AI agent memory 2

Graph database for connected, stateful AI context and agent memory HydraDB is an open-source, native graph database designed as an AI context infrastructure layer for agent memory, ontologies, company brains, GraphRAG, and connected application data. Built directly on object storage, it combines graph traversal, vector search integrations, exact-match search, temporal versioning, and structured relationships to help AI systems retrieve precise, relevant context. HydraDB provides multi-tenant storage, strongly consistent queries, tiered storage across memory, NVMe SSD, and object storage, sub-200ms latency, OpenCypher support, Neo4j-compatible Bolt connectivity, and HTTP APIs.

PostgreSQL platform for hybrid AI agent memory and retrieval Polygres is an all-in-one PostgreSQL platform for AI agents that combines relational data, graph traversal, vector search, and hybrid retrieval through a unified API. It turns existing PostgreSQL databases into working memory for agents, allowing them to retrieve structured rows, connected relationships, semantic matches, full-text results, and filtered context without using a separate vector database, graph database, or synchronization pipeline. Polygres includes pgGraph for foreign-key-based graph traversal and pgContext for fused retrieval across dense HNSW vectors, sparse search, full-text search, filtering, recommendations, discovery, grouped results, and lookups. Users can deploy it as a managed cloud database or self-host its open-source components.