Engine architecture
Designed from the ground up for low-latency agent memory workloads, not retrofitted from a general-purpose vector database.
Most vector databases were built to search millions of documents a handful of times a second. Agent memory is almost the opposite shape: small reads and writes, happening constantly, where a few hundred milliseconds of latency is the difference between an agent feeling instant or feeling sluggish. Every decision below follows from that difference.
HNSW + graph hybrid
Combines fast vector similarity search with structured graph traversal for hybrid retrieval.
- Sub-0.4ms P99 latency
- 100M+ vector capacity
ACID transactional storage
Complete transactional integrity for agent memory writes, with no partial or corrupted state possible.
- WAL journal logging
- Crash-safe recovery
Zero-copy branching
Instant state snapshotting with copy-on-write overhead for parallel agent reasoning trees.
- Instant `claw branch`
- Deterministic replay
Adaptive query planning
Each query is routed to whichever index (vector, graph, or both) actually answers it, instead of always scanning everything.
- Cost-based routing
- No manual index hints
Why sub-millisecond retrieval is possible
Agent memory access patterns look nothing like document search: small payloads, high read frequency, and a strong locality bias toward recent context. ClawDB's index is tuned for that shape of traffic rather than bulk corpus retrieval, trading off some of the raw throughput a document-search engine would optimize for in exchange for consistently low latency on the reads an agent actually makes.
Common questions
Against a single-node instance handling typical agent-memory-sized reads (small payloads, warm cache). Your own numbers will vary with data volume, hardware, and network hops to a hosted instance, so treat it as a target the engine is built for rather than a guarantee for every workload.
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