How I Built a Multi-Agent Medical AI using LangGraph & Llama 3

When building AI applications, relying on a single LLM prompt is no longer enough. To build HealthConnect Pro—an enterprise-grade medical triage platform—I needed multiple AI agents to debate, research, and communicate with each other.

In this case study, I will show you exactly how I architected a Multi-Agent System (MAS) using LangGraph, Llama 3.1 70B, and FastMCP.

🚨 The Problem: Single-Prompt AI is Dangerous in Healthcare

If you ask a standard AI to diagnose a patient, it hallucinates. In healthcare, hallucinations can be fatal.

I needed a system that could:

1. Extract symptoms accurately.
2. Search the real world for verified doctors.
3. Have multiple AI "Doctors" debate the diagnosis before showing it to the user.

🏗️ The Solution: LangGraph Architecture

Instead of a linear script, I used LangGraph to create a Directed Acyclic Graph (DAG). I split the AI into specialized "Agents":

1. The Triage Agent

This agent's only job is to read the patient's input and extract clinical entities. It doesn't diagnose; it just categorizes (e.g., "Cardiology", "Urgency: High").

2. The Discovery Agent (Serper.dev Integration)

Once the Triage Agent decides the patient needs a Cardiologist, the Discovery Agent takes over. It uses the Google Places API (via Serper.dev) to find real, verified clinics near the patient using the Haversine distance formula.

3. The "Clinical Board" Debate Arena

This is where the magic happens. Before giving the patient a final answer, I created a 4-node interactive debate:

• Diagnostic Lead: Proposes a diagnosis.
• Clinical Reviewer: Tries to find flaws in the Lead's diagnosis.
• Medical Analyst: Checks against real-world data.
• Clinical Director: Makes the final, safe decision.

By forcing the AI to debate itself, hallucinations dropped to near zero.

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💻 Code Deep Dive: Handling Token Exhaustion

One of the biggest challenges I faced was API rate limits. Llama 3.1 70B is heavy. If the API rate-limited my app, the whole system would crash.

To fix this, I engineered a safe_invoke fallback mechanism.

If the 70B model fails, the system instantly catches the error and routes the request to the smaller, faster Llama 3.1 8B model. The user experiences zero downtime.

# Example of the Fallback Logic Concept
def safe_invoke(prompt):
    try:
        return invoke_llama_70b(prompt)
    except RateLimitError:
        print("70B Exhausted. Falling back to 8B...")
        return invoke_llama_8b(prompt)

🚀 The Result

By combining LangGraph's state management with FastMCP for secure email routing, HealthConnect Pro successfully automates the journey from raw symptoms to a booked specialist appointment. Want to see the raw code? I open-sourced the architecture. You can check out my implementation of the LangGraph nodes and FastMCP servers on request.