adk-redis examples
Complete examples for every adk-redis capability.
The adk-redis repository includes ten complete examples, each focused on a specific capability. The seven below run on supported backends; the remaining three are listed under Deprecated backend examples.
Prerequisites
All examples require:
- Python 3.10+
- Redis 8.4+:
docker run -d --name redis -p 6379:6379 redis:8.4-alpine - A memory backend (for memory examples): a Redis Agent Memory store on Redis Cloud or self-managed.
- API keys: Most examples need a
GOOGLE_API_KEYfor Gemini
managed_memory_quickstart
Backend: redis-agent-memory · Run: python main.py
The smallest memory example, and the counterpart to simple_redis_memory. Uses redis-agent-memory, so there is no Agent Memory Server and no Docker to set up. Wires RedisSessionMemoryService and RedisLongTermMemoryService to an agent with ADK's built-in preload_memory and load_memory tools.
travel_agent_memory_tools
Backend: redis-agent-memory, switchable · Run: adk web .
Uses REST-based memory tools exclusively, without framework-managed services. The LLM has full control over when to search, create, update, and delete memories. Set REDIS_MEMORY_BACKEND to switch backends.
redis_search_tools
Capability: Vector, text, and range search · Run: adk web .
Three in-process RedisVL search tools plugged into a single agent with a product catalog dataset.
redis_sql_search
Capability: SQL SELECT search · Run: adk web .
A 10-product catalog with the RedisSQLSearchTool. The agent emits parameterized SQL (WHERE category = 'electronics' AND price < :max_price) to answer structured catalog questions. Requires pip install 'adk-redis[sql]'.
redisvl_mcp_search
Capability: RedisVL MCP server via ADK's McpToolset · Run: adk web .
The MCP counterpart of redis_search_tools. A rvl mcp server hosts a knowledge-base index in hybrid (vector + BM25) mode and the agent connects via ADK's native McpToolset. No adk-redis wrapper involved; the standard McpToolset + StdioConnectionParams pattern is used.
semantic_cache
Capability: Local semantic caching (RedisVL) · Run: python main.py
Demonstrates LLM response caching and tool result caching using the RedisVLCacheProvider with local embeddings and ADK callbacks.
langcache_cache
Capability: Managed semantic caching (LangCache) · Run: python main.py
Uses the managed LangCache service for semantic caching with server-side embeddings. No local vectorizer required.
Running an example
Examples marked python main.py run as scripts. Examples marked adk web .
run in the ADK developer UI from inside the example directory.
pip install adk-redis[all]
cd examples/managed_memory_quickstart
export GOOGLE_API_KEY=your-key
python main.py
Deprecated backend examples
Three examples are written against the deprecated opensource-agent-memory
backend: simple_redis_memory, travel_agent_memory_hybrid, and
fitness_coach_mcp. They are listed under
Agent Memory Server (deprecated).
For a redis-agent-memory starting point, use managed_memory_quickstart above.
More info
- Car dealership tutorial: Full walkthrough building an agent from scratch
- adk-redis README: Installation and overview