/amazon-elasticache

Framework Integration Guide

How to connect popular AI/ML frameworks to ElastiCache Valkey. This file covers framework-specific wiring only. For full implementation patterns, see the dedicated guides linked in each section.

1. Strands Agents

Package: strands-valkey-session-manager (community package, v0.1.0+ — MIT license, maintained by jeromevdl)

Import: from strands_valkey_session_manager import ValkeySessionManager

Implements Strands' SessionManager interface. Persists conversation messages, agent state, and session metadata to Valkey automatically. Serverless OK.

For full setup code and key design patterns, see session-store.md.

Strands does not include a built-in semantic cache. Wrap the agent call with cache check/store logic using the approach in semantic-cache.md.

2. mem0

Package: mem0

Native Valkey vector store provider (provider: "valkey"). Handles index creation, embedding storage, and similarity search internally. Requires node-based Valkey 8.2 or later (recommend 9.0).

Key wiring points:

• Use valkeys:// URL scheme (the s enables TLS). Port 6379 for node-based.

• The llm block is required for mem0's fact extraction. Use Bedrock:

  "llm": {
      "provider": "aws_bedrock",
      "config": {
          "model": "us.anthropic.claude-sonnet-4-6-v1:0",
          "max_tokens": 512,
      }
  }

• Always pass a user_id to memory.add() and memory.search() for user-scoped memory isolation.

• Key config fields: embedding_model_dims (e.g., 1024 for Titan V2) and index_type (flat or hnsw).

For full mem0 config, HNSW parameters, short/long-term memory patterns, and identity model, see agent-memory.md. For mem0 embedder configs per provider, see embedding-providers.md.

3. LangChain / LangGraph

Package: langgraph-checkpoint-aws (install with pip install 'langgraph-checkpoint-aws[valkey]')

Checkpointing (ValkeySaver)

Persist LangGraph agent state across invocations.

from langgraph_checkpoint_aws import ValkeySaver

with ValkeySaver.from_conn_string(
    "valkeys://your-cluster.serverless.use1.cache.amazonaws.com:6379",
    ttl_seconds=3600,
) as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)
    config = {"configurable": {"thread_id": "session-1"}}
    result = graph.invoke({"messages": [HumanMessage(content="Hello")]}, config)

LLM Caching (ValkeyCache)

Exact-match caching of LLM responses (no vector search needed, works on serverless).

from langgraph_checkpoint_aws import ValkeyCache

cache = ValkeyCache.from_conn_string(
    "valkeys://your-cluster.serverless.use1.cache.amazonaws.com:6379",
    prefix="llm_cache:",
    ttl=3600,
)

Use valkeys:// URL scheme for TLS.

Semantic Caching (ValkeyStore)

Vector-based semantic caching of LLM responses (requires node-based Valkey 8.2 or later; recommend 9.0).

from langgraph_checkpoint_aws import ValkeyStore, ValkeyIndexConfig

index_config = ValkeyIndexConfig(
    collection_name="semantic_cache",
    embed=embeddings,
    fields=["query"],
    index_type="HNSW",
    dims=1024,
)

store = ValkeyStore.from_conn_string(
    "valkeys://your-cluster.cache.amazonaws.com:6379",
    index=index_config,
)
store.setup()

Unlike ValkeyCache (exact-match), ValkeyStore uses vector search to match semantically similar queries. For full implementation, see semantic-cache.md.

4. ElastiCache TLS Connection Reference

All frameworks must use TLS when connecting to ElastiCache.

Port Reference

Security group note: For serverless caches, your VPC security group must allow inbound TCP on both port 6379 (primary) and port 6380 (reader). If you only open 6379, reader-endpoint connections will fail silently.

For raw valkey-py and valkey-glide connection examples, see elasticache-search.md. For IAM authentication setup, see the setup sub-skill (references/setup/auth-model-selector.md).