supermemoryai/supermemory

State-of-the-art memory and context engine for AI. Docs · Quickstart · Self-host · Dashboard · Discord English · 簡體中文 #1 on every major AI memory benchmark — LongMemEval , LoCoMo , and ConvoMem . 95% Recall@15 with a 99.4% context reduction · ~50ms user profiles. Read the research → Supermemory is the memory and context layer for AI. #1 on LongMemEval , LoCoMo , and ConvoMem — the three major benchmarks for AI memory. We are a research lab building the engine, plugins and tools around it. Your AI forgets everything between conversations. Supermemory fixes that. It automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, forgets expired information, and delivers the right context at the right time. Full RAG, connectors, file processing — the entire context stack, one system. 🧠 Memory Extracts facts from conversations. Handles temporal changes, contradictions, and automatic forgetting. 👤 User Profiles Auto-maintained user context — stable facts + recent activity. One call, ~50ms. 🔍 Hybrid Search RAG + Memory in a single query. Knowledge base docs and personalized context together. 🔌 Connectors Google Drive · Gmail · Notion · OneDrive · GitHub — auto-sync with real-time webhooks. 📄 Multi-modal Extractors PDFs, images (OCR), videos (transcription), code (AST-aware chunking). Upload and it works. All of this is in our single memory structure and ontology. Use Supermemory 🧑‍💻 I use AI tools Give Claude Code, Muse Code, Cursor, Codex and OpenCode persistent memory across every conversation with a plugin or the MCP server. Your AI remembers your preferences, projects, past discussions — and gets smarter over time. → Jump to User setup 🔧 I'm building AI products Add memory, RAG, user profiles, and connectors to your agents and apps with a single API . No vector DB config. No embedding pipelines. No chunking strategies. → Jump to developer quickstart 🖥️ I want to run it myself State-of-the-art memory, on your machine. One binary. Zero config. Bring any model — or run fully offline with Ollama. curl -fsSL https://supermemory.ai/install | bash → Jump to Supermemory local Give your AI memory Plugins and the MCP server give any compatible AI assistant persistent memory. One install, and your AI remembers you. Supermemory Plugins Supermemory comes built with plugins for Claude Code, Muse Code, Cursor, Codex, OpenCode, OpenClaw, and Hermes. These plugins are implementations of the supermemory API, and they are open source! You can find them here: Claude Code plugin: https://github.com/supermemoryai/claude-supermemory Muse Code plugin: https://github.com/supermemoryai/muse-supermemory Cursor plugin: https://github.com/supermemoryai/cursor-supermemory Codex plugin: https://github.com/supermemoryai/codex-supermemory OpenClaw plugin: https://github.com/supermemoryai/openclaw-supermemory OpenCode plugin: https://github.com/supermemoryai/opencode-supermemory Hermes agent (Supermemory memory provider): https://github.com/NousResearch/hermes-agent MCP Server URL: https://mcp.supermemory.ai/mcp { "mcpServers" : { "supermemory" : { "url" : " https://mcp.supermemory.ai/mcp " } } } Read more about our MCP here - https://supermemory.ai/docs/supermemory-mcp/mcp What your AI gets Tool What it does memory Save or forget information. Your AI calls this automatically when you share something worth remembering. recall Search memories by query. Returns relevant memories + your user profile summary. context Injects your full profile (preferences, recent activity) into the conversation at start. In Cursor and Claude Code, just type /context . How it works Once installed, Supermemory runs in the background: You talk to your AI normally. Share preferences, mention projects, discuss problems. Supermemory extracts and stores the important stuff. Facts, preferences, project context — not noise. Next conversation, your AI already knows you. It recalls what you're working on, how you like things, what you discussed before. Memory is scoped with projects (container tags) so you can separate work and personal context, or organize by client, repo, or anything else. Supported clients Claude Desktop · Cursor · Windsurf · VS Code · Claude Code · OpenCode · OpenClaw · Hermes The MCP server is open source — view the source . Manual configuration Add this to your MCP client config: { "mcpServers" : { "supermemory" : { "url" : " https://mcp.supermemory.ai/mcp " } } } Build with Supermemory (API) If you're building AI agents or apps, Supermemory gives you the entire context stack through one API — memory, RAG, user profiles, connectors, and file processing. Install npm install supermemory # or: pip install supermemory Quickstart import Supermemory from "supermemory" ; const client = new Supermemory ( ) ; // Store a conversation await client . add ( { content : "User loves TypeScript and prefers functional patterns" , containerTag : "user_123" , } ) ; // Get user profile + relevant memories in one call const { profile , searchResults } = await client . profile ( { containerTag : "user_123" , q : "What programming style does the user prefer?" , } ) ; // profile.static → ["Loves TypeScript", "Prefers functional patterns"] // profile.dynamic → ["Working on API integration"] // searchResults → Relevant memories ranked by similarity from supermemory import Supermemory client = Supermemory () client . add ( content = "User loves TypeScript and prefers functional patterns" , container_tag = "user_123" ) result = client . profile ( container_tag = "user_123" , q = "programming style" ) print ( result . profile . static ) # Long-term facts print ( result . profile . dynamic ) # Recent context Supermemory automatically extracts memories, builds user profiles, and returns relevant context. No embedding pipelines, no vector DB config, no chunking strategies. Framework integrations Drop-in wrappers for every major AI framework: // Vercel AI SDK import { withSupermemory } from "@supermemory/tools/ai-sdk" ; const model = withSupermemory ( openai ( "gpt-4o" ) , { containerTag : "user_123" , customId : "conv-1" } ) ; // Mastra import { withSupermemory } from "@supermemory/tools/mastra" ; const agent = new Agent ( withSupermemory ( config , "user-123" , { mode : "full" } ) ) ; Vercel AI SDK · LangChain · LangGraph · OpenAI Agents SDK · Mastra · Agno · Claude Memory Tool · n8n Search modes // Hybrid (default) — RAG + Memory in one query const results = await client . search ( { q : "how do I deploy?" , containerTag : "user_123" , searchMode : "hybrid" , } ) ; // Returns deployment docs (RAG) + user's deploy preferences (Memory) // Memories only const results = await client . search ( { q : "user preferences" , containerTag : "user_123" , searchMode : "memories" , } ) ; User profiles Traditional memory relies on search — you need to know what to ask for. Supermemory automatically maintains a profile for every user: const { profile } = await client . profile ( { containerTag : "user_123" } ) ; // profile.static → ["Senior engineer at Acme", "Prefers dark mode", "Uses Vim"] // profile.dynamic → ["Working on auth migration", "Debugging rate limits"] One call. ~50ms. Inject into your system prompt and your agent instantly knows who it's talking to. Connectors Auto-sync external data into your knowledge base: Google Drive · Gmail · Notion · OneDrive · GitHub · Web Crawler Real-time webhooks. Documents automatically processed, chunked, and searchable. API at a glance Method Purpose client.add() Store content — text, conversations, URLs, HTML client.profile() User profile + optional search in one call client.search() Hybrid search across memories and documents ( searchMode ) client.search.documents() Document search with metadata filters (legacy v3 response shape) client.documents.uploadFile() Upload PDFs, images, videos, code client.documents.list() List and filter documents client.settings.