How to integrate Nano nets MCP with CrewAI

This guide walks you through connecting Nano nets to CrewAI using the Composio tool router. By the end, you'll have a working Nano nets agent that can extract table data from recent invoices, upload new receipts for ocr model training, list all documents processed by a workflow through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Nano nets account through Composio's Nano nets MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Nano nets is an AI-powered Intelligent Document Processing API for extracting structured data from unstructured documents. It boosts workflow automation by turning messy documents into clean, usable information.

11 Tools

Introduction

This guide walks you through connecting Nano nets to CrewAI using the Composio tool router. By the end, you'll have a working Nano nets agent that can extract table data from recent invoices, upload new receipts for ocr model training, list all documents processed by a workflow through natural language commands.

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

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

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

The Nano nets MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Nano nets account. It provides structured and secure access to your intelligent document processing tools, so your agent can create, manage, and train OCR models, extract data from documents, and automate document workflows on your behalf.

  • Automated document data extraction: Let your agent process unstructured documents and pull out structured data using Nano nets' powerful AI-driven OCR models.
  • OCR model management: Easily create, update, and delete OCR models, allowing your agent to adjust to changing document types and business needs.
  • Workflow and document handling: Enable your agent to list, track, and manage documents within workflows, so you can monitor processing status and outcomes efficiently.
  • Training image uploads and model improvement: Have your agent upload new training images to OCR models, continually improving accuracy and adapting to new document formats.
  • Comprehensive model insights: Retrieve detailed information about your OCR models and their prediction files, empowering your agent to audit, debug, or optimize model performance as 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:

  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 Nano nets 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 Nano nets 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 Nano nets MCP URL
6

Create a Composio Tool Router session for Nano nets

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

url = session.mcp.url
What's happening:
  • You create a Nano nets only session through Composio
  • Composio returns an MCP HTTP URL that exposes Nano nets 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 Nano nets 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=["nano_nets"],
)
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 Nano nets through Composio's Tool Router. The agent can perform Nano nets 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 Nano nets action and event your agent gets out of the box.

Create Model

Tool to create a new image classification or OCR model.

Delete OCR Model

Permanently deletes an OCR model from Nanonets.

Get All Models

Retrieves all models (OCR and Image Classification) in the user's NanoNets account.

Get All Prediction Files

Retrieve all prediction files (OCR results) for a NanoNets model.

Get OCR Model Details

Tool to retrieve details of an OCR model.

Get OCR Training Images

Tool to retrieve training images for an OCR model.

Get Workflows

Tool to retrieve a list of all workflows in your Nanonets account.

List Workflow Documents

Retrieve a paginated list of documents processed by a NanoNets workflow.

Update Model AI Guidelines

Update AI Agent guidelines for an OCR model.

Upload Training Images by File

Tool to upload a training image file to a specified OCR model.

Upload Training Images by URL

Tool to upload training images by URL to a specified OCR model.

FRAMEWORKS

How to build Nano nets MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Nano nets MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Nano nets MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Nano nets MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Nano nets MCP with Claude Cowork

Codex logo

Codex

Use Nano nets MCP with Codex

OpenClaw logo

OpenClaw

Use Nano nets MCP with OpenClaw

Hermes logo

Hermes

Use Nano nets MCP with Hermes

CLI logo

CLI

Use Nano nets MCP with CLI

Google ADK logo

Google ADK

Use Nano nets MCP with Google ADK

LangChain logo

LangChain

Use Nano nets MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Nano nets MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Nano nets MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Nano nets MCP with LlamaIndex

FAQ

Frequently asked questions

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

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

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