How to integrate Vapi MCP with Autogen

GET STARTED FOR FREE
Framework Integration Gradient
Vapi Logo
AutoGen Logo
divider

Introduction

This guide walks you through connecting Vapi to AutoGen using the Composio tool router. By the end, you'll have a working Vapi agent that can start a new outbound call campaign, get transcript from the last agent call, pause all ongoing voice agent sessions through natural language commands.

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

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

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

The Vapi MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Vapi account. It provides structured and secure access so your agent can perform Vapi operations on your behalf.

Supported Tools & Triggers

Tools
Update AssistantTool to update an existing Vapi assistant configuration.
List CallsTool to list calls from Vapi.
Delete ChatTool to delete a chat by its ID from Vapi.
Get ChatTool to fetch chat details by ID.
Create Analytics QueriesTool to create and execute analytics queries on VAPI data.
Create AssistantTool to create a new Vapi assistant with specified transcriber, voice, and AI model configurations.
Create EvalTool to create an eval for testing conversation flows.
Create OpenAI ChatTool to create an OpenAI-compatible chat using the Vapi API.
Create Phone NumberTool to create a phone number with Vapi.
Create Monitoring PolicyTool to create a monitoring policy in VAPI.
Create Provider ResourceTool to create an 11Labs pronunciation dictionary resource.
Create ScorecardTool to create a scorecard for observability and evaluation.
Delete CallTool to delete a call by its unique identifier.
Delete EvalTool to delete an eval by ID.
Delete Phone NumberTool to delete a phone number from Vapi.
Get EvalTool to retrieve an eval by its ID.
Delete Eval RunTool to delete an eval run by its ID from Vapi.
Update EvalTool to update an existing eval in Vapi.
Get AssistantTool to retrieve a specific assistant by ID from Vapi.
Get CallTool to fetch call details by ID.
Get FileTool to retrieve a file by its ID from Vapi.
Get InsightsTool to retrieve insights from Vapi.
List Monitoring PoliciesTool to retrieve monitoring policies from Vapi.
Get Observability ScorecardTool to list observability scorecards with optional filtering and pagination.
List Provider ResourcesTool to list provider resources from Vapi.
List Structured OutputsTool to list structured outputs with optional filtering.
Get InsightsTool to retrieve insights from VAPI.
List AssistantsTool to list all assistants in your VAPI organization.
List ChatsTool to retrieve a list of chat conversations from VAPI.
List EvalsTool to retrieve a paginated list of evals from Vapi.
List Provider ResourcesTool to retrieve provider resources from Vapi (e.
Update InsightTool to update an existing insight configuration in VAPI.
Create Phone NumberTool to create a phone number with VAPI.
List ScorecardsTool to retrieve a paginated list of scorecards from Vapi.
Create SessionTool to create a new session in Vapi.
List SessionsTool to retrieve a paginated list of sessions from VAPI.
List Structured OutputsTool to list structured outputs with optional filtering and pagination.
Get ToolTool to fetch tool details by ID.
Test Code Tool ExecutionTool to test TypeScript code execution in Vapi's code tool environment.
Update ToolTool to update an existing Vapi tool configuration.
Update Phone NumberTool to update an existing phone number configuration in VAPI.
Upload FileTool to upload a file to Vapi Knowledge Base.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Vapi account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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.

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 Vapi via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Vapi connections to use

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 Vapi session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["vapi"]
    )
    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 Vapi tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Vapi assistant agent with MCP tools
    agent = AssistantAgent(
        name="vapi_assistant",
        description="An AI assistant that helps with Vapi 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 Vapi tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Vapi 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 Vapi 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 Vapi 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 Vapi session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["vapi"]
    )
    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 Vapi assistant agent with MCP tools
        agent = AssistantAgent(
            name="vapi_assistant",
            description="An AI assistant that helps with Vapi 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 Vapi 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 Vapi 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 Vapi, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Vapi MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Vapi MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Vapi MCP with Claude Agent SDK

Google ADK

Google ADK

Use Vapi MCP with Google ADK

LangChain

LangChain

Use Vapi MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Vapi MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Vapi MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Vapi MCP with LlamaIndex

CrewAI

CrewAI

Use Vapi MCP with CrewAI

Claude Code

Claude Code

Use Vapi MCP with Claude Code

Codex

Codex

Use Vapi MCP with Codex

FAQ

What are the differences in Tool Router MCP and Vapi MCP?

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

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

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

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free