How to integrate Avoma MCP with Autogen

This guide walks you through connecting Avoma to AutoGen using the Composio tool router. By the end, you'll have a working Avoma agent that can summarize yesterday's customer call notes, find meetings needing follow-up emails, review sales scorecards from this week through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Avoma account through Composio's Avoma MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Avoma is an AI meeting assistant for recording, transcribing, analyzing, and managing meetings, calls, notes, scorecards, and conversation intelligence. It helps teams turn conversations into searchable notes, follow-ups, coaching insights, and revenue intelligence.

16 Tools

Introduction

This guide walks you through connecting Avoma to AutoGen using the Composio tool router. By the end, you'll have a working Avoma agent that can summarize yesterday's customer call notes, find meetings needing follow-up emails, review sales scorecards from this week through natural language commands.

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

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

Also integrate Avoma with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Avoma
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Avoma tools
  • Run a live chat loop where you ask the agent to perform Avoma 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 Avoma MCP server, and what's possible with it?

The Avoma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Avoma account. It provides structured and secure access to your meetings, calls, transcripts, notes, recordings, and conversation intelligence, so your agent can find meetings, retrieve transcripts, review AI insights, analyze engagement, and inspect scorecard evaluations on your behalf.

  • Meeting and call discovery: Have your agent find meetings and externally recorded calls by date, attendee, direction, CRM details, call type, privacy context, or duration.
  • Transcripts, notes, and snippets: Let the agent retrieve full meeting transcripts, review notes in JSON, HTML, or Markdown, and find user-created or AI-generated snippets.
  • Conversation analysis: Direct your agent to review AI insights, topics, speaker segments, sentiment records, and recording access for processed meetings.
  • Engagement and coaching insights: Instruct your agent to summarize team engagement metrics and inspect scorecard evaluations by date, meeting, scorecard, or evaluated user.
  • Call ingestion and workspace review: Have your agent submit externally hosted call recordings for processing, check their status, list visible users and configuration catalogs, or audit active webhook subscriptions.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Avoma account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Avoma via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

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 Avoma connections to use
5

Import dependencies and create Tool Router session

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 Avoma session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["avoma"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Avoma tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

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")}
)

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
7

Create the model client and agent

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 Avoma assistant agent with MCP tools
    agent = AssistantAgent(
        name="avoma_assistant",
        description="An AI assistant that helps with Avoma operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Avoma tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Avoma 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")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Avoma 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

Complete Code

Here's the complete code to get you started with Avoma and AutoGen:

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 Avoma session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["avoma"]
    )
    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 Avoma assistant agent with MCP tools
        agent = AssistantAgent(
            name="avoma_assistant",
            description="An AI assistant that helps with Avoma 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 Avoma 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 Avoma 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 Avoma, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Create Call

Submit an externally hosted call recording to Avoma for asynchronous processing.

Get Call

Retrieve one externally ingested Avoma call by the dialer system's external ID.

Get Engagement Summary

Aggregate Avoma engagement metrics across visible users for a required UTC date range and optional meeting and user filters.

Get Meeting

Retrieve one meeting by UUID, including processing/readiness state and optionally CRM associations.

Get Meeting Analysis

Return Avoma's AI insights, topical and speaker segments, and sentiment records for one processed meeting in a single read-only invocation.

Get Recording

Get time-limited audio and video URLs for a meeting recording.

List Calls

List externally ingested calls in a required UTC date range, optionally filtered by inbound or outbound direction.

List Configuration Resources

List one Avoma configuration catalog: custom categories, smart categories, scorecards, templates, meeting types, or meeting outcomes.

List Engagement Metrics

List per-user Avoma engagement metrics over a required UTC date range with meeting and user filters.

List Meetings

Find meetings visible to the connected Avoma user within a required UTC date range, with optional attendee, CRM, call-type, privacy-context, duration, and ordering filters.

List Notes

Retrieve Avoma notes for meetings in a required UTC date range, optionally narrowed to a meeting or custom category and rendered as JSON, HTML, or Markdown.

List Scorecard Evaluations

List Avoma scorecard evaluations, optionally filtered by UTC date range, scorecard templates, meeting, or evaluated users.

List Snippets

Find user-created or AI-generated meeting snippets by meeting UUID or by a UTC date range of at most 180 days.

List Transcriptions

Find full meeting transcriptions by meeting UUID or by a UTC date range, with attendee and CRM filters.

List Users

Return users visible in the Avoma organization, including their UUID, email, role, teams, and active status.

List Webhooks

Audit the connected organization's active Avoma webhook subscriptions without reading signing secrets.

FRAMEWORKS

How to build Avoma MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Avoma MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Avoma MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Avoma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Avoma MCP with Claude Cowork

Codex logo

Codex

Use Avoma MCP with Codex

OpenClaw logo

OpenClaw

Use Avoma MCP with OpenClaw

Hermes logo

Hermes

Use Avoma MCP with Hermes

CLI logo

CLI

Use Avoma MCP with CLI

Google ADK logo

Google ADK

Use Avoma MCP with Google ADK

LangChain logo

LangChain

Use Avoma MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Avoma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Avoma MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Avoma MCP with LlamaIndex

CrewAI logo

CrewAI

Use Avoma MCP with CrewAI

FAQ

Frequently asked questions

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

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 Avoma tools.

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

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