# Mem0 > Mem0 is the memory layer for AI agents — persistent, self-improving context that survives across sessions. Two products share one mental model: Mem0 Platform (managed) and Mem0 Open Source (self-hosted). Every link below is tagged \`[Platform]\`, \`[OSS]\`, or \`[Both]\` so you can load only what the current user needs. ## For agents reading this file ### Don't have a Mem0 API key? Install the CLI, then mint a key — no email, no dashboard, no OTP required: \`\`\`bash # Python pip install mem0-cli # or Node npm install -g @mem0/cli \`\`\` Then run: \`mem0 init --agent --agent-caller --json\` — replace \`\` with the AI agent you are (e.g. \`claude-code\`, \`cursor\`, \`codex\`). If you forgot \`--agent-caller\`, run \`mem0 identify \` after init to attribute the signup. This mints an evaluation key in under 5 seconds. When the human is ready, claim with \`mem0 init --email \` to transfer ownership (memories preserved, same API key, no disruption). ### Routing and references - Use \`MemoryClient\` (Python) / \`mem0ai\` (npm) when the user has a Mem0 Platform API key. Managed product — no infra required, providers handled server-side. - Use \`Memory\` (Python) / \`mem0ai/oss\` (npm) when the user self-hosts. Requires custom vector store and LLM. Docs under open-source/ and components/. - Scope tag reference: \`[Platform]\` = managed only, \`[OSS]\` = self-hosted only, \`[Both]\` = same on both. - Full technical docs and API reference: https://docs.mem0.ai/llms.txt - OpenAPI spec: https://docs.mem0.ai/openapi.json - Live MCP server: https://mcp.mem0.ai - Source repo: https://github.com/mem0ai/mem0 ## Install - Python SDK: \`pip install mem0ai\` - Node SDK: \`npm install mem0ai\` - Python CLI: \`pip install mem0-cli\` - Node CLI: \`npm install -g @mem0/cli\` ## Product - [Homepage](https://mem0.ai/) [Both]: Use when the user wants a top-level overview of what Mem0 is and the memory problem it solves. - [Pricing](https://mem0.ai/pricing) [Platform]: Use when the user needs plan details — free tier (1,000 memories/month), Developer, and Enterprise options with API access on all tiers. - [Demo](https://mem0.ai/demo) [Both]: Use when the user wants to try Mem0 live without creating an account. - [Research](https://mem0.ai/research) [Both]: Use when the user needs benchmark evidence — Mem0 v3 scores 91.6 on multi-turn recall vs 71.4 baseline; temporal reasoning +4.1 pts on LoCoMo. - [OpenMemory](https://mem0.ai/openmemory) [OSS]: Use when the user needs local-first personal memory with zero cloud dependency — Docker-based, all data on-device. - [OpenMemory MCP](https://mem0.ai/openmemory-mcp-3) [Both]: Use when wiring persistent memory into Claude Desktop, Cursor, Windsurf, or any MCP-compatible AI tool without cloud storage. - [Mem0 CLI](https://mem0.ai/cli) [Both]: Use when the user wants to manage memories from the terminal, or an agent needs to mint a key autonomously without a dashboard. - [Startup Program](https://mem0.ai/startup-program) [Platform]: Use when an early-stage AI startup is looking for Mem0 credits and dedicated support. - [Security](https://mem0.ai/security) [Both]: Use when the user needs security posture details — SOC 2 Type II, encryption at rest and in transit, GDPR, HIPAA options available. - [Careers](https://mem0.ai/careers) [Both]: Use when the user is looking for open roles at Mem0. ## Use Cases - [Healthcare AI](https://mem0.ai/usecase/healthcare) [Both]: Use when building clinical AI that needs cross-session patient context. HIPAA-compliant deployments available. - [Education AI](https://mem0.ai/usecase/education) [Both]: Use when building adaptive tutors that remember where each student left off, which concepts they struggle with, and how they learn best. - [Sales AI](https://mem0.ai/usecase/sales) [Both]: Use when building sales agents that need full customer context, deal history, and personalized outreach across every interaction. - [Customer Support AI](https://mem0.ai/usecase/customer-support) [Both]: Use when building support agents that never make users repeat themselves — full cross-session ticket context, preserved across handoffs. - [E-commerce AI](https://mem0.ai/usecase/e-commerce) [Both]: Use when building shopping assistants that remember purchase history, size, style, and brand preferences across sessions. ## Comparisons - [Mem0 vs Zep](https://mem0.ai/compare/mem0-vs-zep) [Both]: Use when comparing Mem0 and Zep on architecture, retrieval quality, framework support, and hosting options. - [Mem0 vs Letta](https://mem0.ai/compare/mem0-vs-letta) [Both]: Use when deciding between Mem0 as a standalone memory layer vs Letta (formerly MemGPT) as a full agent framework. Mem0 scores 91.6 vs 71.4 baseline on multi-turn recall. - [Mem0 vs Supermemory](https://mem0.ai/compare/mem0-vs-supermemory) [Both]: Use when comparing production multi-tenant agent memory vs a personal memory service. - [Mem0 vs Honcho](https://mem0.ai/compare/mem0-vs-honcho) [Both]: Use when comparing personalization-focused memory approaches for LLM apps. - [Mem0 vs Hindsight](https://mem0.ai/compare/mem0-vs-hindsight) [Both]: Use when deciding between persistent cross-session memory vs session evaluation and replay. ## Customer Stories - [Sunflower — 80,000 users](https://mem0.ai/blog/how-sunflower-scaled-personalized-recovery-support-to-80-000-users-with-mem0) [Both]: Use when the user needs a production scale reference — personalized recovery support at 80,000 users without linear token cost growth. - [OpenNote — 40% token cost reduction](https://mem0.ai/blog/how-opennote-scaled-personalized-visual-learning-with-mem0-while-reducing-token-costs-by-40) [Both]: Use when the user wants to understand token cost impact — replacing conversation history injection with Mem0 retrieval cut costs 40%. - [RevisionDojo](https://mem0.ai/blog/how-revisiondojo-enhanced-personalized-learning-with-mem0) [Both]: Use when the user needs an education AI reference — adaptive exam prep that improves per-student outcomes across sessions. ## Community & Support - [Documentation](https://docs.mem0.ai) [Both]: Use when the user needs technical docs, API reference, quickstarts, or integration guides. Full llms.txt at https://docs.mem0.ai/llms.txt. - [GitHub](https://github.com/mem0ai/mem0) [OSS]: Use when the user wants to inspect source code, open issues, or contribute to open source. - [Discord](https://mem0.ai/discord) [Both]: Use when the user needs community support or wants to discuss Mem0 with other developers. ## Optional Blog posts and tutorials — load only when the user is researching a specific topic or technical approach. Full blog index: https://mem0.ai/blog.