[docs] Series of docs for mem0-mcp (#3831)
This commit is contained in:
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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@@ -0,0 +1,232 @@
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---
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title: "Mem0 MCP"
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description: "Connect any AI client to Mem0 using Model Context Protocol in minutes"
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icon: "puzzle-piece"
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estimatedTime: "~5 minutes"
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---
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<Info>
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**Prerequisites**
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- Mem0 Platform account ([Sign up here](https://app.mem0.ai))
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- API key ([Get one from dashboard](https://app.mem0.ai/settings/api-keys))
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- Python 3.10+, Docker, or Node.js 14+
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- An MCP-compatible client (Claude Desktop, Cursor, or custom agent)
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</Info>
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## What is Mem0 MCP?
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Mem0 MCP Server exposes Mem0's memory capabilities as MCP tools, letting AI agents decide when to save, search, or update information.
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## Deployment Options
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Choose from three deployment methods:
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1. **Python Package (Recommended)** - Install locally with `uvx` for instant setup
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2. **Docker Container** - Isolated deployment with HTTP endpoint
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3. **Smithery** - Remote hosted service for managed deployments
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## Available Tools
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The MCP server exposes these memory tools to your AI client:
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| Tool | Description |
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|------|-------------|
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| `add_memory` | Save text or conversation history for a user/agent |
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| `search_memories` | Semantic search across existing memories with filters |
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| `get_memories` | List memories with structured filters and pagination |
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| `get_memory` | Retrieve one memory by its `memory_id` |
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| `update_memory` | Overwrite a memory's text after confirming the ID |
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| `delete_memory` | Delete a single memory by `memory_id` |
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| `delete_all_memories` | Bulk delete all memories in scope |
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| `delete_entities` | Delete a user/agent/app/run entity and its memories |
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| `list_entities` | Enumerate users/agents/apps/runs stored in Mem0 |
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---
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## Quickstart with Python (UVX)
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<Steps>
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<Step title="Install the MCP Server">
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```bash
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uv pip install mem0-mcp-server
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```
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</Step>
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<Step title="Configure your MCP client">
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Add this to your MCP client (e.g., Claude Desktop):
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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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Set your environment variables:
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```bash
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export MEM0_API_KEY="m0-..."
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export MEM0_DEFAULT_USER_ID="your-handle"
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```
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</Step>
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<Step title="Test with the Python agent">
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```bash
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# Clone the mem0-mcp repository
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git clone https://github.com/mem0ai/mem0-mcp.git
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cd mem0-mcp
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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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# Run the interactive agent
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python example/pydantic_ai_repl.py
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```
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**Sample Interactions:**
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```
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User: Remember that I love tiramisu
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Agent: Got it! I've saved that you love tiramisu.
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User: What do you know about my food preferences?
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Agent: Based on your memories, you love tiramisu.
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User: Update my project: the mobile app is now 80% complete
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Agent: Updated your project status successfully.
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```
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</Step>
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<Step title="Verify the setup">
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Your AI client can now:
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- Automatically save information with `add_memory`
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- Search memories with `search_memories`
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- Update memories with `update_memory`
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- Delete memories with `delete_memory`
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<Info icon="check">
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If you get "Connection failed", ensure your API key is valid and the server is running.
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</Info>
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</Step>
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</Steps>
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---
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## Quickstart with Docker
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<Steps>
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<Step title="Build the Docker image">
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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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```
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</Step>
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<Step title="Run the container">
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```bash
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docker run --rm -d \
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--name mem0-mcp \
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-e MEM0_API_KEY="m0-..." \
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-p 8080:8081 \
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mem0-mcp-server
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```
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</Step>
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<Step title="Configure your client for HTTP">
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For clients that connect via HTTP (instead of stdio):
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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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</Step>
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<Step title="Verify the setup">
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```bash
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# Check container logs
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docker logs mem0-mcp
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# Test HTTP endpoint
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curl http://localhost:8080/health
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```
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<Info icon="check">
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The container should start successfully and respond to HTTP requests. If port 8080 is occupied, change it with `-p 8081:8081`.
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</Info>
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</Step>
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</Steps>
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||||
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||||
---
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||||
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## Quickstart with Smithery (Hosted)
|
||||
|
||||
For the simplest integration, use Smithery's hosted Mem0 MCP server - no installation required.
|
||||
|
||||
**Example: One-click setup in Cursor**
|
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|
||||
1. Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) and select Cursor as your client
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||||
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||||
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2. Open Cursor → Settings → MCP
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3. Click `mem0-mcp` → Initiate authorization
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4. Configure Smithery with your environment:
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- `MEM0_API_KEY`: Your Mem0 API key
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- `MEM0_DEFAULT_USER_ID`: Your user ID
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||||
- `MEM0_ENABLE_GRAPH_DEFAULT`: Optional, set to `true` for graph memories
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||||
5. Return to Cursor settings and wait for tools to load
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6. Start chatting with Cursor and begin storing preferences
|
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||||
**For other clients:**
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||||
Visit [smithery.ai/server/@mem0ai/mem0-memory-mcp](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) to connect any MCP-compatible client with your Mem0 credentials.
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||||
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||||
---
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||||
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## Quick Recovery
|
||||
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||||
- **"uvx command not found"** → Install with `pip install uv` or use `pip install mem0-mcp-server` instead. Make sure your Python environment has `uv` installed (or system-wide).
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- **"Connection refused"** → Check that the server is running and the correct port is configured
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- **"Invalid API key"** → Get a new key from [Mem0 Dashboard](https://app.mem0.ai/settings/api-keys)
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- **"Permission denied"** → Ensure Docker has access to bind ports (try with `sudo` on Linux)
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||||
---
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||||
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||||
## Next Steps
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="MCP Integration Feature"
|
||||
description="Learn about MCP configuration options and advanced patterns"
|
||||
icon="plug"
|
||||
href="/platform/features/mcp-integration"
|
||||
/>
|
||||
<Card
|
||||
title="Gemini 3 with Mem0 MCP"
|
||||
description="See how to integrate Gemini 3 with Mem0 MCP server"
|
||||
icon="book-open"
|
||||
href="/cookbooks/frameworks/gemini-3-with-mem0-mcp"
|
||||
/>
|
||||
</CardGroup>
|
||||
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||||
## Additional Resources
|
||||
|
||||
- **[Mem0 MCP Repository](https://github.com/mem0ai/mem0-mcp)** - Source code and examples
|
||||
- **[Platform Quickstart](/platform/quickstart)** - Direct API integration guide
|
||||
- **[MCP Specification](https://modelcontextprotocol.io)** - Learn about MCP protocol
|
||||
@@ -36,10 +36,13 @@ Mem0 is the memory engine that keeps conversations contextual so users never rep
|
||||
|
||||
## Choose your path
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<CardGroup cols={3}>
|
||||
<Card title="Launch Your Workspace" icon="rocket" href="/platform/quickstart">
|
||||
Create project and ship first memory.
|
||||
</Card>
|
||||
<Card title="Connect Any AI Client" icon="puzzle-piece" href="/platform/mem0-mcp">
|
||||
Use MCP for universal AI integration.
|
||||
</Card>
|
||||
<Card title="Understand Memory Types" icon="brain" href="/core-concepts/memory-types">
|
||||
User, agent, and session memory behavior.
|
||||
</Card>
|
||||
|
||||
@@ -127,6 +127,10 @@ curl -X POST https://api.mem0.ai/v1/memories/search \
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<Callout type="tip" icon="plug">
|
||||
**Pro Tip**: Want AI agents to manage their own memory automatically? Use <Link href="/platform/mem0-mcp">Mem0 MCP</Link> to let LLMs decide when to save, search, and update memories.
|
||||
</Callout>
|
||||
|
||||
## What's Next?
|
||||
|
||||
<CardGroup cols={3}>
|
||||
|
||||
Reference in New Issue
Block a user