How to integrate ManyReach MCP with Autogen

This guide walks you through connecting ManyReach to AutoGen using the Composio tool router. By the end, you'll have a working ManyReach agent that can create outreach campaigns, add your prospects, build mailing lists, review messages, and manage your tags through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a ManyReach account through Composio's ManyReach MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ManyReach is a cold email outreach platform for campaigns, prospects, senders, messages, and workspaces. It helps teams run targeted outbound email programs from one place.

16 Tools

Introduction

This guide walks you through connecting ManyReach to AutoGen using the Composio tool router. By the end, you'll have a working ManyReach agent that can create outreach campaigns, add your prospects, build mailing lists, review messages, and manage your tags through natural language commands.

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

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

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

The ManyReach MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ManyReach account. It provides structured and secure access to your outreach campaigns, prospects, mailing lists, senders, messages, tags, and workspace details, so your agent can create campaigns, organize prospects, review outreach messages, check senders, and manage tags on your behalf.

  • Campaign planning and management: Have your agent create draft campaigns, find existing campaigns by status, and update their configuration.
  • Prospect and mailing list organization: Let the agent create, find, and update prospects, then create, rename, or reorganize mailing lists for targeted outreach.
  • Message and reply review: Instruct your agent to retrieve sent messages and replies using campaign, sender, address, subject, follow-up, or CRM status filters.
  • Sender and tag management: Direct your agent to find sender mailboxes by connection or warmup state, and create, find, or update tags for prospects, campaigns, and senders.
  • Account monitoring and cleanup: Have your agent check organization details, sending credits, and data token balances, or permanently remove campaigns, prospects, mailing lists, and tags you no longer need.

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

Supported Tools

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

Create Campaign

Create and save a new draft campaign's configuration.

Create Mailing List

Create a ManyReach mailing list.

Create Prospect

Create one ManyReach prospect.

Create Tag

Create a CRM, campaign, or sender tag.

Delete Outreach Resource

Permanently and irreversibly delete exactly one campaign, prospect, mailing list, or tag by ID.

Get Account Overview

Return the authenticated ManyReach organization together with its sending-credit and data-token balances.

List Campaigns

Find campaigns in the authenticated organization, optionally filtering by lifecycle status or archived state.

List Mailing Lists

Return one page of mailing lists available for organizing and importing prospects.

List Messages

Retrieve one read-only page of reply, sent, or manually sent messages with campaign, followup, sender, CRM-status, address, and subject filters.

List Prospects

Search and filter prospects by identity, CRM status, tags, campaign membership, and existing email-validation verdict.

List Senders

Find configured sender mailboxes by folder, connection status, warmup state, or search text.

List Tags

Find CRM, campaign, or sender tags and optionally include prospect counts.

Update Campaign

Update an existing draft campaign's configuration.

Update Mailing List

Rename or move an existing ManyReach mailing list.

Update Prospect

Update contact, personalization, list, CRM, or campaign-sending eligibility fields for one ManyReach prospect by ID.

Update Tag

Update an existing tag's title and description.

FRAMEWORKS

How to build ManyReach MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use ManyReach MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use ManyReach MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use ManyReach MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use ManyReach MCP with Claude Cowork

Codex logo

Codex

Use ManyReach MCP with Codex

OpenClaw logo

OpenClaw

Use ManyReach MCP with OpenClaw

Hermes logo

Hermes

Use ManyReach MCP with Hermes

CLI logo

CLI

Use ManyReach MCP with CLI

Google ADK logo

Google ADK

Use ManyReach MCP with Google ADK

LangChain logo

LangChain

Use ManyReach MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use ManyReach MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use ManyReach MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use ManyReach MCP with LlamaIndex

CrewAI logo

CrewAI

Use ManyReach MCP with CrewAI

FAQ

Frequently asked questions

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

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

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