TemporalStore blog
Deep dives on temporal serving for LLM memory and features.
Architecture notes, benchmarks, and product thinking on TemporalStore — the open-source temporal engine behind LLM context and memory, and the sequence, aggregate, and control-state features that ride the same serving path.
LLM Context
TemporalStore: an open-source engine that can cut LLM token cost and improve answer quality
Why re-sending a growing, noisy transcript every turn is costly — and how a small, denoised,
ranked ContextPack (plus cross-session team memory, and no vector DB or RocksDB underneath)
fixes it, with benchmarks on LOCOMO and LongMemEval vs a VikingMem-style SOTA baseline.
Architecture
Why temporal features need a serving engine, not just another cache
Fraud, ads, recommendation, and agent systems increasingly ask for fresh windows,
filters, distinct counts, and sequences at request time. A generic cache can hold
values, but it does not naturally explain how those values were computed, expired,
filtered, replayed, or kept consistent with event history.
AI Context
Where temporal storage fits in LLM and agent systems
Vector databases retrieve semantic neighbors. Temporal serving stores the structured
state around those interactions: session timelines, recent tool use, policy counters,
user preferences, and freshness-sensitive context that prompt builders need.