How to integrate Callingly MCP with Autogen

This guide walks you through connecting Callingly to AutoGen using the Composio tool router. By the end, you'll have a working Callingly agent that can activate a client account for onboarding, create an outbound call to new lead, get agent schedule for next week through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Callingly account through Composio's Callingly MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Callingly is a lead response management platform that automates immediate call and text follow-ups with new leads. It helps sales teams boost response speed and close more deals by connecting seamlessly with CRMs and lead sources.

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Introduction

This guide walks you through connecting Callingly to AutoGen using the Composio tool router. By the end, you'll have a working Callingly agent that can activate a client account for onboarding, create an outbound call to new lead, get agent schedule for next week through natural language commands.

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

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

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

The Callingly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Callingly account. It provides structured and secure access to your Callingly environment, so your agent can automate lead follow-ups, manage agents and clients, trigger outbound calls, and keep your sales workflows moving without manual intervention.

  • Automated outbound call creation: Instantly generate outbound call records so your team can respond to new leads within seconds without lifting a finger.
  • Agent and team management: Let your agent create, delete, or update agents and teams as your sales organization changes and grows.
  • Client onboarding and offboarding: Seamlessly add, activate, deactivate, or remove client accounts as your business requires—no more manual data entry.
  • Real-time webhook setup: Set up and delete webhooks to receive instant notifications for specific call or lead events, keeping your CRM and other tools in sync.
  • Availability and scheduling insights: Retrieve agent schedules to optimize call assignments and guarantee leads get connected when agents are actually available.

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

Supported Tools

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

Activate/Deactivate Client Account

Tool to activate or deactivate a client account.

Create Agent

Tool to create a new agent.

Create Outbound Call

Creates a new outbound call record and initiates a real outbound call, which incurs cost — ensure explicit user authorization and compliance with applicable consent and telemarketing regulations before use.

Create Client

Creates a new client account in Callingly.

Create Team

Tool to create a new team.

Create Webhook

Tool to create a new webhook for call or lead events.

Delete Agent

Permanently delete an agent from a Callingly account.

Delete Client

Tool to delete a client.

Delete Lead

Tool to delete a lead by ID.

Delete Webhook

Tool to delete a webhook.

Get Agent Schedule

Tool to retrieve the availability schedule for a specific agent.

Get Call

Retrieves detailed information about a specific call by its unique ID.

Get Lead

Tool to retrieve details of a specific lead by its ID.

Get Team

Tool to retrieve details of a specific team.

Get Webhook

Tool to retrieve details of a specific webhook by its ID.

List Calls

Tool to list calls.

List Clients

Tool to list clients.

List Leads

Tool to list leads based on provided filters like date range or phone number.

List Teams

Tool to list teams.

List Team Users

Retrieve all agents assigned to a specific team in Callingly.

List Users

Tool to retrieve a list of agents.

List Webhooks

Tool to list configured webhooks.

Remove Team Agent

Tool to remove a specific agent from a team.

Update Agent

Tool to update an existing agent's details.

Update Lead

Tool to update an existing lead's information.

Update Agent Schedule

Tool to update an agent's availability schedule.

Update Team Agent Settings

Tool to update settings (priority, capacity) for a specific team agent.

Update Team Users

Updates the list of agents assigned to a team.

Update Webhook

Updates an existing webhook's configuration by ID.

FRAMEWORKS

How to build Callingly MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Callingly MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Callingly MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Callingly MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Callingly MCP with Claude Cowork

Codex logo

Codex

Use Callingly MCP with Codex

OpenClaw logo

OpenClaw

Use Callingly MCP with OpenClaw

Hermes logo

Hermes

Use Callingly MCP with Hermes

CLI logo

CLI

Use Callingly MCP with CLI

Google ADK logo

Google ADK

Use Callingly MCP with Google ADK

LangChain logo

LangChain

Use Callingly MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Callingly MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Callingly MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Callingly MCP with LlamaIndex

CrewAI logo

CrewAI

Use Callingly MCP with CrewAI

FAQ

Frequently asked questions

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

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

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