About This Site
Universal memory API for AI, personalizes LLMs, provides unlimited context, and simplifies retrieval. Supermemory is a universal memory API for the AI era, designed to help developers stop building retrieval from scratch and personalize LLMs for their users. It provides an unlimited context API that offers automatic long-term context across conversations. Built for developers who ship, it handles enterprise-grade performance at any scale, seamlessly integrates across teams and tools, and allows users to own their data while maintaining compliance with secure and controllable deployment options (cloud, on-prem, on-device). It is model-agnostic, works with any LLM provider, and delivers sub-400ms latency with best-in-class precision and recall.
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Key Features AI
Core Features
Universal memory API for the AI era
Unlimited context API for LLMs
Enterprise-Grade Performance at Any Scale (billions of data points, low-latency retrieval)
Seamless Integration Across Teams & Tools (Notion, Google Drive, CRMs)
Secure by Design, Fully Controllable deployment (cloud, on-prem, on-device)
Model-agnostic APIs (works with any LLM provider)
Sub-400ms latency at scale
Best in class performance (stronger precision and recall)
Works with AI SDK, Langchain, and more
Language Agnostic SDKs (Python, Javascript)
Advantages
Eliminates the need to build retrieval from scratch, saving time and resources.
Enables personalization of LLMs for enhanced user experiences.
Provides unlimited context for AI applications, improving conversation depth.
Offers automatic long-term context across conversations.
Ensures enterprise-grade performance and scalability for billions of data points with low latency.
Seamlessly integrates with existing tools and data sources (e.g., Notion, Google Drive, CRMs).
Secure by design with full control over data storage and compliance (cloud, on-prem, on-device).
Model-agnostic APIs prevent vendor lock-in, allowing flexibility with LLM providers.
Achieves sub-400ms latency at scale with re-imagined RAG for faster and more efficient performance.
Delivers stronger precision and recall at every benchmark.
Easy to start and deploy, with SDKs for Python and Javascript, enabling deployment in days, not months.
Addresses common pain points like expensive/slow vector databases, complex embedding model choices, format parsing issues, exploding scaling costs, connection sync failures, and multimodal support challenges.
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