Redis with Google Agent Development Kit (ADK)

Build AI agents with persistent memory, semantic search, and caching using Redis and Google ADK.

Google Agent Development Kit (ADK) provides clean abstractions for building AI agents: interfaces for memory, sessions, tools, and callbacks. adk-redis implements these interfaces using Redis, giving agents persistent two-tier memory, semantic search for RAG, and response caching without requiring changes to agent logic.

Architecture

adk-redis connects three backend systems to the ADK framework:

  • Redis Agent Memory Server handles working memory (sessions), long-term memory (extracted facts), auto-summarization, and memory search.
  • RedisVL (Redis Vector Library) powers the search tools and local semantic cache provider.
  • LangCache provides managed semantic caching with server-side embeddings.

Prerequisites

  • Redis 8.4+ with vector search support
  • Agent Memory Server for memory and session services
# Start Redis
docker run -d --name redis -p 6379:6379 redis:8.4-alpine

# Start Agent Memory Server
docker run -d --name agent-memory-server -p 8088:8088 \
  -e REDIS_URL=redis://host.docker.internal:6379 \
  -e GEMINI_API_KEY=your-key \
  -e GENERATION_MODEL=gemini/gemini-2.0-flash \
  -e EMBEDDING_MODEL=gemini/text-embedding-004 \
  redislabs/agent-memory-server:latest \
  agent-memory api --host 0.0.0.0 --port 8088 --task-backend=asyncio

Installation 

# Memory and session services (requires Agent Memory Server)
pip install adk-redis[memory]

# Search tools via RedisVL
pip install adk-redis[search]

# Managed semantic caching via LangCache
pip install adk-redis[langcache]

# Everything
pip install adk-redis[all]

Quick start

Wire up Redis Agent Memory in a few lines:

from google.adk import Agent
from google.adk.agents.callback_context import CallbackContext
from google.adk.runners import Runner
from adk_redis.sessions import (
    RedisWorkingMemorySessionService,
    RedisWorkingMemorySessionServiceConfig,
)
from adk_redis.memory import (
    RedisLongTermMemoryService,
    RedisLongTermMemoryServiceConfig,
)

session_service = RedisWorkingMemorySessionService(
    config=RedisWorkingMemorySessionServiceConfig(
        api_base_url="http://localhost:8088",
        default_namespace="my_app",
    )
)
memory_service = RedisLongTermMemoryService(
    config=RedisLongTermMemoryServiceConfig(
        api_base_url="http://localhost:8088",
        default_namespace="my_app",
    )
)

async def after_agent(callback_context: CallbackContext):
    await callback_context.add_session_to_memory()

agent = Agent(
    name="my_agent",
    model="gemini-2.5-flash",
    instruction="You are a helpful assistant with long-term memory.",
    after_agent_callback=after_agent,
)

runner = Runner(
    agent=agent,
    app_name="my_app",
    session_service=session_service,
    memory_service=memory_service,
)

Capabilities

Capability Description Page
Redis Agent Memory Working and long-term memory via framework services, REST tools, or MCP Redis Agent Memory
Integration patterns Framework-managed, LLM-controlled REST, and MCP tools Integration patterns
Search tools Vector, hybrid, text, and range search via RedisVL Search tools
Semantic caching LLM response and tool result caching Semantic caching
Examples Seven complete examples covering all capabilities Examples

More info

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