How to integrate DocuPipe MCP with CrewAI

This guide walks you through connecting DocuPipe to CrewAI using the Composio tool router. By the end, you'll have a working DocuPipe agent that can parse invoices and extract payment fields, classify contracts by risk level, export reviewed documents as structured json through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a DocuPipe account through Composio's DocuPipe MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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DocuPipe

Api Key

DocuPipe is a document automation API for parsing, classifying, reviewing, and exporting structured data. Use it to turn messy PDFs and files into clean data your workflows can trust.

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Introduction

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

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

Also integrate DocuPipe 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 DocuPipe connection
Set up CrewAI with an MCP enabled agent
Create a Tool Router session or standalone MCP server for DocuPipe
Build a conversational loop where your agent can execute DocuPipe 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 DocuPipe MCP server, and what's possible with it?

The DocuPipe MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your DocuPipe account. It provides structured and secure access to your documents, extraction schemas, processing jobs, and usage data, so your agent can upload and parse documents, extract structured information, track processing, find records, and manage files on your behalf.

Document upload and parsing: Have your agent submit a local file or remote file for parsing, then retrieve its processed content, page count, metadata, and file details.
Structured data extraction: Direct your agent to start structured extraction on a processed document and retrieve the completed results with field details.
Reusable extraction schemas: Let the agent create extraction schemas with custom guidelines, inspect existing schemas, and list the schemas available in your workspace.
Document and job tracking: Instruct your agent to find documents by dataset, creation time, filename, or search terms, and check the progress of processing jobs.
Workspace management and usage: Have your agent review plan and credit usage, inspect job activity, and permanently delete one or several documents when they are no longer needed.

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 DocuPipe 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 DocuPipe 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 DocuPipe MCP URL

Create a Composio Tool Router session for DocuPipe

python

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

url = session.mcp.url

What's happening:

You create a DocuPipe only session through Composio
Composio returns an MCP HTTP URL that exposes DocuPipe 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 DocuPipe 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=["docupipe"],
)
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 DocuPipe through Composio's Tool Router. The agent can perform DocuPipe 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 DocuPipe action and event your agent gets out of the box.

Create Schema

Create a reusable extraction schema from a valid JSON Schema and optional extraction guidelines.

Delete Document

Permanently delete one previously submitted DocuPipe document by ID.

Delete Documents

Permanently delete multiple previously submitted DocuPipe documents by ID in one batch.

Find Documents

Browse documents by dataset and creation time, or search filenames and document IDs when query is provided.

Find Jobs

Retrieve one asynchronous job by ID, or list and filter jobs when job_id is omitted.

Get Account Usage

Return current plan and credit state together with job counts and credits consumed by job type, optionally including daily usage.

Get Document

Retrieve a processed document, including processing state, parsed content, page count, metadata, and file details.

Get Schema

Retrieve one extraction schema and its full JSON Schema definition by ID.

Get Standardization

Retrieve one completed standardization as structured JSON, including extraction data and field metadata.

List Schemas

List extraction schemas available to the connected workspace, optionally including each full JSON Schema payload.

List Standardizations

List structured extraction results, filtered by schema, document, or up to 1000 known standardization IDs.

Standardize Document

Start V3 agentic structured extraction for one processed document and return the job and standardization IDs.

Upload Document

Submit one local file or remote file URL for asynchronous parsing.

FRAMEWORKS

How to build DocuPipe MCP Agent with another framework

OpenAI Agents SDK

Use DocuPipe MCP with OpenAI Agents SDK

Claude Agent SDK

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Claude Code

Use DocuPipe MCP with Claude Code

Claude Cowork

Use DocuPipe MCP with Claude Cowork

Codex

Use DocuPipe MCP with Codex

OpenClaw

Use DocuPipe MCP with OpenClaw

Hermes

Use DocuPipe MCP with Hermes

CLI

Use DocuPipe MCP with CLI

Google ADK

Use DocuPipe MCP with Google ADK

LangChain

Use DocuPipe MCP with LangChain

Vercel AI SDK

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Mastra AI

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LlamaIndex

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FAQ

Frequently asked questions

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

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

Start with DocuPipe.It takes 30 seconds.

Managed auth, hosted MCP servers, and every DocuPipe 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 DocuPipe MCP with CrewAI

Connect DocuPipe without auth hassles

Introduction

Also integrate DocuPipe with

TL;DR

What is CrewAI?

What is the DocuPipe 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 DocuPipe

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

Complete Code

Conclusion

Supported Tools

How to build DocuPipe 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 DocuPipe MCP?+

Can I use Tool Router MCP with CrewAI?+

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

How safe is my data with Composio Tool Router?+

Start with DocuPipe.It takes 30 seconds.

Teams ship real agents on Composio