Google ADK - Mem0

Integrate Mem0 with Google ADK (Agent Development Kit), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.

Overview

In this guide, we’ll create a Google ADK agent that:

Uses ADK’s native MemoryService interface to connect Mem0
Automatically injects relevant memories using ADK’s built-in load_memory tool
Persists session history to Mem0 after each turn via an after-agent callback
Shares memory seamlessly across multi-agent hierarchies

Setup and Configuration

Install the necessary libraries:

pip install google-adk mem0ai python-dotenv

Set up your API keys:

Mem0 API Key
Google AI Studio API Key

Remember to get your API key from Mem0 Platform and set up a Google AI Studio API Key.

import os
from dotenv import load_dotenv

load_dotenv()

# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"

Implement Mem0MemoryService

Create a custom MemoryService by implementing ADK’s BaseMemoryService. Save the following as mem0_memory_service.py:

import asyncio
import os
from typing import Optional
from typing_extensions import override

from google.adk.memory.base_memory_service import BaseMemoryService, SearchMemoryResponse
from google.adk.memory.memory_entry import MemoryEntry
from google.adk.sessions import Session
from google.genai.types import Content, Part
from mem0 import MemoryClient


class Mem0MemoryService(BaseMemoryService):
    """MemoryService implementation backed by the Mem0 Platform."""

    def __init__(self, api_key: Optional[str] = None):
        super().__init__()
        api_key = api_key or os.environ.get("MEM0_API_KEY")
        self._client: Optional[MemoryClient] = MemoryClient(api_key=api_key) if api_key else None

    @override
    async def search_memory(
        self, *, app_name: str, user_id: str, query: str
    ) -> SearchMemoryResponse:
        """Search for memories relevant to the current user and query."""
        if not self._client:
            return SearchMemoryResponse(memories=[])

        try:
            results = await asyncio.to_thread(
                self._client.search,
                query,
                filters={"AND": [{"user_id": user_id}, {"app_id": app_name}]},
                top_k=5,
            )

            entries = []
            for mem in results.get("results", []):
                text = mem.get("memory", "")
                if not text:
                    continue

                raw_ts = mem.get("created_at") or mem.get("updated_at")
                entries.append(
                    MemoryEntry(
                        content=Content(parts=[Part(text=text)]),
                        author=mem.get("metadata", {}).get("author", "user"),
                        timestamp=str(raw_ts) if raw_ts else None,
                    )
                )

            return SearchMemoryResponse(memories=entries)

        except Exception as e:
            print(f"[Mem0MemoryService] search_memory error: {e}")
            return SearchMemoryResponse(memories=[])

    @override
    async def add_session_to_memory(self, session: Session) -> None:
        """Persist a completed ADK session into Mem0."""
        if not self._client:
            return

        user_id = session.user_id
        if not user_id:
            return

        app_name = getattr(session, "app_name", None)

        try:
            messages = []
            for event in session.events:
                if not (event.content and event.content.parts):
                    continue
                role = getattr(event.content, "role", None) or "user"
                if role == "model":
                    role = "assistant"
                elif role not in ("user", "assistant"):
                    continue
                text_parts = [
                    p.text for p in event.content.parts if hasattr(p, "text") and p.text
                ]
                if text_parts:
                    messages.append({"role": role, "content": " ".join(text_parts)})

            if messages:
                metadata = {"app_id": app_name} if app_name else {}
                await asyncio.to_thread(
                    self._client.add, messages, user_id=user_id, metadata=metadata
                )

        except Exception as e:
            print(f"[Mem0MemoryService] add_session_to_memory error: {e}")

Add Auto-Save Callback

This after-agent callback fires at the end of every turn and saves the session to Mem0. Save as memory_callbacks.py:

async def save_session_to_memory(callback_context) -> None:
    """Persist the completed session to Mem0 after each agent turn."""
    try:
        await callback_context.add_session_to_memory()
    except ValueError:
        pass
    except Exception as e:
        print(f"[save_session_to_memory] error: {e}")

Basic Integration Example

The following example demonstrates creating an ADK agent with automatic Mem0 memory:

import asyncio
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools import load_memory
from google.genai.types import Content, Part

from mem0_memory_service import Mem0MemoryService
from memory_callbacks import save_session_to_memory

memory_service = Mem0MemoryService()
session_service = InMemorySessionService()

agent = LlmAgent(
    name="personal_assistant",
    model="gemini-2.0-flash",
    instruction="""You are a helpful personal assistant.
    Relevant memories from past conversations are provided to you automatically.
    Use them to personalize your responses.""",
    description="A personal assistant that remembers user preferences and past interactions",
    tools=[load_memory],
    after_agent_callback=save_session_to_memory,
)

runner = Runner(
    agent=agent,
    session_service=session_service,
    memory_service=memory_service,
    app_name="memory_assistant",
)


async def chat(user_input: str, user_id: str) -> str:
    session = await session_service.create_session(
        app_name="memory_assistant",
        user_id=user_id,
    )
    content = Content(role="user", parts=[Part(text=user_input)])
    async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
        if event.is_final_response() and event.content and event.content.parts:
            return event.content.parts[0].text
    return "No response generated"


if __name__ == "__main__":
    print(asyncio.run(chat(
        "I love Italian food and I'm planning a trip to Rome next month",
        user_id="alice",
    )))

    print(asyncio.run(chat(
        "Any food recommendations for my trip?",
        user_id="alice",
    )))

Multi-Agent Hierarchy with Shared Memory

Because memory_service is passed to the Runner, every agent in the hierarchy shares the same memory automatically. Only the root coordinator needs the auto-save callback: ADK fires it once when the full turn completes:

import asyncio
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools.agent_tool import AgentTool
from google.adk.tools import load_memory
from google.genai.types import Content, Part

from mem0_memory_service import Mem0MemoryService
from memory_callbacks import save_session_to_memory

memory_service = Mem0MemoryService()
session_service = InMemorySessionService()

travel_agent = LlmAgent(
    name="travel_specialist",
    model="gemini-2.0-flash",
    instruction="""You are a travel planning specialist.
    Relevant memories about the user's travel preferences are provided automatically.
    Use them to make personalized recommendations.""",
    description="Specialist in travel planning and recommendations",
    tools=[load_memory],
)

health_agent = LlmAgent(
    name="health_advisor",
    model="gemini-2.0-flash",
    instruction="""You are a health and wellness advisor.
    Relevant memories about the user's health goals are provided automatically.
    Use them to give personalized advice.""",
    description="Specialist in health and wellness advice",
    tools=[load_memory],
)

coordinator = LlmAgent(
    name="coordinator",
    model="gemini-2.0-flash",
    instruction="""You are a coordinator that delegates requests to specialist agents.
    For travel-related questions, delegate to the travel specialist.
    For health-related questions, delegate to the health advisor.
    Relevant memories about the user are provided automatically.""",
    description="Coordinates requests between specialist agents",
    tools=[
        load_memory,
        AgentTool(agent=travel_agent, skip_summarization=False),
        AgentTool(agent=health_agent, skip_summarization=False),
    ],
    after_agent_callback=save_session_to_memory,
)

runner = Runner(
    agent=coordinator,
    session_service=session_service,
    memory_service=memory_service,
    app_name="specialist_system",
)


async def chat_with_specialists(user_input: str, user_id: str) -> str:
    session = await session_service.create_session(
        app_name="specialist_system",
        user_id=user_id,
    )
    content = Content(role="user", parts=[Part(text=user_input)])
    async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
        if event.is_final_response() and event.content and event.content.parts:
            return event.content.parts[0].text
    return "No response generated"


if __name__ == "__main__":
    response = asyncio.run(chat_with_specialists("Plan a healthy meal for my Italy trip", user_id="alice"))
    print(response)

Key Features

Automatic Memory Injection: ADK’s built-in load_memory tool searches Mem0 at the start of each turn and injects relevant memories directly into the agent context. No prompt instructions are needed.
Automatic Session Saving: The save_session_to_memory callback persists every completed turn to Mem0 without any manual calls.
Native ADK Integration: Mem0MemoryService implements ADK’s BaseMemoryService and integrates via the Runner. It works natively across the entire agent hierarchy.
User Scoping: user_id is passed automatically from the ADK session context, ensuring memories are always scoped to the correct user.
Multi-Agent Support: A single Mem0MemoryService instance shared through the Runner gives all agents, coordinators and specialists, access to the same user memory.

Configuration Options

Using Vertex AI

To use Google Cloud Vertex AI instead of AI Studio, set the following environment variables before creating agents:

import os
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "True"
os.environ["GOOGLE_CLOUD_PROJECT"] = "your-project-id"
os.environ["GOOGLE_CLOUD_LOCATION"] = "us-central1"

Advanced Memory Filtering

You can customize how memories are searched by modifying Mem0MemoryService.search_memory. For example, to filter by category:

results = await asyncio.to_thread(
    self._client.search,
    query,
    filters={
        "AND": [
            {"user_id": user_id},
            {"app_id": app_name},
            {"categories": {"contains": "travel"}}
        ]
    },
    top_k=10,
)

InMemorySessionService stores sessions in memory and is intended for prototyping. For production, use a persistent session service and clean up sessions when they are no longer needed.

Conclusion

By implementing Mem0MemoryService as an ADK BaseMemoryService, you get persistent, user-scoped memory across single agents and complex multi-agent hierarchies with minimal code. Memory injection and session saving happen automatically, keeping your agent prompts clean and your token usage efficient.

Healthcare Agent Cookbook

Build HIPAA-compliant healthcare agents with Google ADK

OpenAI Agents SDK

Compare with OpenAI’s agent framework

Using Mem0? Star us on GitHub to help more developers discover memory for AI apps.

​Overview

​Setup and Configuration

​Implement Mem0MemoryService

​Add Auto-Save Callback

​Basic Integration Example

​Multi-Agent Hierarchy with Shared Memory

​Key Features

​Configuration Options

​Using Vertex AI

​Advanced Memory Filtering

​Conclusion

Healthcare Agent Cookbook

OpenAI Agents SDK

Overview

Setup and Configuration

Implement Mem0MemoryService

Add Auto-Save Callback

Basic Integration Example

Multi-Agent Hierarchy with Shared Memory

Key Features

Configuration Options

Using Vertex AI

Advanced Memory Filtering

Conclusion