NativeArc automatically caches execution results at the step level within agent workflows, using input/output signatures and execution context to identify reusable computations—no custom logic needed.

Minimize redundant model calls, vector store queries, and API requests. Reduce both compute time and cloud costs—especially in high-frequency or production-scale pipelines.

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

Each step’s cache entry is indexed using a cryptographic hash of structured inputs and outputs, ensuring consistency and reproducibility across runs while avoiding stale or mismatched cache hits.ibility.

Easily inspect cache status, trace origins, and manage entries via the NativeArc UI or API. Each cache hit is logged and referenceable, aiding reproducibility and debugging.

Define caching rules per node—force fresh execution, reuse always, or TTL-based expiry. Supports runtime overrides and cache-busting for debugging or experimental workflows.