Efficient Large Context Window Handling for Gemini Models

[Adewale-1]

Problem

The current ADK implementation has several inefficiencies when handling large context windows (1M-2M tokens) with Gemini models:

1. Serialization Bottleneck: State serialization becomes extremely slow with large contexts
2. Memory Inefficiency: Duplicate contexts are stored across agents
3. Missing Integration: Poor integration with Gemini's context caching mechanism
4. Poor Scalability: Memory usage grows linearly with number of agents

Proposed Solution

Implement a reference-based context storage system with the following components:

• A ContextReferenceStore class for efficient storage and retrieval of large contexts
• An enhanced LargeContextState class extending ADK's State
• LangGraph integration utilities to support the reference-based approach

Expected Benefits

Initial benchmarks show:

• 600x+ faster serialization
• ~15,900x smaller serialized size
• Up to 49x memory reduction with multiple agents
• Proper integration with Gemini API caching

Implementation Details

Will be provided in the PR, but the approach uses content-based hashing and references to avoid duplicate storage, while maintaining compatibility with existing ADK APIs.