---
title: "Gemini 3 with Mem0 MCP"
description: "Create snappy, smart, memory-aware agents by pairing Gemini 3 with Mem0 MCP server."
---
**Works with:** Mem0 Platform (MCP server)
Gemini 3, when paired with Mem0's cloud MCP server, works in synergy to create snappy, smart, memory-aware agents.
This is the primary example of MCP integration - the same patterns work with Claude Desktop, Cursor, or any MCP-compatible client.
## MCP Server Tools
The Mem0 MCP server provides these tools to Gemini:
| Tool | Description |
| --------------------- | ---------------------------------------- |
| `add_memory` | Store new information in memory |
| `search_memories` | Find relevant memories |
| `get_memories` | Retrieve specific memories by ID |
| `get_memory` | Retrieve one memory by its `memory_id` |
| `update_memory` | Modify existing memory content |
| `delete_memory` | Remove specific memories |
| `delete_all_memories` | Clear all memories for a user |
| `delete_entities` | Delete all memories related to an entity |
| `list_entities` | Enumerate users/agents/apps/runs stored |
## Setup
### Configure Mem0 MCP
Add Mem0 MCP to your MCP client:
```bash
npx mcp-add \
--name mem0-mcp \
--type http \
--url "https://mcp.mem0.ai/mcp" \
--clients "claude,claude code,cursor,windsurf,vscode,opencode"
```
### Install dependencies
```bash
pip install pydantic-ai nest-asyncio python-dotenv google-genai
```
### Environment Setup
Create a file named `.env`:
```bash
MEM0_API_KEY=m0-xxxxxxxxxxxxxxxxx
GEMINI_API_KEY=your-gemini-api-key-here
```
Ensure you have your Mem0 API key from the Mem0 Dashboard and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
## Gemini Memory Agent
This example shows how to create a memory-augmented agent using Gemini 3 through an agent loop.
Save this as gemini_agent.py:
```python
import asyncio
import os
from dotenv import load_dotenv
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
# Load environment variables
load_dotenv()
class MemoryAgent:
def __init__(self, model="gemini-3-pro-preview"):
self.agent = None
self.server = None
self.model = model
self._setup()
def _setup(self):
"""Initialize the agent with MCP tools"""
# Connect to Mem0's cloud MCP server
self.server = MCPServerHTTP(
url="https://mcp.mem0.ai/mcp"
)
# Create agent with Gemini and memory tools
self.agent = Agent(
f"google-gla:{self.model}",
toolsets=[self.server],
system_prompt=(
"You are an assistant with memory capabilities. "
"Automatically remember important details about users, "
"preferences, and facts. Search memories before answering "
"questions about past information."
),
)
print(f"Agent initialized with {self.model}")
async def chat(self, message):
"""Send message and get response"""
async with self.server:
result = await self.agent.run(message)
return result.output
async def interactive_chat(self):
"""Run interactive chat session"""
print("\nMemory Agent Ready! (Type 'quit' to exit)")
while True:
try:
user_input = input("\nYou: ").strip()
if user_input.lower() in ["quit", "exit"]:
print("Goodbye!")
break
if not user_input:
continue
response = await self.chat(user_input)
print(f"\nAgent: {response}")
except KeyboardInterrupt:
print("\nGoodbye!")
break
except Exception as e:
print(f"\nError: {e}")
async def main():
"""Main entry point"""
agent = MemoryAgent()
await agent.interactive_chat()
if __name__ == "__main__":
asyncio.run(main())
```
## Running the Agent
To run the interactive agent:
```bash
python gemini_agent.py
```
## Example Interactions
### Multi-Tool Capabilities
*Shows Gemini generating synthetic data while simultaneously storing and searching in one request*
**Prompt:**
```
I need player statistics for a basketball team dashboard demo. Generate 5 synthetic player profiles with different positions, ages, and performance metrics, then store all of them. Also check if we have any existing player data in memory.
```
**Response:**
```
I've created and stored 5 synthetic player profiles for your basketball team dashboard demo:
- Marcus Chen (32, Point Guard) - 12.5 PPG, 8.2 APG, 45% FG, 3 seasons with team
- Jamal Williams (28, Center) - 18.7 PPG, 10.5 RPG, 2.3 BPG, rookie season
- Carlos Rodriguez (35, Small Forward) - 15.3 PPG, 6.8 RPG, 38% 3PT, team captain
- Tito Kawaguchi (24, Shooting Guard) - 22.1 PPG, 4.1 APG, 42% 3PT, 6th man of the year candidate
- Viktor Petrov (30, Power Forward) - 14.8 PPG, 9.2 RPG, 52% FG, defensive specialist
I searched for existing player data in your memory but found none. All synthetic player profiles have been stored with appropriate basketball statistics for demo purposes.
```
### Smart Query Generation
*Demonstrates how Gemini transforms vague human input into optimal search queries*
**Prompt:**
```
Anything about my health I should know about?
```
**Response:**
```
Based on your memory history, I searched for health-related information using these queries: "medical", "allergies", "dietary restrictions", "exercise", "wellness", "doctor visits", "health conditions".
**Health Information Found:**
- Allergies: Shellfish allergy
- Dietary preferences: Weekday vegetarian, flexible weekends, dislikes kiwi
- Exercise routine: 5km runs on weekday mornings
- Diet: Oat milk cappuccino with one sugar (monitoring sugar intake recommended)
I don't see any medical conditions or recent doctor visits in your stored information.
```
### Memory Attribution
*Shows how Gemini distinguishes between stored memories and general knowledge*
**Prompt:**
```
What's my coffee order again?
```
**Response:**
```
Based on your stored memories, your coffee order is an oat milk cappuccino with one sugar.
This information was retrieved from your memory history where you previously mentioned this preference. I don't have any information about whether you've changed this preference recently.
```
## Why Use Gemini with Mem0 MCP?
### How Mem0 Enhances Your Application
- **Smart Memory Management** - Organizes memories into searchable information *without setting up vector databases*
- **Fast Retrieval** - Instant lookups with *sub-millisecond ping*, handles large datasets
- **Simple Integration** - Uses Mem0 API in the backend, works with *any MCP client* with just a few lines of code
### Gemini 3 + Mem0 Benefits
- **Native function calling**: Built-in support for Mem0's memory tools
- **Large context window**: Supports up to 1M tokens for extensive memory context
- **Parallel execution**: Can call multiple memory tools simultaneously
- **Cost-effective**: Competitive pricing for memory-intensive applications
## What You Built
- **Memory-augmented AI agent** - Gemini with persistent memory across sessions
- **Automatic context management** - Agent automatically stores and retrieves relevant information
- **Multi-tool parallel execution** - Simultaneous memory operations for efficiency
- **Natural memory interface** - Users interact normally while agent manages memory behind the scenes
## Conclusion
You've successfully built a Gemini 3 agent with persistent memory using Mem0's MCP server. The agent can now remember user preferences, maintain context across sessions, and provide more personalized interactions.
## Next Steps