# Nanonets AI integration on Definable

> Nanonets provides an AI-driven Intelligent Document Processing API that transforms unstructured documents into structured data, enabling efficient data extraction and workflow automation.

## What this connects

Nanonets provides an AI-driven Intelligent Document Processing API that transforms unstructured documents into structured data, enabling efficient data extraction and workflow automation.

Vendor: https://app.nanonets.com

## Tools available

**11** tools available. First 11:

- `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.

## Auth

Auth schemes: `API_KEY`.

## How agents use Nanonets

Inside a Definable workflow, Nanonets is one of the tools the **Distributor specialist** can call. Example coordination patterns:

- **Researcher → Nanonets** — the Researcher (GPT-5.5) pulls context from Nanonets (records, threads, documents), synthesises findings, and briefs the rest of the team.
- **Writer → Distributor → Nanonets** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Nanonets (create record, post message, draft email).
- **Designer / Engineer → Distributor → Nanonets** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Nanonets (attach file, open PR comment, post status).

The Verifier checks every Nanonets call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention.

## Categories

- ai document extraction — https://definable.ai/apps/category/ai-document-extraction/
- artificial intelligence — https://definable.ai/apps/category/artificial-intelligence/

## Related

- HTML page: https://definable.ai/apps/nano_nets/
- Same category (ai document extraction): https://definable.ai/apps/category/ai-document-extraction/
- All integrations: https://definable.ai/apps/
- Workflow (multi-agent loop): https://definable.ai/workflow/
- Apps llms.txt index: https://definable.ai/llms-apps.txt