๐งต TL;DR: Long-running AI agents rack up token costs fast. Deep Agents turns on prompt caching with zero config, cutting up to 80% of cost on real tasks.
Title: Prompt Caching with Deep Agents
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Key points
๐ธ Every request reprocesses the full history, system prompt, and tool defs โ costs compound
โก Prompt caching reuses the compute for static content and only processes the new delta
๐งฉ Explicit cache breakpoints keep partial hits even when the prompt prefix changes a bit
๐ค Deep Agents auto-applies 3 strategies: explicit breakpoints / provider-side implicit caching / cache-maximizing prompt structure
๐ Measured: Claude Haiku 4.5 -77%, GPT-5.4-mini -80%, Gemini 3.5-Flash -49%
๐ญ LangSmith surfaces cache-read tokens per call so you can measure and optimize savings
โณ The longer the conversation, the bigger the win; short runs see little benefit
Abstracting away provider differences with zero config is what makes this land in production.
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