How to integrate Reply MCP with Autogen

This guide walks you through connecting Reply to AutoGen using the Composio tool router. By the end, you'll have a working Reply agent that can list all active email campaigns this week, get contacts in your 'leads q3' list, show all available email templates through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Reply account through Composio's Reply MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Reply.io is a sales engagement platform for automating multichannel outreach and managing email campaigns. It helps sales teams boost productivity by tracking responses and centralizing campaign performance.

21 Tools

Introduction

This guide walks you through connecting Reply to AutoGen using the Composio tool router. By the end, you'll have a working Reply agent that can list all active email campaigns this week, get contacts in your 'leads q3' list, show all available email templates through natural language commands.

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

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

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

The Reply MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Reply.io account. It provides structured and secure access to your sales engagement platform, so your agent can create and manage lists, fetch campaign schedules, organize contacts, and monitor outreach performance for you.

  • Personal list creation and management: Easily instruct your agent to create, organize, or delete personal contact lists to streamline your outreach workflow.
  • Contact organization and retrieval: Have your agent fetch contacts within specific lists, helping you target and personalize campaigns more effectively.
  • Campaign and schedule insights: Ask your agent to retrieve all campaign schedules or see which campaigns a specific contact belongs to, giving you up-to-date visibility into your outreach pipeline.
  • Email account and template discovery: Let your agent pull a list of available email accounts or fetch your personal, team, and community templates for quick access while composing new campaigns.
  • Blacklist monitoring and management: Direct your agent to retrieve the full list of blacklisted domains and emails, so you can ensure compliance and maintain deliverability standards with ease.

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

Supported Tools

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

Create Personal List

Tool to add a new personal list on the People page.

Delete Email Account

Tool to delete a specific email account.

Delete List By ID

Tool to delete a list by its ID.

Delete Sequence

Tool to delete a sequence.

Delete User

Tool to delete a user.

Generate ULID

Generate ULID

Get All Lists

Tool to retrieve all available people lists.

Get full blacklist of domains and emails

Tool to retrieve the full list of blacklisted domains and emails.

Get all campaign schedules

Retrieves all campaign schedules from Reply.

Get Campaigns For Contact

Tool to retrieve campaigns a contact belongs to by contact ID.

Get Contacts in List by ID

Tool to retrieve contacts in a specific personal list.

Get List by ID

Tool to return a specific people list by its ID.

Get templates list

Retrieves all email templates from Reply.

List Campaigns

Tool to list all campaigns (sequences).

List Contacts Basic

Tool to list contacts.

Reply.io List Email Accounts

Tool to list all email accounts.

Mark Contact As Finished

Marks a contact (by email) or all contacts under a domain as finished in all Reply.

Mark Contact as Replied

Tool to mark a contact as replied in all campaigns by email or domain.

Move Contacts to Lists

Tool to move one or more contacts to specified lists.

Remove Domain from Blacklist

Tool to remove the specified domain from the blacklist.

Search Contacts by Email

Tool to search contacts by email.

FRAMEWORKS

How to build Reply MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Reply MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Reply MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Reply MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Reply MCP with Claude Cowork

Codex logo

Codex

Use Reply MCP with Codex

OpenClaw logo

OpenClaw

Use Reply MCP with OpenClaw

Hermes logo

Hermes

Use Reply MCP with Hermes

CLI logo

CLI

Use Reply MCP with CLI

Google ADK logo

Google ADK

Use Reply MCP with Google ADK

LangChain logo

LangChain

Use Reply MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Reply MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Reply MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Reply MCP with LlamaIndex

CrewAI logo

CrewAI

Use Reply MCP with CrewAI

FAQ

Frequently asked questions

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

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

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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