Earlier quoted context omitted.
Exact match, word for word. agent-cache takes everything that defines an LLM request - which model you're calling (gpt-4o, Claude, etc.), the full conversation history (system prompt + user messages + assistant responses), sampling parameters like temperature, and any tool/function definitions the model has access to - serializes it all into a canonical JSON string with sorted keys, and hashes it with SHA-256. That h…
Do you automate the same prompt twice and if you already burn the tokens to get the first answer what the second answer gain do you record it to try and teach your agents for future to look locally or?
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