About IntelliNode Medical Use Cases
IntelliNode is an open-source library for graph-based AI workflow orchestration; this repository offers educational multi-agent examples for healthcare and wellness, including MCP-based clinical data prediction. The examples are not intended for patient care.
Key features
- 01Orchestrate graph-based AI workflows
- 02Demonstrate multiple AI providers
- 03Show text, image, and speech generation
- 04Run MCP-based clinical data prediction
- 05Serve CSV data with Polars or Pandas
Use cases
- 01Study nutrition assessment and meal planning
- 02Demonstrate collaboration across models
- 03Build an educational clinical prediction example
MCP support
The repository includes an MCP server that serves CSV files using Polars or Pandas for its graph-based medical prediction example.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEIntelliNode Medical Use Cases
IntelliNode is an open-source library for orchestrating AI workflows using graph-based architectures. This repository contains educational examples demonstrating how multi-agent systems can be applied to healthcare and wellness scenarios.
Install Intellinode
# Basic installation
pip install intelli
# With MCP support
pip install "intelli[mcp]"
Environment Setup
Create a .env file in the project root with these keys:
OPENAI_API_KEY=your_openai_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here
Install project dependencies:
pip install -r requirements.txt
Launch Jupyter:
jupyter lab
MCP Server
To run the MCP server that serves CSV files using Polars:
pip install -r requirements.txt
cd mcp_server
# polars data provider
python eicu_mcp_server_polars.py
(Alternative) Start the server using the Pandas as data provider:
python eicu_mcp_server.py
Lab Overview
Lab 1: Nutrition Assessment with IntelliNode
- OpenAI GPT-4 analyzes client notes, Anthropic Claude creates meal plans.
- Demonstrates connecting multiple AI providers in healthcare workflows.
Lab 2: Multiple Models with IntelliNode
- Showcases text, image, and speech generation in one system.
Lab 3: MCP Medical Prediction with Graph
- Medical prediction system using Model Context Protocol (MCP).
- MCP server serves CSV files using Polars backend.
- Agents predict outcomes from clinical data.
Slides
PyData - Graph Theory for Multi-Agent Integration https://www.slideshare.net/slideshow/pydata-graph-theory-for-multi-agent-integration/280302074
⚠️ Important Disclaimer
These examples are provided for educational purposes only, and are not intended for actual patient care as presented.
For production deployments, you must implement logging, secure clinical approvals, and establish appropriate governance around the workflow.
Lab Contribution
The use case and examples in this repository were provided by MedWrite.ai.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.