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Article Summaries:

  • SurrealDB, a London‑based startup, has released SurrealDB 3.0 in general availability, adding persistent agent memory, enhanced vector search, and an in‑database plugin system to its multi‑model engine. The company says the new version tackles “architectural sprawl” that arises when AI agents require transactional state, long‑term memory, similarity search, and real‑time reactivity across structured, unstructured, and graph data. SurrealDB is positioned as a complement to existing databases rather than a wholesale replacement, targeting AI‑native firms that build agents, knowledge graphs, and real‑time applications. The launch follows a $23 million funding round, bringing total capital raised to $44 million.
  • SurrealDB, a London‑based startup founded in 2022, has released SurrealDB 3.0 in general availability. The new version expands its multi‑model engine to better support AI agents, adding persistent agent memory, enhanced vector search capabilities, and an in‑database plugin system. The company also announced a $23 million funding round, bringing its total capital raised to $44 million. SurrealDB positions itself as a single‑engine solution for AI‑native workloads that traditionally require separate relational, vector, graph, and real‑time databases, aiming to reduce latency, duplication, and operational overhead for agent‑based systems.

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