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Agentic AI Atlas · Supermemory GitHub Repository
page:docs-supermemory-research-raw-09-github-readmea5c.ai
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page:docs-supermemory-research-raw-09-github-readme

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Supermemory GitHub Repository json

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    "article": "\n# Supermemory GitHub Repository\n\nSource: https://github.com/supermemoryai/supermemory\n\n## Overview\n\nSupermemory is a \"memory engine and app that is extremely fast, scalable\" -- \"the Memory API for the AI era.\" Ranks #1 across three major benchmarks: LongMemEval, LoCoMo, and ConvoMem.\n\n## Core Features\n\n### Memory System\n\nAutomatically extracts facts from conversations, handles temporal changes and contradictions, implements automatic forgetting of expired information.\n\n### User Profiles\n\nDual-layer context: stable facts + recent activity. Retrievable in ~50ms per query.\n\n### Hybrid Search\n\nCombines RAG (document retrieval) with personalized memory in single queries.\n\n### Connectors\n\nIntegrates with Google Drive, Gmail, Notion, OneDrive, GitHub, and web crawlers with real-time webhook synchronization.\n\n### Multi-modal Processing\n\nPDFs, images (OCR), videos (transcription), code (AST-aware parsing).\n\n## Architecture\n\nFour layers:\n1. Memory Engine (fact extraction and contradiction resolution)\n2. User Profiles (static + dynamic context)\n3. Hybrid Search (combined RAG and memory)\n4. Connectors and File Processing\n\n## Installation\n\n```bash\nnpm install supermemory  # JavaScript/Node.js\npip install supermemory  # Python\n```\n\n## Integration Frameworks\n\nVercel AI SDK, LangChain, LangGraph, OpenAI Agents SDK, Mastra, Agno, Claude Memory Tool, n8n.\n\n## Client Plugins\n\n- Claude Supermemory Plugin: https://github.com/supermemoryai/claude-supermemory\n- OpenClaw Plugin: https://github.com/supermemoryai/openclaw-supermemory\n- OpenCode Plugin: https://github.com/supermemoryai/opencode-supermemory\n- Hermes Agent: https://github.com/NousResearch/hermes-agent\n\n## MemoryBench\n\nOpen-source benchmarking framework supporting Supermemory, Mem0, and Zep providers. Includes MemScore metric for comparing quality, latency, and token efficiency.\n\n## Tech Stack\n\nTypeScript (64%), MDX (28.9%), Python (6.2%). Built with Postgres, Remix, TailwindCSS, Vite, Cloudflare Workers/KV/Pages, Drizzle ORM.\n\n## License\n\nMIT\n",
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