【#OpenDataSpaces】# Interview Part 2 is live! AI adoption is accelerating — but why does agentic AI fail in enterprise? Part 2 gets to the root cause: the #context# scarcity. And distributed data management is the missing piece. Watch the full version on🔗

# Useful but Little-Known Features of ADK 2.0 🌍 What if your agent could remember conversations from last week and use that context to give better answers today? ADK 2.0's Memory feature provides agents with long-term knowledge that persists across sessions. It stores past conversations and learned information, retrieving them when needed. 📌 Title: Memory 🔗 URL: 🧩 Overview Unlike State, Memory manages long-term knowledge that spans across sessions. Three memory service implementations are available: InMemoryMemoryService for development and testing, VertexAiMemoryBankService for production with semantic search, and VertexAiRagMemoryService for vector-based RAG. Two built-in tools handle retrieval: PreloadMemory (auto-loads at session start) and LoadMemory (loads on demand). For programmatic access, use tool_context.search_memory(). You can also combine multiple memory services through custom tools. 🛠 How to use it Set up a memory service and add memory tools to your agent. ```python from google.adk.memory import InMemoryMemoryService from import PreloadMemory, LoadMemory # Development: in-memory implementation memory_service = InMemoryMemoryService() # Add memory tools to the agent agent = Agent( name="assistant", tools=[PreloadMemory(), LoadMemory()], ... ) # Configure the runner with the memory service runner = Runner( agent=agent, memory_service=memory_service, ... ) ``` To search memory programmatically from within a tool: ```python def my_tool(query: str, tool_context: ToolContext) -> str: results = tool_context.search_memory(query="past conversations") return str(results) ``` For multiple memory sources, create custom tools that integrate them together. 🏗 Building it into production ・Prototype quickly with InMemoryMemoryService, then switch to VertexAI services for production ・Use PreloadMemory to auto-load frequently needed context and improve response quality ・Design appropriate boundaries for what gets stored in memory to prevent data bloat ・Build custom tools to integrate multiple memory sources into a comprehensive knowledge base 💡 Use cases 🧠 Generate personalized responses based on past conversation history 📚 Retain long-term memory of project discussions and decisions 🔍 Automatically retrieve relevant past interactions via semantic search 🤝 Share a knowledge base across multiple agents using memory as a common layer ⚠️ Watch out InMemoryMemoryService loses all data when the process terminates — do not use it in production. VertexAI-based services require GCP setup and configuration. As stored data grows, search latency can be affected, so plan a data management strategy for your memory stores. ✨ Memory gives your agents the ability to carry context across sessions. It's a game-changer for building agents that deliver consistently better long-term user experiences. #ADK# #AIAgent#