Stop Guessing Your Meds: Building a Multi-Step Drug Interaction Agent with LangGraph and DrugBank

Source: Dev.to

Overview

When it comes to healthcare, “hallucination” isn’t just a quirky AI bug—it’s a critical safety risk. Building a system that flags Drug‑Drug Interactions (DDI) requires more than a simple LLM prompt; it needs rigorous logic, structured data validation, and multi‑step reasoning.

In this tutorial we’ll build a sophisticated Medical Safety Agent using:

• LangGraph – orchestration logic
• DrugBank API – gold‑standard interaction data
• Tavily Search API – latest FDA alerts
• Pydantic – strict schema validation

The agent will perform structured lookups, cross‑reference allergy histories, and output a clinical‑grade safety report. Whether you’re building the next big MedTech app or just exploring LangGraph’s cyclic capabilities, this guide is for you.

Why Not Traditional RAG?

Traditional Retrieval‑Augmented Generation (RAG) often fails in medical contexts because it lacks the branching logic needed for complex scenarios (e.g., “If Drug A and B interact, check if the patient’s allergy to Drug C makes it worse”).

With LangGraph we can create a state machine where the agent can “loop back” to clarify information or perform additional searches if the initial data is insufficient.

Architecture Diagram

graph TD
    A[User Input: Meds & Allergies] --> B(State Parser)
    B --> C{Interaction Agent}
    C -->|Lookup| D[DrugBank API / Tavily]
    D -->|Data Found| E{Conflict Detected?}
    E -->|Yes| F[Risk Assessment Node]
    E -->|No| G[Final Safety Report]
    F --> H[Cross‑reference Allergies]
    H --> G
    G --> I((Output to User))
    style C fill:#f96,stroke:#333,stroke-width:2px
    style G fill:#00ff0022,stroke:#333

Tech Stack

• LangGraph – orchestration logic
• Pydantic – strict schema validation (crucial for medical data)
• DrugBank API – interaction data source
• Tavily Search API – searching latest FDA alerts not yet in databases

Data Models

from pydantic import BaseModel, Field
from typing import List, Optional

class InteractionDetail(BaseModel):
    severity: str = Field(description="High, Medium, or Low")
    description: str = Field(description="Detailed explanation of the interaction")
    evidence: str = Field(description="Source of this information (e.g., DrugBank)")

class MedicationSafetyReport(BaseModel):
    is_safe: bool
    conflicts_found: List[InteractionDetail]
    allergy_warnings: List[str]
    recommendation: str = Field(description="Actionable advice for the patient")

Tool Definitions

from langchain_core.tools import tool
from typing import List

@tool
def check_drug_interaction(drug_list: List[str]):
    """Fetches interaction data between a list of medications from DrugBank."""
    # Logic to call DrugBank API
    # For demo purposes, we return a simulated response
    return f"Checking interactions for: {', '.join(drug_list)}... Potential interaction found between Aspirin and Warfarin."

@tool
def search_latest_fda_alerts(query: str):
    """Searches for the most recent FDA safety warnings using Tavily."""
    # Tavily implementation here
    return f"Recent alert: Increased risk of bleeding observed in combination therapy..."

State Definition & Graph Construction

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated, Sequence, List, Optional
import operator

class AgentState(TypedDict):
    messages: Annotated[Sequence[str], operator.add]
    medications: List[str]
    allergies: List[str]
    report: Optional[MedicationSafetyReport]

def interaction_analysis_node(state: AgentState):
    # The LLM decides which tools to call based on state.medications
    # It uses the Pydantic schema defined above
    return {"messages": ["Analyzing interaction data..."]}

# Define the Graph
workflow = StateGraph(AgentState)

workflow.add_node("analyze", interaction_analysis_node)
workflow.set_entry_point("analyze")
workflow.add_edge("analyze", END)

app = workflow.compile()

Production Considerations

• PHI compliance – ensure data is encrypted at rest and in transit.
• Latency – multi‑step reasoning can add overhead; consider async calls and caching.
• Model fine‑tuning – domain‑specific data improves reliability.

For deeper dives on scaling AI agents in regulated industries, see the WellAlly Tech Blog (engineering deep‑dives).

Running the Agent

inputs = {
    "medications": ["Aspirin", "Warfarin", "Lisinopril"],
    "allergies": ["Sulfa drugs"],
    "messages": ["Is it safe to take these medications together?"]
}

for output in app.stream(inputs):
    for key, value in output.items():
        print(f"Node: {key}")
        # In a real app, this would display the Pydantic‑validated report

Key Benefits

• Iterative Reasoning – If a conflict between Aspirin and Warfarin is found, the agent can trigger a second search (e.g., “Aspirin/Warfarin dosage risks”) before delivering the final answer.
• Type Safety – Pydantic guarantees the frontend receives a JSON object it can reliably parse into a UI warning component.
• Audit Trail – LangGraph’s state management logs every “thought” the agent had, which is vital for medical auditing.

What’s Next?

• Add a Human‑in‑the‑Loop node for pharmacist approval.
• Integrate with an EHR (Electronic Health Record) system via FHIR APIs.

Have you tried building… (the tutorial continues).

Building medical agents? What are the biggest hurdles you’ve faced? Let me know in the comments! 👇

If you enjoyed this tutorial, don’t forget to visit wellally.tech/blog for more advanced tutorials on AI Agents and MedTech innovation!