# How to integrate Nano nets MCP with CrewAI ```json { "title": "How to integrate Nano nets MCP with CrewAI", "toolkit": "Nano nets", "toolkit_slug": "nano_nets", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/nano_nets/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/nano_nets/framework/crew-ai.md", "updated_at": "2026-05-12T10:19:46.789Z" } ``` ## 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 SDK](https://composio.dev/toolkits/nano_nets/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/nano_nets/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/nano_nets/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/nano_nets/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/nano_nets/framework/codex) - [OpenClaw](https://composio.dev/toolkits/nano_nets/framework/openclaw) - [Hermes](https://composio.dev/toolkits/nano_nets/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/nano_nets/framework/cli) - [Google ADK](https://composio.dev/toolkits/nano_nets/framework/google-adk) - [LangChain](https://composio.dev/toolkits/nano_nets/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/nano_nets/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/nano_nets/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/nano_nets/framework/llama-index) ## 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. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `NANO_NETS_CREATE_MODEL` | Create Model | Tool to create a new image classification or OCR model. Use when you need to initialize a model before uploading training images. Provide a list of categories/classes that the model should learn to identify or extract. | | `NANO_NETS_DELETE_MODEL` | Delete OCR Model | Permanently deletes an OCR model from Nanonets. Use this action when you need to remove a trained model that is no longer needed. This action is irreversible - once deleted, the model and all its training data cannot be recovered. Prerequisites: Obtain the model_id from the 'Get all OCR models' action first. | | `NANO_NETS_GET_ALL_MODELS` | Get All Models | Retrieves all models (OCR and Image Classification) in the user's NanoNets account. Returns model details including ID, type, status, accuracy, and extractable fields/categories. Use to discover available models before performing predictions or training operations. | | `NANO_NETS_GET_ALL_PREDICTION_FILES` | Get All Prediction Files | Retrieve all prediction files (OCR results) for a NanoNets model. Use this tool to: - List all documents/images that have been processed by an OCR model - Get prediction results including extracted text and field values - Access file URLs and processing status for each prediction The response includes prediction labels with extracted text, confidence scores, and bounding box coordinates for each processed file. | | `NANO_NETS_GET_MODEL_DETAILS` | Get OCR Model Details | Tool to retrieve details of an OCR model. Use when you need full metadata of a model by its ID. | | `NANO_NETS_GET_TRAINING_IMAGES` | Get OCR Training Images | Tool to retrieve training images for an OCR model. Use when you need to page through images associated with a model before training or analysis. | | `NANO_NETS_GET_WORKFLOWS` | Get Workflows | Tool to retrieve a list of all workflows in your Nanonets account. Use when you need to inventory or inspect all configured workflows. | | `NANO_NETS_LIST_DOCUMENTS` | List Workflow Documents | Retrieve a paginated list of documents processed by a NanoNets workflow. Returns document metadata including processing status, upload timestamp, verification status, and page details. Use this to monitor document processing progress or access extracted data from previously uploaded documents. | | `NANO_NETS_UPDATE_MODEL` | Update Model AI Guidelines | Update AI Agent guidelines for an OCR model. Sets instructions for how the AI should handle field and table predictions. Only works for Instant Learning models. Use this to customize extraction behavior for specific document types. | | `NANO_NETS_UPLOAD_TRAINING_IMAGES_BY_FILE` | Upload Training Images by File | Tool to upload a training image file to a specified OCR model. Use when adding a local image file to train the model. Supported file formats include PNG, JPEG, and PDF. | | `NANO_NETS_UPLOAD_TRAINING_IMAGES_BY_URL` | Upload Training Images by URL | Tool to upload training images by URL to a specified OCR model. Use when adding URLs of images to a model for training purposes. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Nano nets MCP server is an implementation of the Model Context Protocol that connects your AI agent to Nano nets. It provides structured and secure access so your agent can perform Nano nets operations on your behalf through a secure, permission-based interface. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 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 ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies **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 ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables 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 ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **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 ```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") ``` ### 5. Create a Composio Tool Router session for Nano nets **What's happening:** - You create a Nano nets only session through Composio - Composio returns an MCP HTTP URL that exposes Nano nets tools ```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 ``` ### 6. Initialize the MCP Server **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. ```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, ) ``` ### 7. Create a CLI Chatloop and define the Crew **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. ```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") ``` ## Complete Code ```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 ## How to build Nano nets MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/nano_nets/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/nano_nets/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/nano_nets/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/nano_nets/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/nano_nets/framework/codex) - [OpenClaw](https://composio.dev/toolkits/nano_nets/framework/openclaw) - [Hermes](https://composio.dev/toolkits/nano_nets/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/nano_nets/framework/cli) - [Google ADK](https://composio.dev/toolkits/nano_nets/framework/google-adk) - [LangChain](https://composio.dev/toolkits/nano_nets/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/nano_nets/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/nano_nets/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/nano_nets/framework/llama-index) ## Related Toolkits - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data. - [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease. - [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training. - [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses. - [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files. - [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently. - [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price. - [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere. - [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code. - [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more. - [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place. - [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work. - [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems. - [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project. - [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks. - [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API. - [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Nano nets MCP? 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. ### 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 Nano nets tools. ### Can I manage the permissions and scopes for Nano nets while using Tool Router? 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. ### 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 Nano nets data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)