simple_redis_memory

Redis Memory Sample

This sample demonstrates the complete two-tier memory architecture using Redis Agent Memory Server with the adk-redis package:

1. RedisWorkingMemorySessionService - Session management with auto-summarization
2. RedisLongTermMemoryService - Persistent long-term memory with semantic search

Architecture

┌────────────────────────────────────────────────────────────────┐
│                          ADK Agent                             │
├──────────────────────────────┬─────────────────────────────────┤
│     TIER 1: Working Memory   │    TIER 2: Long-Term Memory     │
├──────────────────────────────┼─────────────────────────────────┤
│ • Current session messages   │ • Extracted facts & preferences │
│ • Auto-summarization         │ • Semantic vector search        │
│ • Context window management  │ • Cross-session persistence     │
│ • TTL support                │ • Recency-boosted retrieval     │
├──────────────────────────────┴─────────────────────────────────┤
│                    Agent Memory Server API                     │
├────────────────────────────────────────────────────────────────┤
│                          Redis 8.4                             │
└────────────────────────────────────────────────────────────────┘

Prerequisites

• Python 3.10+
• Docker (for Redis 8.4 and Agent Memory Server)

Setup

1. Install uv and dependencies

First, install uv if you haven't already:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or with pip
pip install uv

Then install the package with all dependencies:

uv pip install "adk-redis[all]"

2. Start Redis 8.4

Option A: Automated setup (recommended)

# Run from the repository root
./scripts/start-redis.sh

This script will automatically start Redis 8.4 with health checks and verify it's running correctly.

Option B: Manual setup

docker run -d --name redis -p 6379:6379 redis:8.4-alpine

Verify Redis is running:

docker ps | grep redis
# Or test the connection
docker exec redis redis-cli ping
# Should return: PONG

Note: Redis 8.4 includes the Redis Query Engine (evolved from RediSearch) with native support for vector search, full-text search, and JSON operations. Docker will automatically download the image (~40MB) on first run.

3. 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-gemini-api-key \
  -e GENERATION_MODEL=gemini/gemini-2.0-flash \
  -e EMBEDDING_MODEL=gemini/text-embedding-004 \
  -e FAST_MODEL=gemini/gemini-2.0-flash \
  -e SLOW_MODEL=gemini/gemini-2.0-flash \
  -e EXTRACTION_DEBOUNCE_SECONDS=5 \
  redislabs/agent-memory-server:0.13.2 \
  agent-memory api --host 0.0.0.0 --port 8088 --task-backend=asyncio

Configuration Options:

• LLM Provider: Agent Memory Server uses LiteLLM and supports 100+ providers (OpenAI, Gemini, Anthropic, AWS Bedrock, Ollama, etc.). Set the appropriate environment variables for your provider (e.g., GEMINI_API_KEY, GENERATION_MODEL=gemini/gemini-2.0-flash). See the Agent Memory Server LLM Providers docs for details.
• Memory Extraction Debounce: EXTRACTION_DEBOUNCE_SECONDS controls how long to wait before extracting memories from a conversation (default: 300 seconds). Lower values (e.g., 5) provide faster memory extraction, while higher values reduce API calls.
• Embedding Models: Agent Memory Server also uses LiteLLM for embeddings. For local/offline embeddings, use Ollama (e.g., EMBEDDING_MODEL=ollama/nomic-embed-text, REDISVL_VECTOR_DIMENSIONS=768). Note: The redis/langcache-embed-v1 model used in the semantic_cache example is not supported by Agent Memory Server (it's RedisVL-specific). See Embedding Providers docs for all options.

4. Verify Setup

curl http://localhost:8088/v1/health

5. Configure Environment

Create .env in this directory:

GOOGLE_API_KEY=your-google-api-key
REDIS_MEMORY_SERVER_URL=http://localhost:8088
REDIS_MEMORY_NAMESPACE=adk_agent_memory
REDIS_MEMORY_EXTRACTION_STRATEGY=discrete
REDIS_MEMORY_CONTEXT_WINDOW=8000
REDIS_MEMORY_RECENCY_BOOST=true

Usage

Run the web server:

cd examples/simple_redis_memory
uv run python main.py

Open http://localhost:8080 in your browser.

Note: This project uses uv for dependency management. If you prefer to use pip, install the package first: uv pip install "adk-redis[all]" and then run python main.py.

Demo Script

Run the interactive demo to see memory in action:

uv run python demo_conversation.py

This will:

1. Create a session and share personal information
2. Wait for memory extraction
3. Create a NEW session and test memory recall

Test Conversation

Session 1 - Share information:

User: Hi, I'm Nitin. I'm a Machine Learning Engineer working on ML projects.
User: I love coffee, especially Berliner Frühstück Coffee from Berliner Kaffeerösterei. 
User: My favorite programming language is Python.

Session 2 - Test memory recall:

User: What do you remember about me?
User: What's my favorite coffee?

Features

Feature	Working Memory (Tier 1)	Long-Term Memory (Tier 2)
Scope	Current session	All sessions
Auto-summarization	Yes	No
Semantic search	No	Yes
Fact extraction	Background	Persistent
TTL support	Yes	No

Configuration

Variable	Default	Description
REDIS_MEMORY_SERVER_URL	http://localhost:8088	Memory server URL
REDIS_MEMORY_NAMESPACE	adk_agent_memory	Namespace for isolation
REDIS_MEMORY_EXTRACTION_STRATEGY	discrete	discrete, summary, preferences
REDIS_MEMORY_CONTEXT_WINDOW	8000	Max tokens before summarization
REDIS_MEMORY_RECENCY_BOOST	true	Boost recent memories in search