[docs] Series of docs for mem0-mcp (#3831)
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Parth Sharma
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---
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title: MCP Integration
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description: "Connect any AI client to Mem0 using Model Context Protocol for universal memory access"
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---
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> Model Context Protocol (MCP) provides a standardized way for AI agents to manage their own memory through Mem0, without manual API calls.
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## Why use MCP
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When building AI applications, memory management often requires manual integration. MCP eliminates this complexity by:
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- **Universal compatibility**: Works with any MCP-compatible client (Claude Desktop, Cursor, custom agents)
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- **Agent autonomy**: AI agents decide when to save, search, or update memories
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- **Zero infrastructure**: No servers to maintain - Mem0 handles everything
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- **Standardized protocol**: One integration works across all your AI tools
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## Available tools
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The MCP server exposes 9 memory tools to your AI client:
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| Tool | Purpose |
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|------|---------|
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| `add_memory` | Store conversations or facts |
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| `search_memories` | Find relevant memories with filters |
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| `get_memories` | List memories with pagination |
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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` | Bulk delete memories |
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| `delete_entities` | Remove user/agent/app entities |
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| `get_memory` | Retrieve single memory by ID |
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| `list_entities` | View stored entities |
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## Deployment options
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Choose the deployment method that fits your workflow:
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<AccordionGroup>
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<Accordion title="Python package (recommended)">
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Install and run locally with uvx:
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```bash
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uv pip install mem0-mcp-server
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```
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Configure your client:
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```json
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{
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"mcpServers": {
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"mem0": {
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"command": "uvx",
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"args": ["mem0-mcp-server"],
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"env": {
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"MEM0_API_KEY": "m0-...",
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"MEM0_DEFAULT_USER_ID": "your-handle"
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}
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}
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}
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}
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```
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</Accordion>
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<Accordion title="Docker container">
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Containerized deployment with HTTP endpoint:
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```bash
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docker build -t mem0-mcp-server https://github.com/mem0ai/mem0-mcp.git
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docker run --rm -d -e MEM0_API_KEY="m0-..." -p 8080:8081 mem0-mcp-server
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```
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Configure for HTTP:
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```json
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{
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"mcpServers": {
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"mem0-docker": {
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"command": "curl",
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"args": ["-X", "POST", "http://localhost:8080/mcp", "--data-binary", "@"],
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"env": {
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"MEM0_API_KEY": "m0-..."
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}
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}
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}
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}
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```
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</Accordion>
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<Accordion title="Smithery">
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One-click setup with managed service:
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Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) and:
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1. Select your AI client (Cursor, Claude Desktop, etc.)
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2. Configure your Mem0 API key
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3. Set your default user ID
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4. Enable graph memory (optional)
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5. Copy the generated configuration
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Your client connects automatically - no installation required.
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</Accordion>
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</AccordionGroup>
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## Configuration
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### Required environment variables
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```bash
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MEM0_API_KEY="m0-..." # Your Mem0 API key
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MEM0_DEFAULT_USER_ID="your-handle" # Default user ID
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```
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### Optional variables
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```bash
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MEM0_ENABLE_GRAPH_DEFAULT="true" # Enable graph memories
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MEM0_MCP_AGENT_MODEL="gpt-4o-mini" # LLM for bundled examples
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```
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<AccordionGroup>
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<Accordion title="Test your setup with the Python agent">
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The included Pydantic AI agent provides an interactive REPL to test memory operations:
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```bash
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# Install the package
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pip install mem0-mcp-server
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# Set your API keys
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export MEM0_API_KEY="m0-..."
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export OPENAI_API_KEY="sk-openai-..."
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# Clone and test with the agent
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git clone https://github.com/mem0ai/mem0-mcp.git
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cd mem0-mcp-server
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python example/pydantic_ai_repl.py
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```
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**Testing different server configurations:**
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- **Local server** (default): `python example/pydantic_ai_repl.py`
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- **Docker container**:
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```bash
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export MEM0_MCP_CONFIG_PATH=example/docker-config.json
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export MEM0_MCP_CONFIG_SERVER=mem0-docker
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python example/pydantic_ai_repl.py
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```
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- **Smithery remote**:
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```bash
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export MEM0_MCP_CONFIG_PATH=example/config-smithery.json
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export MEM0_MCP_CONFIG_SERVER=mem0-memory-mcp
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python example/pydantic_ai_repl.py
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```
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Try these test prompts:
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- "Remember that I love tiramisu"
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- "Search for my food preferences"
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- "Update my project: the mobile app is now 80% complete"
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- "Show me all memories about project Phoenix"
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- "Delete memories from 2023"
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</Accordion>
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</AccordionGroup>
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## How the testing works
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1. **Configuration loads** - Reads from `example/config.json` by default
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2. **Server starts** - Launches or connects to the Mem0 MCP server
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3. **Agent connects** - Pydantic AI agent (Mem0Guide) attaches to the server
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4. **Interactive REPL** - You get a chat interface to test all memory operations
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## Example interactions
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Once connected, your AI agent can:
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```
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User: Remember that I'm allergic to peanuts
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Agent: [calls add_memory] Got it! I've saved your peanut allergy.
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User: What dietary restrictions do I know about?
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Agent: [calls search_memories] You have a peanut allergy.
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```
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The agent automatically decides when to use memory tools based on context.
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## Try these prompts
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```python
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# Multi-task operations
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"Generate 5 user personas for our e-commerce app with different demographics, store them all, then search for existing personas"
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# Natural context retrieval
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"Anything about my work preferences I should remember?"
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# Complex information updates
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"Update my current project: the mobile app is now 80% complete, we've fixed the login issues, and the launch date is March 15"
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# Time-based queries
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"What meetings did I have last week about Project Phoenix?"
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# Memory cleanup
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"Delete all test data and temporary memories from our development phase"
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# Personal preferences
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"I drink oat milk cappuccino with one sugar every morning, and I prefer standing desks"
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# Health and wellness tracking
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"I'm allergic to peanuts and shellfish, and I go for 5km runs on weekday mornings"
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```
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These examples demonstrate how MCP enables natural language memory operations - the AI agent automatically determines when to add, search, update, or delete memories based on context.
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## What you can do
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The Mem0 MCP server enables powerful memory capabilities for your AI applications:
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- **Health tracking**: "I'm allergic to peanuts and shellfish" - Add new health information
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- **Research data**: "Store these trial parameters: 200 participants, double-blind, placebo-controlled" - Save structured data
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- **Preference queries**: "What do you know about my dietary preferences?" - Search and retrieve relevant memories
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- **Project updates**: "Update my project status: the mobile app is now 80% complete" - Modify existing memory
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- **Data cleanup**: "Delete all memories from 2023" - Bulk remove outdated information
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- **Topic overview**: "Show me everything about Project Phoenix" - List all memories for a subject
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## Performance tips
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- Enable graph memories for relationship-aware recall
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- Use specific filters when searching large memory sets
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- Batch operations when adding multiple memories
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- Monitor memory usage in the Mem0 dashboard
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## Best practices
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- **Start simple**: Use the Python package for development
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- **Use wildcards**: `user_id: "*"` to search across all users
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- **Test locally**: Use the bundled Python agent to verify setup
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- **Monitor usage**: Track memory operations in the dashboard
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- **Document patterns**: Share successful prompt patterns with your team
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{/* DEBUG: verify CTA targets */}
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<CardGroup cols={2}>
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<Card
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title="Memory Filters"
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description="Refine memory retrieval with powerful filtering capabilities"
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icon="scale-balanced"
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href="/platform/features/v2-memory-filters"
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/>
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<Card
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title="Gemini 3 with MCP"
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description="See MCP in action with Google's Gemini 3 model"
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icon="book-open"
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href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
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/>
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</CardGroup>
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@@ -33,8 +33,8 @@ Mem0 Platform features help managed deployments scale from basic filtering to gr
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<Card title="Manage Data Lifecycle" icon="database" href="/platform/features/direct-import">
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Imports, exports, timestamps, and expirations.
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</Card>
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<Card title="Extend With Integrations" icon="plug" href="/platform/features/webhooks">
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Webhooks, feedback loops, and multi-agent chat.
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<Card title="Connect Any AI Client" icon="puzzle-piece" href="/platform/mem0-mcp">
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Universal memory integration via MCP.
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</Card>
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</CardGroup>
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