Bi-temporal (time-travel) memory: how it works and when to use it
Bi-temporal (time-travel) memory, answers what the agent believed at a past instant using validity windows, so you can reconstruct 'what did we know then?'. Here's how Crowkis does it and why it matters for cost and safety.
Production LLM traffic is deeply repetitive, and repetition is exactly what a bill is made of. Bi-temporal (time-travel) memory is how Crowkis answers what the agent believed at a past instant using validity windows, so you can reconstruct 'what did we know then?'.
How it works
Crowkis answers what the agent believed at a past instant using validity windows, so you can reconstruct 'what did we know then?'. It runs inside one Redis-compatible engine, so it composes with semantic caching, agent memory, and the other intelligence layers instead of being a separate service you wire together.
CMEMASOF support u_42 "address" AT 2026-04-01
Why it matters
Repetitive LLM workloads are where the money is, and semantic caching can cut costs up to 60-70% on repetitive workloads. Bi-temporal (time-travel) memory is part of what makes that reuse safe rather than reckless, the difference between a cache you trust in production and one you audit after every incident. Drop it in over RESP, gRPC, REST, or MCP, no rewrite required.
Infrastructure earns the critical path one boring, verifiable feature at a time.