HawDB

HawDB is an AI-native embedded database for agents. It keeps the facts an agent reasons over — entities, relationships, documents, and events — in one crash-safe process, returns them as bounded context, and gives each agent its own durable branch to work in.

• Context layer. Graph structure, relational rows, lexical search, and vector search share one engine. A retrieval request takes search candidates, filters them, walks the authorized graph, reranks with a typed score, and hydrates the result from the canonical snapshot. See Context, in one pipeline.
• Built for agent workloads. Agents talk to the database through parameterized Cypher and SQL. Every read has an explicit row and payload budget, and an over-budget request fails instead of returning a silently truncated answer. A read transaction pins one published snapshot, so a multi-step plan sees one consistent state.
• Branches for agents. A copy-on-write branch costs metadata and an empty WAL, not a copy of the data. An agent can try a plan, write intermediate facts, or run a long task on…