[docs] Gemini-3 demo with mem0-mcp (#3810)
This commit is contained in:
Parth Sharma
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---
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title: "Gemini 3 with Mem0 MCP"
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description: "Create snappy, smart, memory-aware agents by pairing Gemini 3 with Mem0 MCP server."
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---
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Gemini 3, when paired with mem0-mcp-server, works in synergy to create snappy, smart, memory-aware agents.
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<Callout type="info" icon="sparkles" color="#8B5CF6">
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You'll build a Gemini 3 agent that automatically stores user preferences and retrieves relevant context without manual memory management.
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</Callout>
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## MCP Server Tools
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The Mem0 MCP server provides these tools to Gemini:
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| Tool | Description |
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| --------------------- | ---------------------------------------- |
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| `add_memory` | Store new information in memory |
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| `search_memories` | Find relevant memories |
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| `get_memories` | Retrieve specific memories by ID |
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| `get_memory` | Retrieve one memory by its `memory_id` |
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| `update_memory` | Modify existing memory content |
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| `delete_memory` | Remove specific memories |
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| `delete_all_memories` | Clear all memories for a user |
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| `delete_entities` | Delete all memories related to an entity |
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| `list_entities` | Enumerate users/agents/apps/runs stored |
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## Setup
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### Install dependencies
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```bash
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pip install pydantic-ai nest-asyncio python-dotenv uv google-genai
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```
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### Environment Setup
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Create a file named `.env`:
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```bash
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MEM0_API_KEY=m0-xxxxxxxxxxxxxxxxx
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GEMINI_API_KEY=your-gemini-api-key-here
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MEM0_DEFAULT_USER_ID=demo-user
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```
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<Note>
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Ensure you have your Mem0 API key from the [Mem0 Dashboard](https://app.mem0.ai) and your Gemini API key from the [Google AI Studio](https://ai.studio/app/api-keys).
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</Note>
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## Gemini Memory Agent
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This example shows how to create a memory-augmented agent using Gemini 3 through an agent loop.
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<Info icon="document">
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Save this as <strong>gemini_agent.py</strong>:
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</Info>
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```python
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import asyncio
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import os
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from dotenv import load_dotenv
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from pydantic_ai import Agent
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from pydantic_ai.mcp import MCPServerStdio
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# Load environment variables
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load_dotenv()
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class MemoryAgent:
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def __init__(self, model="gemini-3-pro-preview"):
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self.agent = None
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self.server = None
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self.model = model
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self._setup()
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def _setup(self):
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"""Initialize the agent with MCP tools"""
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# Create MCP server directly
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self.server = MCPServerStdio(
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command="uvx",
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args=["mem0-mcp-server"],
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env=os.environ
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)
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# Create agent with Gemini and memory tools
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self.agent = Agent(
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f"google-gla:{self.model}",
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toolsets=[self.server],
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system_prompt=(
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"You are an assistant with memory capabilities. "
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"Automatically remember important details about users, "
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"preferences, and facts. Search memories before answering "
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"questions about past information."
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),
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)
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print(f"Agent initialized with {self.model}")
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async def chat(self, message):
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"""Send message and get response"""
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async with self.server:
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result = await self.agent.run(message)
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return result.output
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async def interactive_chat(self):
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"""Run interactive chat session"""
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print("\nMemory Agent Ready! (Type 'quit' to exit)")
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while True:
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try:
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user_input = input("\nYou: ").strip()
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if user_input.lower() in ["quit", "exit"]:
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print("Goodbye!")
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break
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if not user_input:
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continue
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response = await self.chat(user_input)
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print(f"\nAgent: {response}")
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except KeyboardInterrupt:
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print("\nGoodbye!")
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break
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except Exception as e:
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print(f"\nError: {e}")
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async def main():
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"""Main entry point"""
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agent = MemoryAgent()
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await agent.interactive_chat()
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if __name__ == "__main__":
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asyncio.run(main())
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```
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## Running the Agent
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To run the interactive agent:
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```bash
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python gemini_agent.py
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```
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## Example Interactions
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### Multi-Tool Capabilities
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*Shows Gemini generating synthetic data while simultaneously storing and searching in one request*
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**Prompt:**
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```
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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.
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```
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**Response:**
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```
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I've created and stored 5 synthetic player profiles for your basketball team dashboard demo:
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- Marcus Chen (32, Point Guard) - 12.5 PPG, 8.2 APG, 45% FG, 3 seasons with team
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- Jamal Williams (28, Center) - 18.7 PPG, 10.5 RPG, 2.3 BPG, rookie season
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- Carlos Rodriguez (35, Small Forward) - 15.3 PPG, 6.8 RPG, 38% 3PT, team captain
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- Tito Kawaguchi (24, Shooting Guard) - 22.1 PPG, 4.1 APG, 42% 3PT, 6th man of the year candidate
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- Viktor Petrov (30, Power Forward) - 14.8 PPG, 9.2 RPG, 52% FG, defensive specialist
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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.
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```
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### Smart Query Generation
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*Demonstrates how Gemini transforms vague human input into optimal search queries*
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**Prompt:**
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```
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Anything about my health I should know about?
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```
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**Response:**
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```
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Based on your memory history, I searched for health-related information using these queries: "medical", "allergies", "dietary restrictions", "exercise", "wellness", "doctor visits", "health conditions".
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**Health Information Found:**
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- Allergies: Shellfish allergy
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- Dietary preferences: Weekday vegetarian, flexible weekends, dislikes kiwi
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- Exercise routine: 5km runs on weekday mornings
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- Diet: Oat milk cappuccino with one sugar (monitoring sugar intake recommended)
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I don't see any medical conditions or recent doctor visits in your stored information.
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```
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### Memory Attribution
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*Shows how Gemini distinguishes between stored memories and general knowledge*
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**Prompt:**
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```
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What's my coffee order again?
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```
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**Response:**
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```
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Based on your stored memories, your coffee order is an oat milk cappuccino with one sugar.
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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.
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```
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## Why Use Gemini with Mem0 MCP?
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### How Mem0 Enhances Your Application
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- **Smart Memory Management** - Organizes memories into searchable information *without setting up vector databases*
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- **Fast Retrieval** - Instant lookups with *sub-millisecond ping*, handles large datasets
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- **Graph Capabilities** - Builds knowledge *automatically* as you push information
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- **Simple Integration** - Uses Mem0 API in the backend, works with *any MCP client* with just a few lines of code
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### Gemini 3 + Mem0 Benefits
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- **Native function calling**: Built-in support for Mem0's memory tools
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- **Large context window**: Supports up to 1M tokens for extensive memory context
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- **Parallel execution**: Can call multiple memory tools simultaneously
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- **Cost-effective**: Competitive pricing for memory-intensive applications
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## What You Built
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- **Memory-augmented AI agent** - Gemini with persistent memory across sessions
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- **Automatic context management** - Agent automatically stores and retrieves relevant information
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- **Multi-tool parallel execution** - Simultaneous memory operations for efficiency
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- **Natural memory interface** - Users interact normally while agent manages memory behind the scenes
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## Conclusion
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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.
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## Next Steps
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<CardGroup cols={2}>
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<Card
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title="Advanced Memory Patterns"
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description="Learn about memory filtering, entities, and advanced search techniques"
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icon="brain"
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href="/platform/features/v2-memory-filters"
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/>
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<Card
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title="MCP Integration Guide"
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description="Explore deeper integration with Model Context Protocol"
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icon="plug"
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href="/platform/integrations/mcp"
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/>
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</CardGroup>
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+2
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"cookbooks/frameworks/llamaindex-multiagent",
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"cookbooks/frameworks/multimodal-retrieval",
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"cookbooks/frameworks/eliza-os-character",
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"cookbooks/frameworks/chrome-extension"
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"cookbooks/frameworks/chrome-extension",
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"cookbooks/frameworks/gemini-3-with-mem0-mcp"
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]
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}
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]
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