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supermemory™

★4.2
💬7247
💲Free

Supermemory streamlines the integration of AI memory systems by offering a universal API that eliminates the need to build custom retrieval infrastructure. It enhances LLM interactions with unlimited context, ensures seamless scalability for billions of data points, and supports secure, flexible deployment options. With SDKs for Python and JavaScript, developers can deploy quickly and efficiently.

💻
Platform
web
AIAPIContext ManagementData IntegrationDeveloper ToolsLLMMemory API

What is supermemory™?

Supermemory is a universal memory API designed to help developers integrate personalized large language models (LLMs) into their applications without building retrieval systems from scratch. It enables automatic long-term context across conversations, supports enterprise-grade performance at any scale, and allows secure deployment options (cloud, on-prem, on-device). Ideal for developers seeking to enhance user experiences with LLMs, it works seamlessly with various tools and data sources while ensuring flexibility through model-agnostic APIs.

Core Technologies

  • AI
  • LLM
  • Memory API
  • Context Management
  • Retrieval Augmented Generation (RAG)
  • Vector Database Alternative
  • Data Integration
  • Developer Tools
  • API
  • SDK

Key Capabilities

  • Personalizing LLMs
  • Providing unlimited context
  • Simplifying retrieval
  • Enabling automatic long-term context
  • Supporting multimodal data
  • Offering scalable performance
  • Ensuring secure deployment

Use Cases

  • Personalizing LLMs for end users
  • Building agentic apps with long-term context
  • Indexing documents, video, or structured data
  • Connecting to Notion, Google Drive, CRMs
  • Developing co-intelligence platforms
  • Powering cursor-based writing tools
  • Searching large vendor databases

Core Benefits

  • Eliminates manual retrieval system development
  • Improves LLM conversation depth
  • Offers low-latency performance at scale
  • Enhances security and compliance
  • Reduces time-to-market for AI apps
  • Avoids vendor lock-in with model-agnostic design
  • Supports diverse data formats and integrations

Key Features

  • Universal memory API for AI
  • Unlimited context API for LLMs
  • Enterprise-grade performance at any scale
  • Seamless integration with tools like Notion and Google Drive
  • Secure deployment options
  • Model-agnostic APIs
  • Sub-400ms latency
  • Best-in-class precision and recall
  • Language-agnostic SDKs

How to Use

  1. 1
    Integrate the Supermemory API or SDK into your app
  2. 2
    Add memories via POST request to the memories endpoint
  3. 3
    Search memories using GET requests with query parameters
  4. 4
    Connect external apps like OneDrive via dedicated endpoints
  5. 5
    Change OpenAI client base URL for automatic context support

Frequently Asked Questions

Q.What is Supermemory?

A.It's a universal memory API for AI that helps developers personalize LLMs and manage long-term context without building retrieval systems from scratch.

Q.How does Supermemory handle context for LLMs?

A.It offers an unlimited context API that provides automatic long-term context across conversations by integrating directly with LLM providers like OpenAI.

Q.What kind of data can Supermemory handle?

A.It supports documents, video, structured product data, and formats like Markdown, HTML, PDF, Word docs, images, and audio/video.

Q.Is Supermemory scalable?

A.Yes, it's built for enterprise-grade performance and handles billions of data points with low-latency retrieval as data grows.

Q.Can Supermemory be deployed on-premise?

A.Yes, it offers full control over data storage and can be deployed in the cloud, on-prem, or on-device.

Q.Does Supermemory work with any LLM?

A.Yes, it features model-agnostic APIs, allowing compatibility with any LLM provider without lock-in.

Pros & Cons (Reserved)

✓ Pros

  • Eliminates the need to build retrieval from scratch
  • Enables personalization of LLMs for enhanced user experiences
  • Provides unlimited context for AI applications
  • Offers automatic long-term context across conversations
  • Ensures enterprise-grade performance and scalability
  • Seamlessly integrates with existing tools and data sources
  • Secure by design with full data control
  • Model-agnostic APIs prevent vendor lock-in
  • Achieves sub-400ms latency at scale
  • Delivers stronger precision and recall
  • Easy to start and deploy with available SDKs
  • Addresses common pain points like slow vector databases and complex embeddings

✗ Cons

  • No cons provided.

Alternatives

No alternatives found.