LLM infrastructure for temporal
memory and state

An AI agent is only as good as the context it is given. The usual answer — stitch a vector database to a cache to a feature store to a queue, then replay whole histories into every prompt — is expensive to run, hard to keep in sync, and impossible to audit. TemporalStore treats context management as a serving plus dedicated storage problem: one temporal engine remembers what happened, when, where, and why across sessions, agents, devices, teams, and tools, then serves the model a compact, source-backed ContextPack instead of a transcript.

The payoff is concrete. Managed, de-duplicated packs send only fresh, relevant evidence, so you get Fewer tokens at equal answer quality — often a large reduction in prompt tokens. Time-valid memory with stale-context blocking and replayable citations means Better answers and less hallucination. And because retrieval is bounded temporal reads over a memory-first, multi-layer architecture, serving latency stays extremely low even across millions of entities. Hot context stays in memory, warm state moves through PMem and SSD, and cold replay history lands in shared storage. See benchmarks.

Beyond speed and quality, TemporalStore is one service instead of four: memory, retrieval, summaries, safety counters, and replay live in a single store. Every decision is replayable and auditable — reconstruct the exact context behind any answer for debugging, evaluation, or compliance. And it is open source: self-host on premise, run in your cloud, or scale the same engine to disaggregated shared storage and five-nines when you need it.