--- title: Prompt Caching with Deep Agents | daily.dev description: Prompt caching can reduce LLM token costs by 41–80%, but different model providers implement it differently, making provider-agnostic caching complex.... canonical: https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Prompt Caching with Deep Agents | daily.dev og:description: Prompt caching can reduce LLM token costs by 41–80%, but different model providers implement it differently, making provider-agnostic caching complex.... og:url: https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa og:image: https://api.daily.dev/og/posts/fucfU0kxA.png og:image:alt: Prompt Caching with Deep Agents og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Prompt Caching with Deep Agents **[LangChain](https://daily.dev/sources/langchain)** · 5 min read · 0 upvotes · 0 comments ## Summary Prompt caching can reduce LLM token costs by 41–80%, but different model providers implement it differently, making provider-agnostic caching complex. LangChain's Deep Agents harness automatically handles prompt caching across all major providers — setting explicit cache breakpoints where supported, opting into implicit caching otherwise, and structuring prompts to maximize cache hits. Benchmarks on real agent trajectories show 49–80% token cost reductions across Claude Haiku, GPT mini, and Gemini Flash. LangSmith provides observability into cache reads, token usage, and cost savings at per-invocation and per-trajectory levels. Upcoming features like cache prewarm, routing keys, and configurable TTL promise further savings. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How Does Prompt Caching Work and When Does It Actually Cut LLM Costs?](https://daily.dev/posts/how-does-prompt-caching-work-and-when-does-it-actually-cut-llm-costs--vnardpi3a) · DigitalOcean Community · 1 upvotes · 0 comments - [Prompt Caching Explained](https://daily.dev/posts/prompt-caching-explained-mhomvre8o) · DigitalOcean Community · 2 upvotes · 0 comments - [Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems](https://daily.dev/posts/prompt-caching-for-anthropic-and-openai-models-building-cost-efficient-ai-systems-mjzh92xkq) · DigitalOcean · 2 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#langsmith](https://daily.dev/tags/langsmith) [View this post on daily.dev](https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Prompt Caching with Deep Agents","url":"https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa"},"datePublished":"2026-07-08T19:22:59.073Z","dateModified":"2026-07-08T19:24:08.246Z","description":"Prompt caching can reduce LLM token costs by 41–80%, but different model providers implement it differently, making provider-agnostic caching complex....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/b33f1b784d41c47186c297a6894709b5?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/b33f1b784d41c47186c297a6894709b5?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"LangChain","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"LangChain","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0f4f2629e0724a849823f8cd0d913e13","url":"https://daily.dev/sources/langchain"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/prompt-caching-with-deep-agents-fucfu0kxa","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,ai-agents,langchain,langsmith","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"LangChain","item":"https://daily.dev/sources/langchain"},{"@type":"ListItem","position":3,"name":"Prompt Caching with Deep Agents"}]} ```