A graph-native context development platform for storing, enriching, and retrieving structured knowledge with semantic search and portable context cores, supporting RDF, SPARQL, and other standards for AI agent knowledge management.
A hyper-fast local vector database for use with LLM Agents, providing lightweight vector storage and similarity search capabilities for embedding as instant memory and knowledge retrieval components in agent applications.
MemVid is a long-term memory layer for AI agents that uses video encoding for lightweight single-file storage, replacing complex RAG pipelines with instant retrieval.
A private and local AI personal knowledge management app. All data and processing stay on-device with built-in RAG, semantic search, and knowledge graph features for managing personal knowledge bases with full privacy.
AI Data Runtime for Agents. Provides serverless Postgres with a multimodal datalake, enabling scalable retrieval and training. Unifies vector storage, dataset management, and streaming data loading for AI agent workflows.