---
title: 'Healthcare Assistant with Mem0 and Google ADK'
description: 'Build a personalized healthcare agent that remembers patient information across conversations using Mem0 and Google ADK'
---

<Snippet file="security-compliance.mdx" />

# Healthcare Assistant with Memory

This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.

## Overview

The Healthcare Assistant helps patients by:
- Remembering their medical history and symptoms
- Providing general health information
- Scheduling appointment reminders
- Maintaining a personalized experience across conversations

By integrating Mem0's memory layer with Google ADK, the assistant maintains context about the patient without requiring them to repeat information.

## Setup

Before you begin, make sure you have:

Installed Google ADK and Mem0 SDK:
```bash
pip install google-adk
pip install mem0ai
```

## Code Breakdown

Let's get started and understand the different components required in building a healthcare assistant powered by memory

```python
# Import dependencies
import os
from google.adk.agents import Agent
from google.adk.sessions import InMemorySessionService
from google.adk.runners import Runner
from google.genai import types
from mem0 import MemoryClient

# Set up API keys (replace with your actual keys)
os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
os.environ["MEM0_API_KEY"] = "your-mem0-api-key"

# Define a global user ID for simplicity
USER_ID = "Alex"

# Initialize Mem0 client
mem0_client = MemoryClient()
```

## Define Memory Tools

First, we'll create tools that allow our agent to store and retrieve information using Mem0:

```python
def save_patient_info(information: str) -> dict:
    """Saves important patient information to memory."""

    # Store in Mem0
    response = mem0_client.add(
        [{"role": "user", "content": information}],
        user_id=USER_ID,
        run_id="healthcare_session",
        metadata={"type": "patient_information"}
    )


def retrieve_patient_info(query: str) -> dict:
    """Retrieves relevant patient information from memory."""

    # Search Mem0
    results = mem0_client.search(
        query,
        user_id=USER_ID,
        limit=5,
        threshold=0.7,  # Higher threshold for more relevant results
        output_format="v1.1"
    )

    # Format and return the results
    if results and len(results) > 0:
        memories = [memory["memory"] for memory in results.get('results', [])]
        return {
            "status": "success",
            "memories": memories,
            "count": len(memories)
        }
    else:
        return {
            "status": "no_results",
            "memories": [],
            "count": 0
        }
```

## Define Healthcare Tools

Next, we'll add tools specific to healthcare assistance:

```python
def schedule_appointment(date: str, time: str, reason: str) -> dict:
    """Schedules a doctor's appointment."""
    # In a real app, this would connect to a scheduling system
    appointment_id = f"APT-{hash(date + time) % 10000}"

    return {
        "status": "success",
        "appointment_id": appointment_id,
        "confirmation": f"Appointment scheduled for {date} at {time} for {reason}",
        "message": "Please arrive 15 minutes early to complete paperwork."
    }
```

## Create the Healthcare Assistant Agent

Now we'll create our main agent with all the tools:

```python
# Create the agent
healthcare_agent = Agent(
    name="healthcare_assistant",
    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
    instruction="""You are a helpful Healthcare Assistant with memory capabilities.

Your primary responsibilities are to:
1. Remember patient information using the 'save_patient_info' tool when they share symptoms, conditions, or preferences.
2. Retrieve past patient information using the 'retrieve_patient_info' tool when relevant to the current conversation.
3. Help schedule appointments using the 'schedule_appointment' tool.

IMPORTANT GUIDELINES:
- Always be empathetic, professional, and helpful.
- Save important patient information like symptoms, conditions, allergies, and preferences.
- Check if you have relevant patient information before asking for details they may have shared previously.
- Make it clear you are not a doctor and cannot provide medical diagnosis or treatment.
- For serious symptoms, always recommend consulting a healthcare professional.
- Keep all patient information confidential.
""",
    tools=[save_patient_info, retrieve_patient_info, schedule_appointment]
)
```

## Set Up Session and Runner

```python
# Set up Session Service and Runner
session_service = InMemorySessionService()

# Define constants for the conversation
APP_NAME = "healthcare_assistant_app"
USER_ID = "Alex"
SESSION_ID = "session_001"

# Create a session
session = session_service.create_session(
    app_name=APP_NAME,
    user_id=USER_ID,
    session_id=SESSION_ID
)

# Create the runner
runner = Runner(
    agent=healthcare_agent,
    app_name=APP_NAME,
    session_service=session_service
)
```

## Interact with the Healthcare Assistant

```python
# Function to interact with the agent
async def call_agent_async(query, runner, user_id, session_id):
    """Sends a query to the agent and returns the final response."""
    print(f"\n>>> Patient: {query}")

    # Format the user's message
    content = types.Content(
        role='user',
        parts=[types.Part(text=query)]
    )

    # Set user_id for tools to access
    save_patient_info.user_id = user_id
    retrieve_patient_info.user_id = user_id

    # Run the agent
    async for event in runner.run_async(
        user_id=user_id,
        session_id=session_id,
        new_message=content
    ):
        if event.is_final_response():
            if event.content and event.content.parts:
                response = event.content.parts[0].text
                print(f"<<< Assistant: {response}")
                return response

    return "No response received."

# Example conversation flow
async def run_conversation():
    # First interaction - patient introduces themselves with key information
    await call_agent_async(
        "Hi, I'm Alex. I've been having headaches for the past week, and I have a penicillin allergy.",
        runner=runner,
        user_id=USER_ID,
        session_id=SESSION_ID
    )

    # Request for health information
    await call_agent_async(
        "Can you tell me more about what might be causing my headaches?",
        runner=runner,
        user_id=USER_ID,
        session_id=SESSION_ID
    )

    # Schedule an appointment
    await call_agent_async(
        "I think I should see a doctor. Can you help me schedule an appointment for next Monday at 2pm?",
        runner=runner,
        user_id=USER_ID,
        session_id=SESSION_ID
    )

    # Test memory - should remember patient name, symptoms, and allergy
    await call_agent_async(
        "What medications should I avoid for my headaches?",
        runner=runner,
        user_id=USER_ID,
        session_id=SESSION_ID
    )

# Run the conversation example
if __name__ == "__main__":
    asyncio.run(run_conversation())
```

## How It Works

This healthcare assistant demonstrates several key capabilities:

1. **Memory Storage**: When Alex mentions her headaches and penicillin allergy, the agent stores this information in Mem0 using the `save_patient_info` tool.

2. **Contextual Retrieval**: When Alex asks about headache causes, the agent uses the `retrieve_patient_info` tool to recall her specific situation.

3. **Memory Application**: When discussing medications, the agent remembers Alex's penicillin allergy without her needing to repeat it, providing safer and more personalized advice.

4. **Conversation Continuity**: The agent maintains context across the entire conversation session, creating a more natural and efficient interaction.

## Key Implementation Details

### User ID Management

Instead of passing the user ID as a parameter to the memory tools (which would require modifying the ADK's tool calling system), we attach it directly to the function object:

```python
# Set user_id for tools to access
save_patient_info.user_id = user_id
retrieve_patient_info.user_id = user_id
```

Inside the tool functions, we retrieve this attribute:

```python
# Get user_id from session state or use default
user_id = getattr(save_patient_info, 'user_id', 'default_user')
```

This approach allows our tools to maintain user context without complicating their parameter signatures.

### Mem0 Integration

The integration with Mem0 happens through two primary functions:

1. `mem0_client.add()` - Stores new information with appropriate metadata
2. `mem0_client.search()` - Retrieves relevant memories using semantic search

The `threshold` parameter in the search function ensures that only highly relevant memories are returned.

## Conclusion

This example demonstrates how to build a healthcare assistant with persistent memory using Google ADK and Mem0. The integration allows for a more personalized patient experience by maintaining context across conversation turns, which is particularly valuable in healthcare scenarios where continuity of information is crucial.

By storing and retrieving patient information intelligently, the assistant provides more relevant responses without requiring the patient to repeat their medical history, symptoms, or preferences.
