Integrate Redis with Google ADK for persistent sessions and semantic caching.
npx skills add https://github.com/redis-developer/adk-redis --skill adk-redis
Please help me install this Agent Skill. Skill: adk-redis Source: https://github.com/redis-developer/adk-redis/tree/main Command: npx skills add https://github.com/redis-developer/adk-redis --skill adk-redis
This Skill provides a comprehensive Redis integration for Google's Agent Development Kit (ADK), enabling persistent sessions and long-term memory, RedisVL-based search tools, MCP toolsets, and semantic caching.
McpToolset.Install the adk-redis package with pip install adk-redis. Configure the backend and connect to your Redis server. Use the provided examples to integrate the skills into your ADK agent.
High-intent search queries and answers about installing and using this skill.
To add persistent memory to Google ADK agents, you can use the adk-redis integration. It enables durable conversation state and long-term memory for ADK agents by connecting to Redis or RedisVL backends.
Semantic caching in agent development stores LLM responses and tool results to optimize performance. By implementing semantic caching with Redis, duplicate or similar queries are served from cache, reducing load on the LLM and lowering latency.
Yes, you can use RedisVL search tools with Google ADK. This integration provides vector, hybrid, range, text, and SQL search capabilities over RedisVL indices, accessible directly within your ADK agent workflow.
To set up MCP toolsets for Redis in ADK, install the adk-redis package and configure your Redis connection. This provides ADK's native McpToolset with access to RedisVL indices or the Agent Memory Server.
Google ADK does not strictly require Redis for long-term agent memory, but it is necessary if you want to use the Redis Agent Memory Server or RedisVL for durable conversation state and persistent memory storage.
To integrate Redis with Google ADK, you need the adk-redis package along with dependencies including google-adk, pydantic, agent-memory-client, redisvl, and langcache to enable persistent sessions and vector search.
gotempshAutomate installation and initial configuration of a self-hosted Temps platform.
rocklambrosProfile LLM application spend from trace logs with token counts and model pricing.
ComposioHQBuild stateful AI agents with LangGraph and Composio Tool Router.
ricableManage agent memory with hybrid SQLite and AgentDB backends using HNSW indexing.