How to integrate ManyReach MCP with CrewAI

This guide walks you through connecting ManyReach to CrewAI 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 CrewAI 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.

Get started for freeGet a demo
ManyReach logoManyReach
Api Key

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 CrewAI 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 CrewAI 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 AILlamaIndex

TL;DR

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A ManyReach 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 ManyReach 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 ManyReach MCP URL
6

Create a Composio Tool Router session for ManyReach

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

url = session.mcp.url
What's happening:
  • You create a ManyReach only session through Composio
  • Composio returns an MCP HTTP URL that exposes ManyReach 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 ManyReach 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=["manyreach"],
)
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 ManyReach through Composio's Tool Router. The agent can perform ManyReach 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 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

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. 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 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.

Start with ManyReach.It takes 30 seconds.

Managed auth, hosted MCP servers, and every ManyReach tool your agent needs.Free to start.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

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.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies