# Practical and Useful Patterns with ADK Sending the same system prompts and tool definitions over and over? Context Caching dramatically reduces repeated prefix token costs 💰 📌 **Title**: Context Caching 🔗 **URL**: ## 🧩 Overview Context Caching caches the static portions of context sent to the LLM (system instructions, tool definitions, etc.) to reduce repeated token costs. By configuring `ContextCacheConfig`, instead of resending the same prefix tokens with every request, the agent references cached context. The cost optimization impact is especially significant in multi-user environments where many users share the same agent with identical prompts and tool definitions. ## 🛠 How to Use Import `Agent` from `google.adk` and `ContextCacheConfig` from `google.adk.agents`. Create a cache configuration with `ContextCacheConfig(max_entries=100, ttl_seconds=3600)` to set the maximum number of cache entries and the time-to-live in seconds. Pass this `cache_config` to the `Agent`'s `context_cache_config` parameter so that the static portions of the `instruction` (your long system prompt) and `tools` definitions (e.g., `search_kb`, `create_ticket`, `escalate`) are cached, reducing repeated token costs. ## 🏗 Practical Usage **Large-scale customer support optimization:** In a customer support agent, these elements are common across all users: - System instructions (response guidelines, tone, prohibited actions) - Tool definitions (knowledge base search, ticket creation, escalation) - Few-shot examples These static contexts can amount to thousands of tokens per request. For a support bot handling 10,000 requests daily, Context Caching delivers massive token savings. **RAG pipeline optimization:** When tool definitions include knowledge base schemas and search parameter descriptions, caching these optimizes per-query costs. **Multi-tenant SaaS:** When sharing the same agent definition across multiple tenants, only tenant-specific information becomes the dynamic portion while common prompts and tool definitions are shared via cache. ## 💡 Use Cases - 💰 Cost reduction: Cut costs from repeatedly sending long system prompts - 🚀 Latency improvement: Faster prefill processing on cache hits - 👥 Multi-user optimization: Share cache across multiple users with the same prompts - 🏢 Multi-tenant: Efficiently cache tenant-common context portions - 📚 Large tool definitions: Optimize tool definition costs for agents with many tools ## ⚠️ Caveats - Context Caching depends on model provider support. Verify available models in advance - TTL too short reduces hit rates; too long consumes memory. Tune based on access patterns - Benefits are limited if system prompts or tool definitions change frequently - Caching itself may incur costs. Check provider pricing and evaluate total cost - Dynamic context (user-specific information, etc.) is not cacheable. Design clear separation between static and dynamic portions ✨ Context Caching implements "don't repeat yourself" at the infrastructure level. It delivers major cost optimization benefits in multi-user environments! #ADK# #AIAgent#