Supermemory is a context engineering platform that serves as a state-of-the-art long-term and short-term memory layer for AI agents. Developed by Dhravya Shah and team, it provides the essential infrastructure required to move beyond simple, stateless AI interactions. By leveraging a custom-built knowledge graph, dynamic dreaming, and advanced retrieval-augmented generation (RAG) capabilities, Supermemory enables agents to maintain persistent user profiles, recall historical context, and adapt to evolving information in real time with sub-300ms latency. The system effectively bridges the gap between raw data storage and intelligent, context-aware agent performance.

The platform functions as a comprehensive context management layer that handles the entire pipeline of data ingestion, semantic understanding, and intelligent retrieval. It simplifies complex RAG workflows by automating chunking, entity resolution, and graph traversal. Rather than relying on isolated vector databases that start every session from zero, Supermemory builds a living ontology of user preferences, facts, and project histories that persist across sessions, tools, and platforms, ensuring that every AI agent in a developer's stack acts as a cohesive extension of the user.

Some of the key features are:

• Persistent Memory Graph: Stores knowledge in a structured graph that supports automatic updates, contradictions, and multi-hop reasoning rather than static vector chunks.
• SuperRAG: Provides a high-performance hybrid search mechanism that combines semantic search with keyword matching and graph traversal for millisecond-speed retrieval.
• Contextual Chunking: Utilizes smart parsing techniques to ingest diverse file formats like PDFs, images, video, and web pages while preserving semantic meaning.
• Dynamic Dreaming: Automatically summarizes and consolidates memory over time, allowing agents to reflect on information similar to human thought processes.
• Connector Ecosystem: Offers native sync capabilities for services like Notion, Google Drive, Gmail, GitHub, and S3 to keep agent knowledge base fresh without manual re-indexing.
• Multi-Tool Compatibility: Integrates seamlessly with popular AI tools and IDEs like Claude, Cursor, and VS Code via the Model Context Protocol (MCP) and custom plugins.
• Enterprise Security: Provides flexible deployment options including managed cloud, on-premises, and air-gapped instances with SOC 2 Type II and GDPR compliance.

To use Supermemory, developers initialize the client SDK in their application or connect to the system via the provided plugins for coding assistants. Once integrated, the system automatically processes ingested data, extracts actionable facts, and builds a queryable graph. During agent interactions, the platform's API performs real-time context injection, fetching the most relevant memories and user profiles to ground LLM responses, ensuring that the AI agent's performance scales with the quality of accumulated knowledge.

Some common use cases include:

• Coding Assistants: Maintaining persistent knowledge of codebase architectures, team-specific coding standards, and recurring preferences across different IDEs and sessions.
• Personal AI Agents: Creating a 'second brain' that connects user notes, emails, and documents to provide highly personalized assistance regardless of the AI model being used.
• Customer Support Automation: Building agents that remember user history and preferences to deliver contextually grounded, consistent, and fast support interactions.
• Enterprise Knowledge Management: Synching internal wikis, documentation, and institutional tools to allow internal AI agents to answer questions with the latest verified data.