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Agentic AI Atlas · Supermemory Introduction
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Supermemory Introduction
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Supermemory Introduction
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# Supermemory Introduction Source: https://supermemory.ai/docs/introduction, https://supermemory.ai/docs/intro ## Core Definition Supermemory is "the Memory API for the AI era" -- infrastructure for AI agent memory and context management. Achieves state-of-the-art performance on LongMemEval and LoCoMo benchmarks. ## Key Characteristics - **Scalability** -- handles growing data volumes - **Performance** -- "hyper fast" operations - **Affordability** -- cost-effective pricing - **Production-Ready** -- suitable for real-world deployment ## Main Components - **Memory APIs**: Composable APIs for memory operations and RAG - **User Profiles**: Contextual intelligence for LLMs combining static and dynamic facts - **SDK Integration**: Multiple SDKs for Python and TypeScript - **Connectors**: Real-time sync with Google Drive, Gmail, Notion, OneDrive, GitHub, web crawlers ## Operational Flow 1. **Input**: Users submit text, files, and chat conversations 2. **Processing**: Supermemory indexes them and builds a semantic understanding graph tied to entities (users, documents, projects, organizations) 3. **Retrieval**: At query time, the most contextually relevant information reaches the language model ## Context Delivery Methods - **Memory API** extracts and maintains evolving user facts in real-time - **User Profiles** combine static baseline with dynamic episodic details - **RAG Integration** provides semantic search with metadata filtering and contextual chunking All three share the same context pool when using identical user identifiers.
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