Build a Multi‑Agent Health Report System with CrewAI, GPT‑4o, and Tesseract OCR
Build a multi‑agent pipeline that extracts lab data with Tesseract, feeds it to a GPT‑4o researcher, nutritionist, and code executor, then outputs a personalized health report.
Set up a Python 3.10+ environment, install the listed packages, define the three CrewAI agents, and run the pipeline to generate a personalized health report.
Summary
Article explains how to build a production‑grade multi‑agent system that turns scanned lab reports into a personalized health roadmap. The stack uses Python 3.10+, Tesseract OCR to extract raw text, CrewAI for agent orchestration, LangChain‑OpenAI for GPT‑4o calls, and the Serper API for live medical literature searches.
Three specialized agents are defined: a Medical Literature Specialist that queries clinical guidelines, a Functional Nutritionist that drafts supplement and meal plans, and a Data & Calculation Analyst that computes dosage calculations with code execution enabled. The workflow starts with the OCR agent feeding structured data to the researcher, whose output becomes the context for the nutritionist, and finally the analyst produces the final report. The example includes a pip install line that pulls crewai, langchain_openai, pytesseract, opencv-python, and serper-api, and ends with a call to action to test the pipeline and produce actionable items for users.
Key changes
- Uses Python 3.10+ and pip installs crewai, langchain_openai, pytesseract, opencv-python, serper-api
- Employs Tesseract OCR to convert PDF/image lab reports into raw text
- Defines three CrewAI agents: Medical Literature Specialist, Functional Nutritionist, Data & Calculation Analyst
- Integrates GPT‑4o via LangChain‑OpenAI for natural‑language reasoning
- Uses Serper API for live medical literature searches
- Orchestrates tasks so researcher output feeds nutritionist, then analyst for dosage calculations