A Confidence-Aware Multi-Agent RAG System for Healthcare using LangGraph

🔗 Project Links

• GitHub Repository:
  https://github.com/lookmohan/multi-agent-healthcare-rag-langgraph

• Google Colab Notebook (Executable):
  https://colab.research.google.com/drive/1S58SpHUMD9Yr65NqIbQjLizXTbKFFF0_?usp=sharing

🧭 Introduction

Large Language Models (LLMs) have rapidly become powerful tools for natural language understanding and generation. In domains such as healthcare, they show promise for tasks like clinical question answering, literature summarization, and decision support. However, healthcare is a high-stakes domain, where incorrect, biased, or overconfident responses can have serious consequences.

Traditional LLM-based systems and even standard Retrieval-Augmented Generation (RAG) pipelines often suffer from key limitations:

• Hallucinated answers when relevant documents are missing
• Overconfidence even when uncertainty is high
• Poor transparency into how answers are formed
• Limited risk awareness and safety reasoning

This project introduces a confidence-aware multi-agent RAG system built using LangGraph, designed to address these challenges through explicit agent collaboration, risk analysis, and iterative refinement.

🎯 Problem Statement

Single-agent RAG systems typically follow a linear pipeline: retrieve documents → generate answer. While effective in some domains, this approach is insufficient for healthcare because:

• Retrieval relevance is not always validated
• Risk and uncertainty are not explicitly modeled
• The system rarely admits “I don’t know”
• No mechanism exists to iteratively improve low-quality answers

The goal of this project is to design a system that:

1. Decomposes reasoning into specialized agents
2. Explicitly evaluates document relevance
3. Identifies risks and limitations
4. Quantifies answer confidence
5. Iteratively improves responses when confidence is low

🧠 Why a Multi-Agent Approach?

Healthcare decision-making naturally involves multiple perspectives: research evidence, clinical insight, risk assessment, and quality evaluation. This project mirrors that structure by assigning distinct responsibilities to different AI agents, rather than relying on a single monolithic model.

Using a multi-agent architecture enables:

• Clear separation of concerns
• More interpretable reasoning steps
• Better safety through redundancy and critique
• Modular extensibility for future improvements

LangGraph is used as the orchestration framework to manage agent execution, state transitions, and iterative control.

🏗️ System Architecture Overview

The system follows a structured, agentic workflow:

1️⃣ Query Analysis Agent

• Receives the user query
• Samples top-k retrieved documents
• Determines whether the query is answerable from the knowledge base
• Prevents misleading RAG responses when context is insufficient

2️⃣ Research Agent

• Retrieves healthcare documents using FAISS vector similarity search
• Applies relevance filtering
• Explicitly acknowledges when no relevant context is found
• Falls back to general knowledge with clear disclaimers

3️⃣ Insight Agent

• Analyzes retrieved context
• Extracts high-level insights and implications
• Focuses on interpretation rather than repetition

4️⃣ Risk Agent

• Identifies:
  • Clinical safety risks
  • Bias and fairness issues
  • Regulatory and compliance concerns
  • Uncertainties and limitations

5️⃣ Decision Agent

• Synthesizes outputs from all previous agents
• Produces a structured, coherent final answer
• Adds explicit relevance and limitation notes when needed

6️⃣ Confidence Agent

• Evaluates the final answer on a scale of 0 to 1
• Considers completeness, accuracy, clarity, and specificity
• Penalizes answers not grounded in retrieved documents

🔁 Confidence-Based Iteration (MCP-Style Control)

A confidence-aware routing mechanism is implemented:

• If confidence < 0.7
• And iteration count < 3
• The system loops back to the research stage
• Attempts to improve grounding and answer quality

This prevents both:

• Overconfident hallucinations
• Infinite reasoning loops

▶️ How to Run the Project (Google Colab)

This project is designed to run entirely in Google Colab, ensuring easy reproducibility.

Step 1: Open the Notebook

Open the Colab notebook using the link below:

[https://colab.research.google.com/drive/1S58SpHUMD9Yr65NqIbQjLizXTbKFFF0](https://colab.research.google.com/drive/1S58SpHUMD9Yr65NqIbQjLizXTbKFFF0)_

Step 2: Enable GPU (Optional)

Runtime → Change runtime type → Hardware Accelerator → GPU

Step 3: Install Dependencies

!pip install langgraph langchain faiss-cpu sentence-transformers pypdf groq

Step 4: Set API Key

import os
os.environ["GROQ_API_KEY"] = "your_api_key_here"

Step 5: Run the Multi-Agent System

result = app.invoke({
    "query": "What are the main risks and best practices for deploying LLMs in healthcare?",
    "messages": []
})

📊 Example Output

The system produces:

• Context relevance analysis
• Research, Insight, and Risk agent summaries
• A synthesized final answer
• A confidence score with reasoning

If no relevant documents are found, the system explicitly states this and lowers the confidence score, avoiding misleading certainty.

⚠️ Limitations

• Output quality depends on document coverage
• Confidence scoring is heuristic-based
• The system does not replace medical professionals or regulatory review

🔮 Future Improvements

• Human-in-the-loop review
• Streamlit UI with download support
• Formal evaluation metrics
• Expanded healthcare document sets
• Advanced MCP-based agent communication

✅ Conclusion

This project demonstrates how agentic AI principles and multi-agent orchestration using LangGraph can significantly improve the safety, transparency, and reliability of RAG systems in healthcare. By decomposing reasoning across specialized agents and introducing confidence-based control, the system provides a strong foundation for responsible AI deployment in high-risk domains.