supermemory

Supermemory provides a robust, state-of-the-art memory and context management layer for AI agents and applications. It solves the critical problem of stateless AI by building a living knowledge graph that evolves through user interactions. By maintaining long-term context, it allows developers to build agents that remember user preferences, project specific data, and past discussions, effectively extending the context window indefinitely for any chatbot, task assistant, or knowledge-intensive application.

The system is designed for high-performance retrieval and automatic information extraction. It handles multi-modal inputs, including text, PDFs, videos, images, and URLs, processing them into a structured ontology of memories. Developers can integrate this directly via SDKs for TypeScript and Python, or through proxy-based patterns like the Infinite Chat Provider, ensuring seamless compatibility with Vercel AI SDK, LangChain, and other major agent frameworks.

• Advanced Memory API: Automatically extracts facts from conversations, handles temporal changes, manages contradictions, and performs automatic forgetting of expired data.

• Dynamic User Profiles: Combines static facts like names and roles with episodic data from recent interactions to create a personalized, evolving user context for every request.

• Hybrid Semantic Search: Integrates RAG with metadata filtering and contextual chunking to return highly relevant information rather than mere keyword matches.

• Multi-modal Ingestion: Native support for processing and indexing diverse content formats including documents, code repositories, web pages, and video/audio transcriptions.

• Developer-First Integration: Offers SDKs, an MCP (Model Context Protocol) server for local IDE integration (Cursor, VS Code, Claude Desktop), and robust APIs for custom backend implementations.

• Utilize Container Tags: Use unique identifiers for users or projects to isolate memory spaces and prevent cross-contamination of contexts.

• Data Governance: Apply metadata during ingestion to enable advanced filtering and better retrieval accuracy within your specific knowledge domain.

• Performance Optimization: The system operates with low latency, with most profile retrievals occurring in ~50ms, suitable for real-time agent responses.

• Scalability: Leverages distributed storage and asynchronous processing for large-scale knowledge bases, including batch document uploads and real-time webhooks for connected services like GitHub, Google Drive, or Notion.

• Best Practices: Use threshold settings to balance precision and recall during semantic searches, and leverage the knowledge graph construction to maintain relationships between derived facts.