docs: improve SEO metadata across docs pages
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mintlify[bot]
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title: AgentOps
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description: "Integrate Mem0 with AgentOps for automatic monitoring, analytics, and real-time tracking of memory operations."
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description: "Monitor Mem0 memory operations with AgentOps to get automatic tracking, analytics dashboards, and real-time insights into AI agent performance."
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---
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Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.
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---
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title: Agno
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description: "Add persistent multimodal memory to Agno-based agents using Mem0 for text and image interactions."
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description: "Add persistent multimodal memory to Agno agents with Mem0 to store text and image interactions, personalize responses, and keep context across chats."
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---
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This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno) enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
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---
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title: Antigravity
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description: "Add persistent memory to Google Antigravity with the Mem0 plugin: MCP server, lifecycle hooks, and slash commands."
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description: "Add persistent memory to Google Antigravity with the Mem0 plugin, featuring an MCP server, lifecycle hooks, and slash commands for the agy CLI and IDE."
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---
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Add persistent memory to [**Google Antigravity**](https://antigravity.google) (`agy` CLI and Desktop IDE) with the Mem0 plugin. Your agent forgets everything between sessions. Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
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---
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title: AutoGen
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description: "Build conversational AI agents with AutoGen and Mem0 for context-aware, personalized interactions."
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description: "Build conversational AI agents with AutoGen and Mem0 to create memory-enabled chatbots that recall past interactions for personalized customer service."
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---
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Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
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---
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title: AWS Bedrock
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description: "Use Mem0 with AWS Bedrock and OpenSearch Service for cloud-native persistent semantic memory storage."
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description: "Use Mem0 with AWS Bedrock and Amazon OpenSearch Service to enable cloud-native semantic memory storage, embeddings, and LLM calls for intelligent agents."
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This integration demonstrates how to use **Mem0** with **AWS Bedrock** and **Amazon OpenSearch Service (AOSS)** to enable persistent, semantic memory in intelligent agents.
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---
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title: Camel AI
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description: "Plug Mem0 cloud memory into Camel's agents with the built‑in Mem0Storage."
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description: "Plug Mem0 cloud memory into Camel AI agents using the built-in Mem0Storage backend to persist and recall conversation context across multi-agent sessions."
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partnerBadge: "Camel AI"
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title: ChatDev
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description: "Add persistent, cloud-managed memory to ChatDev multi-agent workflows with Mem0: no code required, just YAML configuration."
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description: "Add persistent cloud memory to ChatDev multi-agent workflows with Mem0 as a built-in memory store, configured entirely in YAML with no code required."
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Build multi-agent workflows in [ChatDev](https://github.com/OpenBMB/ChatDev) with persistent memory powered by Mem0. ChatDev is a zero-code multi-agent platform where agents, tools, and workflows are defined entirely in YAML. Mem0 integrates as a built-in memory store (`type: mem0`), giving your agents cloud-managed semantic search and cross-session persistence, all without writing any code.
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---
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title: Claude Code
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description: "Add persistent memory to Claude Code and Claude Cowork with the Mem0 plugin: MCP server, lifecycle hooks, and SDK skill."
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description: "Add persistent memory to Claude Code CLI and Claude Cowork desktop app with the Mem0 plugin: MCP server, lifecycle hooks, and an SDK skill for recall."
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---
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Add persistent memory to [**Claude Code**](https://docs.anthropic.com/en/docs/claude-code) (CLI) and **Claude Cowork** (desktop app) with the Mem0 plugin. Your agent forgets everything between sessions. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context before every response.
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---
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title: Codex
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description: "Add persistent memory to OpenAI Codex with the Mem0 plugin: MCP server, lifecycle hooks, and SDK skill."
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description: "Add persistent memory to OpenAI Codex with the Mem0 plugin featuring an MCP server, lifecycle hooks, and SDK skill so Codex remembers tasks and context."
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---
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Add persistent memory to [**OpenAI Codex**](https://openai.com/index/codex/) with the Mem0 plugin. Codex forgets everything between tasks. This plugin fixes that by connecting to Mem0's cloud memory layer via MCP, automatically capturing learnings at key lifecycle points, and retrieving relevant context before every response.
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---
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title: CrewAI
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description: "Combine CrewAI agent-based architecture with Mem0 for persistent memory across agent interactions."
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description: "Combine CrewAI multi-agent orchestration with Mem0 for persistent memory across agent interactions, enabling personalized task execution and user history."
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---
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Build an AI system that combines CrewAI's agent-based architecture with Mem0's memory capabilities. This integration enables persistent memory across agent interactions and personalized task execution based on user history.
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---
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title: Cursor
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description: "Add persistent memory to Cursor with the Mem0 MCP server for context-aware coding."
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description: "Add persistent memory to Cursor with the Mem0 MCP server so your AI coding assistant remembers project context, decisions, and preferences across sessions."
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---
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Add persistent memory to [**Cursor**](https://cursor.com) with the Mem0 MCP server. Your AI assistant forgets everything between sessions. Mem0 fixes that by connecting Cursor to Mem0's cloud memory layer via MCP so you can save and retrieve relevant context during coding sessions.
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title: Dify
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description: "Integrate Mem0 as a plugin in Dify AI workflows for persistent conversation storage and retrieval."
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description: "Integrate Mem0 as a plugin in Dify AI workflows for persistent conversation storage, semantic recall, and context-aware chatbots with no custom code."
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---
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# Integrating Mem0 with Dify AI
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---
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title: ElevenLabs
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description: "Create voice-based conversational AI agents with ElevenLabs and Mem0 for context-aware voice interactions."
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description: "Build voice-based conversational AI agents with ElevenLabs and Mem0 for context-aware voice interactions that remember users across phone calls and chats."
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---
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Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables persistent, context-aware voice interactions that remember past conversations.
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title: Flowise
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description: "Add persistent Mem0 memory to Flowise chatflows for context-aware conversations in the low-code builder."
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description: "Add persistent Mem0 memory to Flowise chatflows for context-aware conversations in the drag-and-drop low-code LLM builder with no manual code required."
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---
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The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.
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title: Google ADK
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description: "Integrate Mem0 with Google Agent Development Kit for persistent memory across multi-agent workflows."
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description: "Integrate Mem0 with Google Agent Development Kit (ADK) for persistent memory across multi-agent workflows, boosting context retention and personalization."
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---
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Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google ADK (Agent Development Kit)](https://github.com/google/adk-python), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
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title: Langchain Tools
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description: 'Integrate Mem0 with LangChain tools to enable AI agents to store, search, and manage memories through structured interfaces'
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description: 'Use Mem0 LangChain tools to let AI agents store, search, and manage memories through structured interfaces for context-aware retrieval and personalization'
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## Overview
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title: Langchain
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description: "Build personalized AI agents using LangChain for conversation flow and Mem0 for long-term memory retention."
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description: "Build personalized AI agents using LangChain for conversation flow and Mem0 for long-term memory retention, ideal for travel planners and smart assistants."
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Build a personalized Travel Agent AI using LangChain for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient travel planning experiences.
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title: LangGraph
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description: "Build customer support agents using LangGraph for conversation flow and Mem0 for personalized responses."
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description: "Build customer support agents using LangGraph for stateful conversation flow and Mem0 for long-term memory to deliver personalized responses."
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---
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Build a personalized Customer Support AI Agent using LangGraph for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient support experiences.
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title: Livekit
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description: "Build memory-enabled voice assistants with LiveKit, Deepgram, OpenAI, and Mem0 for context-aware conversations."
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description: "Build memory-enabled voice assistants with LiveKit, Deepgram, OpenAI, and Mem0 for context-aware voice conversations and intelligent real-time agents."
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---
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This guide demonstrates how to create a memory-enabled voice assistant using LiveKit, Deepgram, OpenAI, and Mem0, focusing on creating an intelligent, context-aware travel planning agent.
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---
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title: LlamaIndex
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description: "Use Mem0 as a memory store in LlamaIndex with support for ReAct and FunctionCalling agent types."
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description: "Use Mem0 as a memory store in LlamaIndex with support for ReAct and FunctionCalling agent types to add persistent recall and personalized context."
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---
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LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-index-memory-mem0). In this guide, we'll show you how to use it.
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title: Mastra
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description: "Use Mastra agents with Mem0 as the memory backend for cross-conversation storage and retrieval."
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description: "Use Mastra agents with Mem0 as the memory backend through custom tools for cross-conversation storage, retrieval, and personalized agent responses."
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---
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The [**Mastra**](https://mastra.ai/) integration demonstrates how to use Mastra's agent system with Mem0 as the memory backend through custom tools. This enables agents to remember and recall information across conversations.
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title: n8n
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description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node, no code required."
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description: "Add long-term memory to n8n workflows and AI Agents with the Mem0 community node to store, search, and recall durable facts across runs, no code required."
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---
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Your n8n workflows start from zero on every run. The [`@mem0/n8n-nodes-mem0`](https://www.npmjs.com/package/@mem0/n8n-nodes-mem0) community node fixes that: store durable facts as memories, recall them in any later run, and hand the node to an [n8n AI Agent](https://docs.n8n.io/advanced-ai/) as a tool so it can remember and recall on its own.
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title: OpenAI Agents SDK
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description: "Integrate Mem0 with the OpenAI Agents SDK for persistent memory across multi-agent workflows."
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description: "Integrate Mem0 with the OpenAI Agents SDK for persistent memory across multi-agent workflows, adding context retention and personalization to your agents."
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---
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Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), a lightweight framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
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title: OpenClaw
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description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with skills-based memory extraction and recall."
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description: "Add long-term memory to OpenClaw agents using the Mem0 plugin with skills-based memory extraction, automatic capture, and semantic recall across sessions."
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Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents with the `@mem0/openclaw-mem0` plugin. Your agent forgets everything between sessions. This plugin fixes that by automatically watching conversations, extracting what matters, and bringing it back when relevant.
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title: OpenCode
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description: "Add persistent memory to OpenCode with the Mem0 plugin: native SDK-backed memory tools, lifecycle hooks, and skills."
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description: "Add persistent memory to OpenCode with the Mem0 plugin featuring native SDK-backed memory tools, lifecycle hooks, and skills that recall project context."
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---
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Add persistent memory to [**OpenCode**](https://opencode.ai) with the Mem0 plugin. Your agent forgets everything between sessions. Mem0 fixes that by storing decisions, preferences, and learnings so they carry over automatically.
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title: Pi Agent
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description: "Add persistent memory to Pi Agent with the Mem0 plugin semantic search, auto-capture, and dream consolidation."
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description: "Add persistent memory to Pi Agent with the Mem0 plugin featuring semantic search, automatic capture, and dream consolidation for smarter agent recall."
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Add persistent memory to [**Pi Agent**](https://pi.dev) with `@mem0/pi-agent-plugin`. Your agent forgets everything between sessions. This plugin fixes that by automatically capturing knowledge from conversations, storing it in Mem0's cloud memory layer, and retrieving relevant context before every response.
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title: 'Pipecat'
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description: 'Integrate Mem0 with Pipecat for conversational memory in AI agents'
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description: 'Integrate Mem0 with Pipecat for long-term conversational memory in voice and chat AI agents, delivering personalized responses based on user history'
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# Pipecat Integration
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title: "Raycast Extension"
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description: "Mem0 Raycast extension for intelligent memory management"
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description: "Store and retrieve memories from Raycast with the Mem0 extension for intelligent memory management, quick capture, and semantic search on your Mac."
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---
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Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
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title: Respan
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description: "Combine Mem0 persistent memory with Respan observability for tracked, cost-optimized AI applications."
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description: "Combine Mem0 persistent memory with Respan LLM observability to build tracked, cost-optimized AI applications with performance metrics and personalization."
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---
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Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Respan.
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---
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title: Vercel AI SDK
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description: "Use the Mem0 AI SDK Provider with Vercel AI SDK for persistent memory in conversational AI applications."
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description: "Use the Mem0 AI SDK Provider with Vercel AI SDK to add persistent memory to conversational AI applications for context-aware responses and personalization."
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The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-provider) is a library developed by **Mem0** to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.
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title: Zapier
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description: "Add, search, and manage Mem0 memories from any Zap using the Mem0 Zapier app, no code required."
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description: "Add, search, and manage Mem0 memories from any Zap using the Mem0 Zapier app to bring persistent memory to workflows across thousands of apps, no code."
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---
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Zaps fire and forget. The [Mem0](https://mem0.ai) app gives them memory: store durable facts from a form submission, a support ticket, or a chat message, then recall them later from any of [Zapier's](https://zapier.com) thousands of apps. No code, no server.
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