis.

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

Tool to list all telemetry pipelines configured for the account.

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

Tool to configure index rate alerting settings including thresholds and notification channels.

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

Tool to update a member's role and group assignments by their email address.

Update Preset Alert

Tool to update an existing preset alert by ID.

Update Mezmo View

Tool to update an existing Mezmo view by its ID.

FRAMEWORKS

How to build Mezmo MCP Agent with another framework

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

Claude Code logo

Claude Code

Use Mezmo MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Mezmo MCP with Claude Cowork

Codex logo

Codex

Use Mezmo MCP with Codex

OpenClaw logo

OpenClaw

Use Mezmo MCP with OpenClaw

Hermes logo

Hermes

Use Mezmo MCP with Hermes

CLI logo

CLI

Use Mezmo MCP with CLI

Google ADK logo

Google ADK

Use Mezmo MCP with Google ADK

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

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. 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 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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agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies