# How to integrate Mezmo MCP with Autogen

```json
{
  "title": "How to integrate Mezmo MCP with Autogen",
  "toolkit": "Mezmo",
  "toolkit_slug": "mezmo",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/mezmo/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/mezmo/framework/autogen.md",
  "updated_at": "2026-05-12T10:18:55.352Z"
}
```

## Introduction

This guide walks you through connecting Mezmo to AutoGen 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 By the end, you'll have a working Mezmo agent that can send application error logs to mezmo, delete outdated pipeline alert for a component, ingest security event logs from last hour through natural language commands.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 AutoGen 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 SDK](https://composio.dev/toolkits/mezmo/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/mezmo/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/mezmo/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/mezmo/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/mezmo/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/mezmo/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/mezmo/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/mezmo/framework/cli)
- [Google ADK](https://composio.dev/toolkits/mezmo/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/mezmo/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/mezmo/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/mezmo/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/mezmo/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/mezmo/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the required dependencies for Autogen and Composio
- Initialize Composio and create a Tool Router session for Mezmo
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Mezmo tools
- Run a live chat loop where you ask the agent to perform Mezmo operations

## What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.
Key features include:
- Multi-Agent Systems: Build collaborative agent workflows
- MCP Workbench: Native support for Model Context Protocol tools
- Streaming HTTP: Connect to external services through streamable HTTP
- AssistantAgent: Pre-built agent class for tool-using assistants

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MEZMO_CREATE_CATEGORY` | Create Category | Tool to create a new category for views, boards, or screens in Mezmo. Use when organizing Mezmo resources into categories. |
| `MEZMO_CREATE_INGESTION_EXCLUSION` | Create Ingestion Exclusion Rule | Tool to create an exclusion rule for log ingestion to control costs. Use this when you need to prevent specific logs from being ingested or stored based on query patterns. Exclusion rules help reduce ingestion costs by filtering out debug logs, test environment logs, or other non-essential log data. |
| `MEZMO_CREATE_KEY` | Create API Key | Tool to create a new API key (ingestion or service key) in Mezmo. The API auto-generates a unique name for the key. Use when you need to provision a new key for log ingestion or API access. |
| `MEZMO_CREATE_MEMBER` | Create Member Invitation | Tool to invite a new member to the Mezmo organization with a specified role. Use this to send invitations to team members and optionally assign them to specific groups. |
| `MEZMO_CREATE_PRESET_ALERT` | Create Preset Alert | Tool to create a new preset alert in Mezmo with specified name and notification channels. Use this to configure alerts that can be triggered based on log conditions. Supports email, PagerDuty, and webhook notification channels. |
| `MEZMO_CREATE_VIEW` | Create View | Tool to create a new Mezmo view with filtering and alert configuration. Use when you need to set up custom log views with specific filters (query, hosts, apps, levels, tags) and optional alert channels (email, PagerDuty, webhook). At least one filter parameter must be provided in addition to the view name. |
| `MEZMO_DELETE_CATEGORY` | Delete Category | Tool to delete a category by its type and ID. Use when you need to remove a view, board, or screen category from Mezmo configuration. |
| `MEZMO_DELETE_INGESTION_EXCLUSION` | Delete Ingestion Exclusion | Tool to remove an ingestion exclusion rule by its ID. Use when you need to delete a specific exclusion rule from Mezmo's ingestion configuration. |
| `MEZMO_DELETE_KEY` | Delete API Key | Tool to delete an API key by its unique identifier. Use when you need to remove an ingestion key from Mezmo to revoke access. |
| `MEZMO_DELETE_MEMBER` | Delete Organization Member | Tool to remove a member from the organization by their email address. Use when you need to revoke a user's access to the organization. |
| `MEZMO_DELETE_PIPELINE_ALERT` | Delete Pipeline Alert | Tool to delete an alert for a specific component within a pipeline. Use after confirming pipeline ID, component kind, component ID, and alert ID. |
| `MEZMO_DELETE_PRESET_ALERT` | Delete Preset Alert | Tool to delete a preset alert by its ID. Use after confirming the preset alert ID exists. |
| `MEZMO_DELETE_VIEW` | Delete View | Tool to delete a view by its ID. Use when you need to remove a specific view from Mezmo. |
| `MEZMO_GET_ALERT` | Get Preset Alert | Tool to retrieve details of a specific preset alert by its ID. Use when you need to view the configuration of an existing alert. |
| `MEZMO_GET_CATEGORY` | Get Category | Tool to retrieve a category configuration by its type and ID. Use when you need to fetch details about a specific Mezmo category (view, board, or screen). |
| `MEZMO_GET_INDEX_RATE_ALERT` | Get Index Rate Alert Configuration | Tool to retrieve current index rate alert settings for the Mezmo account. Use this to check if index rate alerting is enabled and view configured thresholds and notification channels. |
| `MEZMO_GET_INGESTION_EXCLUSION` | Get Ingestion Exclusion Rule | Tool to retrieve an ingestion exclusion rule by its ID. Use when you need to fetch details of a specific exclusion rule. |
| `MEZMO_GET_INGESTION_STATUS` | Get Ingestion Status | Tool to get the current ingestion status for the Mezmo account. Use when you need to check whether log ingestion is currently active or paused. |
| `MEZMO_GET_KEY` | Get API Key | Tool to retrieve an API key configuration by its ID. Use when you need to fetch details about a specific Mezmo API key. |
| `MEZMO_GET_MEMBER` | Get Member | Tool to retrieve member information by their ID. Use when you need to fetch details about a specific member in your Mezmo account. |
| `MEZMO_GET_STREAM_CONFIG` | Get Stream Configuration | Tool to retrieve the current event streaming configuration for the Mezmo account. Use when you need to check if streaming is enabled and get streaming settings. Returns error details if streaming is unavailable on the account/plan. |
| `MEZMO_GET_VIEW` | Get View Details | Tool to retrieve details of a specific view by its ID. Use when you need to fetch view configuration including name, query, filters, and other attributes. |
| `MEZMO_INGEST_LOGS` | Ingest Logs to Mezmo | Ingest log lines into Mezmo Log Analysis. Use this tool to send structured log data from hosts, applications, or services to Mezmo for centralized logging, analysis, and alerting. Logs are sent to the Mezmo ingestion endpoint and will appear in the Mezmo dashboard. |
| `MEZMO_LIST_ALERTS` | List Preset Alerts | Tool to list all preset alerts configured for the Mezmo account. Use when you need to retrieve notification rules that trigger based on log patterns. Returns preset alert configurations including their channels (email, PagerDuty, webhook). |
| `MEZMO_LIST_KEYS` | List API Keys | Tool to list all API keys and ingestion keys configured for the account. Use when you need to retrieve all keys for viewing or management purposes. |
| `MEZMO_LIST_MEMBERS` | List Members | Tool to list all team members in the Mezmo account configuration. Use when you need to retrieve information about all members in the organization. |
| `MEZMO_LIST_PIPELINES` | List Telemetry Pipelines | Tool to list all telemetry pipelines configured for the account. Use when you need to view or retrieve information about existing pipelines that manage the flow and transformation of telemetry data. |
| `MEZMO_LIST_VIEWS` | List Views | Tool to list all views configured for the account. Views are saved search queries and filters for quick access to specific log data. |
| `MEZMO_RESUME_INGESTION` | Resume Log Ingestion | Tool to resume log ingestion for the account after it has been stopped. Use when you need to re-enable log collection after a pause. |
| `MEZMO_UPDATE_CATEGORY` | Update Category | Tool to update a category name by its type and ID. Use when you need to rename an existing category in Mezmo. |
| `MEZMO_UPDATE_INDEX_RATE_ALERT` | Update Index Rate Alert Configuration | Tool to configure index rate alerting settings including thresholds and notification channels. Use this when you need to set up or modify alerts for unusual log ingestion rates based on absolute line counts or statistical deviations. |
| `MEZMO_UPDATE_INGESTION_EXCLUSION` | Update Ingestion Exclusion Rule | Tool to update an existing exclusion rule by its ID. Use when you need to modify the query, active status, indexonly behavior, or title of an existing exclusion rule. At least one field (query, active, indexonly, or title) must be provided for update. |
| `MEZMO_UPDATE_KEY` | Update API Key | Tool to update an API key name by its ID. Use when you need to rename an existing Mezmo API key. |
| `MEZMO_UPDATE_MEMBER` | Update Member Role and Groups | Tool to update a member's role and group assignments by their email address. Use when you need to change a member's permissions or group memberships. |
| `MEZMO_UPDATE_PRESET_ALERT` | Update Preset Alert | Tool to update an existing preset alert by ID. Allows modifying the alert's name and notification channels. Use when you need to change alert configuration after creation. Requires full resource representation with both name and channels. |
| `MEZMO_UPDATE_VIEW` | Update Mezmo View | Tool to update an existing Mezmo view by its ID. Use when you need to modify a view's name or search query. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Mezmo MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Mezmo. Instead of manually wiring Mezmo APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Mezmo account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Mezmo via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

Create a .env file in your project folder.
What's happening:
- COMPOSIO_API_KEY is required to talk to Composio
- OPENAI_API_KEY is used by Autogen's OpenAI client
- USER_ID is how Composio identifies which user's Mezmo connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Mezmo tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Mezmo session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mezmo"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.
What's happening:
- url points to the Tool Router MCP endpoint from Composio
- timeout is the HTTP timeout for requests
- sse_read_timeout controls how long to wait when streaming responses
- terminate_on_close=True cleans up the MCP server process when the workbench is closed
```python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Mezmo tools from the workbench
```python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Mezmo assistant agent with MCP tools
    agent = AssistantAgent(
        name="mezmo_assistant",
        description="An AI assistant that helps with Mezmo operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Mezmo tools to call via MCP
- agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
- Typing exit, quit, or bye ends the loop
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Mezmo related question or task to the agent.\n")

# Conversation loop
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")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Mezmo session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["mezmo"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Mezmo assistant agent with MCP tools
        agent = AssistantAgent(
            name="mezmo_assistant",
            description="An AI assistant that helps with Mezmo operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

        # Conversation loop
        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")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

## Conclusion

You now have an Autogen assistant wired into Mezmo through Composio's Tool Router and MCP. From here you can:
- Add more toolkits to the toolkits list, for example notion or hubspot
- Refine the agent description to point it at specific workflows
- Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Mezmo, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Mezmo MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/mezmo/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/mezmo/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/mezmo/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/mezmo/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/mezmo/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/mezmo/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/mezmo/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/mezmo/framework/cli)
- [Google ADK](https://composio.dev/toolkits/mezmo/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/mezmo/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/mezmo/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/mezmo/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/mezmo/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/mezmo/framework/crew-ai)

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- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Mezmo MCP?

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.

### Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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.

### Can I manage the permissions and scopes for Mezmo while using Tool Router?

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.

### How safe is my data with Composio Tool Router?

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.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)
