docs: navigation (#5900)
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Kartik
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---
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title: "Graph Memory"
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description: "Mem0 Platform builds a native graph linking people, places, and concepts across your memories, with no external graph database to provision."
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icon: "circle-nodes"
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iconType: "solid"
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---
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Mem0 Platform automatically organizes your memories into a **graph**: the **graph entities** mentioned across your memories (the people, places, organizations, and concepts they refer to) become nodes, and memories that share an entity are connected. This is how Mem0 reasons across separate facts, for example linking everything it knows about a person, a company, or a project, without you defining any schema.
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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, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
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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's cloud MCP handles everything
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- **Standardized protocol**: One integration works across all your AI tools
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## Setup
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Add Mem0 MCP to all supported clients with a single command:
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "claude,claude code,cursor,windsurf,vscode,opencode"
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```
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Or configure a specific client:
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```bash
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npx mcp-add \
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--name mem0-mcp \
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--type http \
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--url "https://mcp.mem0.ai/mcp" \
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--clients "cursor"
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```
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For manual configuration, add this to your MCP client config:
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```json
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{
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"mcpServers": {
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"mem0-mcp": {
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"type": "http",
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"url": "https://mcp.mem0.ai/mcp"
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}
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}
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}
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```
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For detailed per-client instructions, see the [Mem0 MCP Quickstart](/platform/mem0-mcp).
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## Available tools
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The MCP server exposes 11 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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| `list_events` | List memory operation events with filters and pagination |
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| `get_event_status` | Check the status of an async memory operation by `event_id` |
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## How it works
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1. **Configure the MCP server** - Add Mem0 MCP to your AI client using the setup command above
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2. **Agent connects** - Your AI client connects to Mem0's cloud MCP server over HTTP
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3. **Autonomous memory** - The agent decides when to store/retrieve memories as part of its reasoning
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4. **No manual API calls** - The agent manages memory automatically through MCP tools
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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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- 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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- **Use the cloud MCP**: The hosted MCP server at `https://mcp.mem0.ai/mcp` handles infrastructure for you
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- **Use wildcards**: `user_id: "*"` to search across all users
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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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<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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@@ -1,59 +0,0 @@
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---
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title: Overview
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description: "See how Mem0 Platform features evolve from baseline filters to advanced retrieval."
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icon: "list"
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---
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Mem0 Platform features help managed deployments scale from basic filtering to advanced retrieval and data governance. Use this page to pick the right feature lane for your team.
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<Info>
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New to the platform? Start with the <Link href="/platform/quickstart">Platform quickstart</Link>,
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then dive into the journeys below.
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</Info>
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## Choose your path
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<CardGroup cols={3}>
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<Card title="Apply Essential Filters" icon="rocket" href="/platform/features/v2-memory-filters">
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Field-level filtering with async defaults.
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</Card>
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<Card title="Go Real-Time with Async" icon="bolt" href="/platform/features/async-client">
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Non-blocking add/search requests for agents.
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</Card>
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<Card
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title="Boost Retrieval Quality"
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icon="sparkles"
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href="/platform/features/advanced-retrieval"
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>
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Metadata filters, rerankers, and toggles.
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</Card>
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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="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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<Tip>
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Self-hosting instead? Jump to the{" "}
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<Link href="/open-source/features/overview">OSS feature overview</Link> for equivalent
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capabilities.
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</Tip>
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## Keep going
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<CardGroup cols={2}>
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<Card
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title="Compare with Open Source"
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description="See how managed features map to the OSS stack."
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icon="server"
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href="/platform/platform-vs-oss"
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/>
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<Card
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title="Run the Quickstart"
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description="Provision the workspace and ship your first advanced search."
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icon="rocket"
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href="/platform/quickstart"
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/>
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</CardGroup>
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@@ -1,7 +1,6 @@
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---
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title: Temporal Reasoning
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description: "Time-aware memory retrieval for Mem0 Platform v3 so queries like 'last week', 'upcoming', and 'right now' return the right memories."
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icon: "clock"
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badge: "v3"
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---
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