FloTorch caches execution results at the step level within agent workflows, scoping each cache entry to its execution context and I/O signature. Stable computations are safely reused across runs — without the risk of cross-run contamination, stale state, or custom cache logic.

Redundant model calls, re-embedded documents, and repeated tool queries are the silent cost drivers in production pipelines. FloTorch eliminates them across all four caching layers — reducing both compute time and cloud costs, and making per-run inference cost predictable rather than variable.

Cache intermediary results from language models, embedding generators, retrieval steps, third-party APIs, or internal tools — without coupling to specific libraries or frameworks. Switch providers without rebuilding your cache layer.

Not every repeated question is worded identically. FloTorch's semantic cache uses vector similarity to match incoming queries against previously cached responses — so paraphrased or near-duplicate inputs return cached results without triggering a new model call. Fewer redundant inferences, lower costs, faster responses at scale.

For deterministic workflows — evaluation pipelines, regression suites, fixed templates — FloTorch caches exact prompt-response pairs and returns them instantly on repeat. Same input, same model, same parameters: zero redundant inference, zero variance in cost or latency.

Each cache entry is indexed using a cryptographic hash of structured inputs and outputs — including prompt version, model ID, and sampling parameters — ensuring consistency and reproducibility across runs while structurally preventing stale or mismatched cache hits.

Every cache hit is logged, traceable, and referenceable. Inspect cache status, trace decision paths, and surface the exact inputs and outputs behind any agent action — giving you the replay capability that debugging, regression testing, and governance all require.

Set TTL rules by data volatility — from real-time feeds to stable knowledge bases. Force fresh execution per node, enable cache-busting for experimental runs, or define runtime overrides. Caching behavior that matches real-world freshness requirements, without writing custom invalidation logic.

Preserve reasoning traces, planning states, and partial task completions across execution boundaries. When a workflow is interrupted mid-run, FloTorch resumes from the last valid checkpoint — no restart from zero, no lost work, and a complete execution record for replay and audit.