How to integrate Lemlist MCP with CrewAI

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Introduction

This guide walks you through connecting Lemlist to CrewAI using the Composio tool router. By the end, you'll have a working Lemlist agent that can export all leads from current campaign, download list of unsubscribed emails, unsubscribe specific lead from a campaign, check status of campaign export request through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Lemlist account through Composio's Lemlist 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 a Composio API key and configure your Lemlist connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Lemlist
  • Build a conversational loop where your agent can execute Lemlist 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 Lemlist MCP server, and what's possible with it?

The Lemlist MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lemlist account. It provides structured and secure access to your outreach campaigns, so your agent can manage leads, automate campaign exports, monitor unsubscribe lists, and orchestrate multichannel engagement on your behalf.

  • Automated campaign management: Retrieve campaign details by ID, audit campaign sequences, and start or monitor campaign exports for streamlined reporting and analytics.
  • Lead and subscriber control: Unsubscribe leads from campaigns, delete unsubscribed emails, or export detailed lists of campaign leads to keep your outreach data fresh and compliant.
  • Outreach data exports: Initiate and track asynchronous exports of campaign statistics or download CSVs of unsubscribed contacts for deeper insights and record-keeping.
  • Webhook administration: Fetch all configured webhooks to sync Lemlist with your other tools or audit integration points for better workflow automation.
  • Schedule management: Permanently delete schedules you no longer need, ensuring your campaigns stay organized and up to date.

Supported Tools & Triggers

Tools
Delete ScheduleTool to delete a specific schedule by scheduleid.
Delete Unsubscribed EmailTool to delete an email from the unsubscribed list.
Unsubscribe Lead From CampaignTool to unsubscribe a lead from a campaign.
Get All WebhooksTool to retrieve the list of all webhooks configured for the team.
Get Campaign By IDTool to retrieve a specific campaign by campaignid.
Start Campaign ExportTool to start an asynchronous export of all campaign statistics (csv).
Get Campaign Export StatusTool to check the status of an asynchronous campaign export.
Get Campaign SequencesTool to retrieve a list of all sequences for a campaign with steps and conditions.
Export Campaign LeadsTool to export campaign leads with state filtering and choose between json or csv output.
Export UnsubscribesTool to download a csv file containing all unsubscribed email addresses.
Get Unsubscribed EmailTool to retrieve a single unsubscribed email record.
Retrieve ActivitiesTool to fetch recent campaign activities.
Retrieve Lead By EmailTool to retrieve a lead by their email address.
Retrieve UnsubscribesTool to retrieve the list of all people who are unsubscribed.
Get Team CreditsTool to retrieve credits left in the team.
Get Team InfoTool to retrieve information about your team.
Get User InfoTool to retrieve all information of the authenticated user.
Mark Lead as Not Interested in CampaignTool to mark a lead as not interested in a specific campaign.
Update CampaignTool to update settings of a campaign.
Update ScheduleTool to update an existing schedule with new parameters.
Add Unsubscribe Email/DomainTool to add an email or domain to the unsubscribed list.
Add Variables to LeadTool to add one or more variables to a lead.
Associate schedule with campaignTool to associate a schedule with a campaign.
Create CampaignTool to create a new campaign.
Create ScheduleTool to create a new schedule for the team.
Mark Lead As InterestedTool to mark a lead as interested in all campaigns.
Mark Lead As Interested In CampaignTool to mark a lead as interested in a specific campaign.
Mark Lead As Not InterestedTool to mark a lead as not interested in all campaigns.
Pause a running campaignTool to pause a running campaign.
Pause LeadTool to pause a lead in all campaigns or a specific campaign.

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Lemlist connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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 crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Lemlist via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Lemlist MCP URL

Create a Composio Tool Router session for Lemlist

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["lemlist"],
)
url = session.mcp.url
What's happening:
  • You create a Lemlist only session through Composio
  • Composio returns an MCP HTTP URL that exposes Lemlist tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Lemlist Assistant",
    goal="Help users interact with Lemlist through natural language commands",
    backstory=(
        "You are an expert assistant with access to Lemlist tools. "
        "You can perform various Lemlist operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Lemlist MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Lemlist operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Lemlist related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_lemlist_agent.py

Complete Code

Here's the complete code to get you started with Lemlist and CrewAI:

python
# file: crewai_lemlist_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Lemlist session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["lemlist"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Lemlist assistant agent
    toolkit_agent = Agent(
        role="Lemlist Assistant",
        goal="Help users interact with Lemlist through natural language commands",
        backstory=(
            "You are an expert assistant with access to Lemlist tools. "
            "You can perform various Lemlist operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Lemlist operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Lemlist related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Lemlist through Composio's Tool Router. The agent can perform Lemlist 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

How to build Lemlist MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Lemlist MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Lemlist MCP with Claude Agent SDK

Google ADK

Google ADK

Use Lemlist MCP with Google ADK

LangChain

LangChain

Use Lemlist MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Lemlist MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Lemlist MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Lemlist MCP with LlamaIndex

FAQ

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

With a standalone Lemlist MCP server, the agents and LLMs can only access a fixed set of Lemlist tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Lemlist and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

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 Lemlist tools.

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

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

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