How to integrate iLovePDF MCP with CrewAI

This guide walks you through connecting iLovePDF to CrewAI using the Composio tool router. By the end, you'll have a working iLovePDF agent that can compress pdfs, merge your documents, split pdfs, convert images to pdf, and turn pdfs into images through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a iLovePDF account through Composio's iLovePDF MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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iLovePDF

Api Key

iLovePDF is a PDF and image processing API for converting, compressing, editing, and signing files. Use it to automate document workflows without building PDF tooling from scratch.

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Introduction

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

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

Also integrate iLovePDF with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex

TL;DR

Here's what you'll learn:

Get a Composio API key and configure your iLovePDF connection
Set up CrewAI with an MCP enabled agent
Create a Tool Router session or standalone MCP server for iLovePDF
Build a conversational loop where your agent can execute iLovePDF 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 iLovePDF MCP server, and what's possible with it?

The iLovePDF MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your iLovePDF account. It provides structured and secure access to your PDF and image processing tools, so your agent can compress files, convert formats, merge documents, split PDFs, and monitor processing tasks on your behalf.

PDF and image compression: Have your agent compress one or more PDFs or images to reduce file sizes and return the processed files.
PDF and image conversion: Direct your agent to turn images into PDFs or convert PDF pages into images.
PDF merging: Instruct your agent to combine two or more PDFs in your chosen order and return one merged document.
Flexible PDF splitting: Let the agent split a PDF by page ranges or fixed intervals, or create a new document with selected pages removed.
Task and account management: Have your agent check processing progress, review output details and remaining allowance, or delete completed tasks and their files.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account and API key
A iLovePDF 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[mcp] python-dotenv

What's happening:

composio connects your agent to iLovePDF via MCP
crewai provides Agent, Task, Crew, and LLM primitives
crewai-tools[mcp] 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

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 iLovePDF MCP URL

Create a Composio Tool Router session for iLovePDF

python

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

url = session.mcp.url

What's happening:

You create a iLovePDF only session through Composio
Composio returns an MCP HTTP URL that exposes iLovePDF tools

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.

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 iLovePDF 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=["i_love_pdf"],
)
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 iLovePDF through Composio's Tool Router. The agent can perform iLovePDF 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 iLovePDF action and event your agent gets out of the box.

Compress Images

Compress one or more images and immediately return the resulting image or ZIP as an offloaded download.

Compress PDFs

Compress one or more PDFs and return the stored PDF or ZIP output in one workflow: task creation, ordered uploads, synchronous processing, download, and best-effort cleanup.

Convert Images to PDF

Convert one or more images into PDF output using iLovePDF's documented defaults and immediately return the generated PDF or ZIP as an offloaded download.

Convert PDF to Images

Convert a PDF using iLovePDF's default PDF-to-JPG behavior and immediately return the generated image or ZIP as an offloaded download.

Delete Task

Immediately delete an iLovePDF task and its related uploaded and processed files from the task's assigned worker.

Get Account Info

Return the connected iLovePDF project's account type and remaining processing allowance.

Get Task Status

Inspect an iLovePDF task's current state and output metadata on its assigned worker.

Merge PDFs

Merge two or more PDFs in the supplied order and return the merged file in one workflow.

Split PDF

Split one PDF by explicit ranges, fixed page intervals, or removed pages and return the resulting PDF or ZIP.

FRAMEWORKS

How to build iLovePDF MCP Agent with another framework

OpenAI Agents SDK

Use iLovePDF MCP with OpenAI Agents SDK

Claude Agent SDK

Use iLovePDF MCP with Claude Agent SDK

Claude Code

Use iLovePDF MCP with Claude Code

Claude Cowork

Use iLovePDF MCP with Claude Cowork

Codex

Use iLovePDF MCP with Codex

OpenClaw

Use iLovePDF MCP with OpenClaw

Hermes

Use iLovePDF MCP with Hermes

CLI

Use iLovePDF MCP with CLI

Google ADK

Use iLovePDF MCP with Google ADK

LangChain

Use iLovePDF MCP with LangChain

Vercel AI SDK

Use iLovePDF MCP with Vercel AI SDK

Mastra AI

Use iLovePDF MCP with Mastra AI

LlamaIndex

Use iLovePDF MCP with LlamaIndex

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Castingwords

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FAQ

Frequently asked questions

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

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

Start with iLovePDF.It takes 30 seconds.

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

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Customer stories

Teams ship real agents on Composio

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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

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

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

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

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

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

Co-Founder, Ingram Technologies

How to integrate iLovePDF MCP with CrewAI

Connect iLovePDF without auth hassles

Introduction

Also integrate iLovePDF with

TL;DR

What is CrewAI?

What is the iLovePDF MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Create a Composio Tool Router session for iLovePDF

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

Complete Code

Conclusion

Supported Tools

How to build iLovePDF MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

Explore Other Toolkits

Boloforms

Box

Carbone

Castingwords

Frequently asked questions

What are the differences in Tool Router MCP and iLovePDF MCP?+

Can I use Tool Router MCP with CrewAI?+

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

How safe is my data with Composio Tool Router?+

Start with iLovePDF.It takes 30 seconds.

Teams ship real agents on Composio