Google ADK Context Engineering - Compaction, Caching & Artifacts (Cut LLM Costs ) ( Tutorial- 10 )

This Google ADK tutorial covers three key techniques to optimize LLM costs and performance: artifacts for managing generated content, context compaction to reduce token usage in long conversations, and context caching to reuse expensive computations. These strategies help developers build more efficient AI applications by minimizing redundant processing and token consumption. The tutorial demonstrates practical implementations using Google's AI Development Kit.

Key Points

• •Use artifacts to separate and manage generated content (code, documents, configs) from conversation context, reducing token overhead
• •Implement context compaction to summarize or compress long conversation histories, maintaining relevance while cutting token usage
• •Enable context caching to store and reuse expensive prompt computations across multiple API calls, reducing latency and costs
• •Combine all three techniques in long-running conversations to achieve significant cost reduction and performance improvements
• •Artifacts prevent context bloat by isolating large generated outputs from the main conversation thread
• •Context caching is especially effective for multi-turn conversations with repeated system prompts or static context
• •Compaction strategies include summarization, filtering irrelevant messages, and consolidating similar exchanges
• •Monitor token usage metrics to identify optimization opportunities in your ADK applications
• •Cache invalidation and refresh strategies ensure up-to-date context while maintaining efficiency gains
• •Combine techniques strategically based on conversation patterns and cost-benefit analysis

Workflow Diagram

Concepts