Unverified50% confidenceSolutionExact time
Passing only the latest user message plus a short summary (rather than the entire history) to the router, and attaching a pre_model_hook to the Supervisor, can streamline its input and reduce Token consumption.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
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Related Claims
UnverifiedEngineering techniques for controlling token costs include semantic caching, context window compression (summarizing historical messages), and routing strategies (using a small model to determine whether a large model needs to be called).66% similarUnverifiedFor static prompt templates with variable placeholders, the rendered prompt can be cached before sending it to the model; using an in-memory LRU cache cuts string interpolation time and reduces request body size.65% similarUnverifiedStreaming allows the server to progressively push tokens to the client during generation, achieving a typewriter-like character-by-character appearance effect, thereby reducing perceived wait time.64% similarUnverifiedWhen using long-context models, input context should be streamlined and the entire codebase should not be stuffed into the prompt; precisely filtering relevant files both saves tokens and improves output quality.64% similarUnverifiedContext includes system prompts, few-shot examples, historical conversation turns, and the current user input, and the later the content appears, the greater its influence on the output usually is.64% similar
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