How to integrate Avoma MCP with CrewAI

This guide walks you through connecting Avoma to CrewAI 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 CrewAI 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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Avoma connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Avoma
  • Build a conversational loop where your agent can execute Avoma operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Avoma connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
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 crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Avoma via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Avoma MCP URL
6

Create a Composio Tool Router session for Avoma

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["avoma"])

url = session.mcp.url
What's happening:
  • You create a Avoma only session through Composio
  • Composio returns an MCP HTTP URL that exposes Avoma tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["avoma"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Avoma through Composio's Tool Router. The agent can perform Avoma operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
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

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. CrewAI 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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