swarmauri_tool_jupyterfromdict 0.11.0.dev1

Swarmauri notebook-construction tool for converting Python dictionaries into validated Jupyter NotebookNode objects.

Swarmauri Jupyter From Dict Tool

swarmauri_tool_jupyterfromdict converts a plain Python dictionary into a validated NotebookNode with nbformat, making it useful for programmatic notebook construction inside Swarmauri workflows.

Why

• Build notebook objects from generated or transformed Python data.
• Normalize notebook creation behind a simple tool contract.
• Validate notebook structure at the point of conversion instead of later in the pipeline.

Features

• Accepts a plain dictionary representing notebook structure.
• Converts the dictionary with nbformat.from_dict.
• Validates the resulting notebook object with nbformat.validate.
• Returns notebook_node on success.
• Returns a structured error payload when conversion or validation fails.

FAQ

What input does this tool expect?

It expects a Python dictionary that follows notebook structure conventions such as nbformat, nbformat_minor, cells, and metadata.

Does it return JSON?

No. It returns a NotebookNode object in notebook_node.

When should I use this instead of swarmauri_tool_jupyterreadnotebook?

Use this package when your notebook starts as in-memory Python data. Use swarmauri_tool_jupyterreadnotebook when the notebook already exists on disk.

Installation

uv add swarmauri_tool_jupyterfromdict

pip install swarmauri_tool_jupyterfromdict

Usage

from swarmauri_tool_jupyterfromdict import JupyterFromDictTool

payload = {
    "nbformat": 4,
    "nbformat_minor": 5,
    "cells": [],
    "metadata": {},
}

result = JupyterFromDictTool()(notebook_dict=payload)
print(result)

Examples

Create a notebook node from generated data

result = JupyterFromDictTool()(
    notebook_dict={
        "nbformat": 4,
        "nbformat_minor": 5,
        "cells": [
            {
                "cell_type": "markdown",
                "metadata": {},
                "source": ["# Generated notebook"],
            }
        ],
        "metadata": {},
    }
)

node = result["notebook_node"]

Detect invalid notebook structure

result = JupyterFromDictTool()(
    notebook_dict={"cells": [], "metadata": {}}
)

if "error" in result:
    print(result["error"])

Related Packages

• swarmauri_tool_jupytervalidatenotebook
• swarmauri_tool_jupyterreadnotebook
• swarmauri_tool_jupyterwritenotebook
• swarmauri_tool_jupyterexecutenotebook
• swarmauri_tool_jupyterclearoutput

Foundational Swarmauri Packages

• swarmauri
• swarmauri_core
• swarmauri_base
• swarmauri_standard

More Documentation

• Swarmauri SDK repository
• nbformat documentation
• Jupyter documentation