AutoGineer
Generated banner for Amazon Elasticache
Generated logo for Amazon Elasticache

Amazon Elasticache

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts
Download ZIP

From the author

Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Amazon ElastiCache skill provides a structured, modular approach to managing in-memory data stores on AWS. It guides developers through the entire lifecycle of ElastiCache and Valkey, from initial requirements gathering and provisioning to complex data modeling, AI-driven vector search, and operational monitoring. The skill enforces best practices for connection safety, security, and infrastructure-as-code, ensuring reliable performance for caching, session management, and GenAI workloads.

What it does

  • Assesses workload requirements to recommend optimal ElastiCache configurations.
  • Automates provisioning and connectivity setup for serverless and node-based clusters.
  • Provides implementation patterns for common use cases like cache-aside, session stores, and rate limiting.
  • Supports advanced AI workloads including semantic caching, RAG, and vector similarity search.
  • Delivers operational diagnostics for performance tuning, cost optimization, and troubleshooting.
  • Manages migration paths from self-managed Redis or between different deployment models.

When to use it

  • When you need to reduce database load or improve API response times with an in-memory cache.
  • When implementing AI agent memory, RAG retrieval, or semantic caching for LLM applications.
  • When migrating existing Redis workloads to a managed AWS environment.
  • When troubleshooting performance issues like high latency, replication lag, or memory pressure.
  • When setting up secure, production-ready caching infrastructure using IaC.

Advantages

  • Standardizes deployment and operational patterns across development teams.
  • Ensures compliance with security best practices like TLS and IAM authentication.
  • Reduces configuration errors through automated input validation and tagging.
  • Provides clear guidance on engine selection and version-specific feature availability.
  • Simplifies complex tasks like cross-region migration and vector search setup.

Usage examples

“I need to speed up my RDS database. Should I use ElastiCache?”
The agent will initiate the requirements sub-skill to analyze your workload, stack, and latency needs, then provide a recommendation on whether ElastiCache is the right fit.
“How do I set up a Valkey cluster for vector search?”
The agent will route to the genai sub-skill, verify you are using a node-based Valkey 8.2+ cluster, and provide the necessary configuration and code to enable vector similarity search.
“My cache hit rate is low and latency is spiking. Can you help me troubleshoot?”
The agent will activate the monitoring sub-skill to analyze your metrics, identify potential hot keys or memory imbalance, and suggest specific remediation steps.
“Help me migrate my self-managed Redis to ElastiCache.”
The agent will guide you through the migration sub-skill, performing preflight checks and validating your source configuration for a zero-downtime cutover.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/amazon-elasticache/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amazon-elasticache.zip -d .agents/skills

Documentation by AWS

ElastiCache

A modular ElastiCache toolkit organized as a registry of sub-skills. Each sub-skill handles one domain of ElastiCache work. The router below matches user intent to the right sub-skill, then loads only the references needed for that sub-skill.

How this skill works

  1. Match the user's request against the semantic categories in the registry below. Match on meaning, not exact wording ("help me figure out which data structures to use" matches data-modeling even without the word "pattern").

  2. Disambiguation: If the user's intent matches multiple sub-skills, apply these rules in order:

    • If .elasticache/requirements.json exists with infrastructure.endpoint set, prefer monitoring or data-modeling (the user has an existing cache).

    • If no cache exists (no requirements.json or no endpoint), prefer requirements.

    • If still ambiguous, ask one clarifying question: "Are you looking to set up something new, or troubleshoot something existing?"

  3. Check the Guardrails section before recommending an engine or deployment model.

  4. Read references/{sub-skill-id}/instructions.md for the matched sub-skill. If the file is not found at a relative path, check your prompt or environment for the skill directory absolute path and retry with {skill-directory}/references/{sub-skill-id}/instructions.md.

  5. If the request spans multiple sub-skills, execute them in pipeline order.

  6. If a sub-skill requires upstream context (engine, deployment model, endpoint) not yet in session memory, route to the upstream sub-skill first.

  7. If no sub-skill matches, activate requirements first.

  8. If a script or CLI call fails, show the error to the user and suggest a specific fix before retrying.

Sub-skill registry

Each entry has: an ID (directory name under references/), a domain description, semantic categories for matching, and upstream/downstream dependencies.

IDNameDomainSemantic CategoriesUpstreamDownstream
requirementsSolution FitGathers workload, stack, scale, latency, persistence, and budget through workspace scan + structured interview. Decides whether ElastiCache is the right service and hands off with a routing recommendation.I need a cache, speed up my app, reduce database load, lower Bedrock cost, should I use ElastiCache, what's best for my workload, evaluating cache options, ElastiCache vs X, Valkey vs X, vague new workload—setup, data-modeling, genai, monitoring, migration
setupCreate and ConnectProvisioning, connectivity, security, authentication, IaC, deployment choice. Gets the user to a working cache with least friction. Covers engine selection, serverless vs node-based, VPC, TLS, RBAC/IAM, jump-host/SSM tunnels, CLI/SDK/CFN/CDK/Terraform starters.create a cache, set up ElastiCache, provision, Valkey cluster, connect Lambda/ECS/EKS/EC2, VPC, security groups, TLS, RBAC, IAM auth, jump host, SSM tunnel, CloudFormation, CDK, Terraform, engine selection, serverless vs node-based, backup, snapshot, restore, exportrequirements (optional)data-modeling, genai, monitoring
data-modelingApplication PatternsPicks data structures, key schema, TTL strategy, invalidation approach, and client code for non-AI patterns: cache-aside, session store, rate limiting, leaderboards, counters, pub/sub, streams, shopping carts, job queues, activity feeds.session store, rate limiting, leaderboard, cache-aside, query caching, counters, streams, pub/sub, shopping cart, job queue, activity feed, key schema, TTL, invalidation, data structuressetup (cache must exist)monitoring
genaiAI and Vector WorkloadsClassifies request into Mode 1 (plain cache), Mode 2 (semantic response cache), or Mode 3 (full vector search). Selects Valkey and forces node-based Valkey 8.2 or above (recommend 9.0) when server-side vector similarity is needed. Covers semantic caching, agent memory, RAG retrieval, recommendation, personalization, conversation/session persistence for AI agents, and framework wiring (Strands, mem0, LangChain).semantic cache, RAG, agent memory, conversational memory, vector search, embeddings, recommendation, personalization, Bedrock latency, Bedrock cost, LLM caching, Strands, mem0, LangChain, conversation history, AI session store, embedding provider, framework integrationsetup (cache must exist)monitoring
monitoringOperate and ObserveDiagnoses performance, cost, and reliability using metrics first, then recommends the smallest change. Covers dashboards, alarms, log delivery, cost reporting, event routing, troubleshooting high CPU / memory / replication lag / connection spikes / low hit rate / hot keys / big keys / slot imbalance / latency spike root cause.cache is slow, cost too high, hit rate low, high CPU, memory pressure, replication lag, connection spikes, dashboards, alarms, CloudWatch, cost comparison, troubleshoot, hot key, uneven shard load, one node pinned, big key, memory bloat, which key is biggest, keyspace distribution, prefix analysis, cost attribution by tenant, memory imbalance, one shard full, slot memory skew, latency spike, slow command incident, root cause for latency bump—setup, migration
migrationEngine and Platform MigrationSelects the migration path and sequences preflight, validation, cutover, and rollback. Covers self-managed Redis → ElastiCache, Redis OSS → Valkey, node-based ↔ serverless, version upgrades. Hard validate-before-migrate gate.migrate, Redis OSS to Valkey, self-managed to ElastiCache, node-based to serverless, serverless to node-based, engine upgrade, version upgrade, zero-downtime cutover, rollback—setup, monitoring

Pipeline order

Sub-skills run independently, but common multi-step journeys follow these pipelines:

  • requirements → setup → (data-modeling | genai) → monitoring

  • migration → setup → monitoring

  • monitoring → setup | migration (if metrics indicate)

State handoff: requirements.json

.elasticache/requirements.json is the single source of truth for cross-sub-skill state. Each sub-skill reads it at start and writes its section after completing work. Read before writing; merge, do not overwrite.

SectionOwnerKey fields
top-levelrequirementsengine, deployment_model, region, runtime, patterns, use_case, vpc_id, subnet_ids, security_group_ids
infrastructuresetupcache_name, resource_id, engine_version, topology, endpoint, port, auth_model, tls, client_library, execution_path, access_mode, tunnel_instance_id, embedding_provider, embedding_model, embedding_dim, embedding_module
genaigenaimode, mode_2_path, framework
migrationmigrationsource_type, source_host, migration_path, cutover_status

Ownership note: deployment_model is set by requirements during initial interview. migration may update it after an engine or deployment model switch (e.g., node-based to serverless).

requirements.json should include "schema_version": 1 and "last_updated": "<ISO timestamp>" at the top level. Every sub-skill that writes to requirements.json must update last_updated. If last_updated is older than 7 days, warn the user that cached state may be stale.

requirements.json tracks one active cache. If the user works with multiple caches in the same project, confirm which cache is active before reading or writing state.

When a sub-skill needs upstream context (engine, endpoint, auth model), check requirements.json first. If the field is null or the file does not exist, route to the upstream sub-skill.

Global rules (apply to every sub-skill)

  1. Execution path. Use AWS CLI, SDK (boto3), CloudFormation, or CDK as the primary path for control-plane work. Use valkey-py as the primary path for data-plane work.

  2. Response depth. Summary (2-3 sentences) for "should I" or "which" questions. Standard (recommendation + config + code + next steps) by default. Expert (full decision matrix with alternatives, cost, security caveats) for "why" or "compare all" questions. Escalate on user request; never downgrade unprompted.

  3. Session memory. Track region, VPC, engine, deployment model, auth model, compute runtime, and language. Carry forward across sub-skills. Do not re-ask. If the user overrides a value, update it everywhere. Inferred values (from workspace scan or IaC) must be re-confirmed before high-risk decisions (engine, deployment model, security posture); low-risk inferences (language, framework, region) can be used as defaults silently.

  4. Source priority. Always answer from skill-local files first (sub-skill references, then scripts/). Do not fetch external documentation or web search unless the local files cannot answer the query. When local files are insufficient, fall back to official AWS docs: https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ for features and https://aws.amazon.com/elasticache/pricing/ for pricing. Never invent price points or version constraints. If the user references a Valkey or Redis version, feature, or pricing tier not covered in local files, fall back to https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ before answering. Do not extrapolate from local content that may be outdated.

  5. Freshness disclaimer. When outputting pricing, version constraints, or feature availability, include a one-line disclaimer: "For current pricing see https://aws.amazon.com/elasticache/pricing/. For current feature availability see https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/."

  6. Connection safety. Never create a Valkey/Redis client at module level (top of file, import time). Initialize connections inside a function or on first use. Module-level connections crash applications that import the module before the cache is reachable.

  7. Code environment. Use python3 (not python) in all commands and shebangs. Do not generate pip install or pytest commands in application code. Prefer the agent's file-writing tool over shell redirection for writing files.

  8. Input validation (write operations only). Before running any create, modify, or delete CLI command or SDK call that uses user-provided values (cache name, region, subnet IDs, security group IDs, VPC ID, KMS key, snapshot name), validate them with scripts/input_validator.py. Run python3 scripts/input_validator.py cache_name=<val> region=<val> ... or import validate_all as a library. Skip validation for read-only operations (describe, list) where bad input is harmless.

  9. Resource tagging (always apply on resource creation). When creating any cache (create-serverless-cache, create-replication-group, create-cache-cluster), ALWAYS include these tags:

    --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value={your-model-id}

    Example: --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value=claude-sonnet-4-20250514

    Set generation_model to your own model identifier. Include these tags even if the user does not mention tagging. If the user provides additional tags, append these to their tags rather than replacing them.

Reference loading

Load additional references only when the current turn's answer requires them.

On-demand pointers (not preloaded; fetch when the trigger applies):

  • references/shared-ux/production-readiness.md — when the user asks if their cache is ready for production, or after setup completes and the user wants to go to production

  • references/shared-ux/action-safety.md — before any destructive action (risk levels, never-auto-execute list)

  • references/shared-ux/error-remediation.md — when the user hits a specific ElastiCache error code (MOVED, CROSSSLOT, CLUSTERDOWN, MULTI/EXEC+IAM, etc.)

  • references/shared-foundation/boundary-doc.md — when the user asks what this skill covers

  • references/shared-foundation/attribution.md — when generating CLI commands, SDK code, or IaC templates

  • references/shared-foundation/architecture-diagrams.md — when the user asks for architecture diagrams or visual reference

  • references/shared-runtime/lambda.md — when connecting from Lambda (cold start gotchas, IAM auth code, lazy init)

  • references/shared-runtime/ecs.md — when connecting from ECS (SIGTERM shutdown, connection pool drain, task definition)

  • references/shared-runtime/eks.md — when connecting from EKS (IRSA, service mesh bypass, SecurityGroupPolicy CRD)

  • references/shared-runtime/api-gateway.md — when integrating with API Gateway (no direct path, caching layers comparison)

  • references/shared-runtime/rds-acceleration.md — when caching RDS/Aurora queries (thundering herd, stampede protection, invalidation)

  • references/shared-runtime/secret-injection.md — when the user asks about credential management per compute platform

  • references/shared-security/encryption-defaults.md — when adding encryption to an existing unencrypted cluster (TLS two-step migration, at-rest immutability)

  • references/shared-security/config-guardrails.md — when the user wants continuous compliance monitoring (AWS Config rules, custom Lambda rules)

  • references/shared-security/vpc-patterns.md — when debugging port/security-group issues (port 6380 serverless reader, anti-patterns)

Folder convention: references/ contains 10 folders. 6 match the sub-skills (requirements, setup, data-modeling, genai, monitoring, migration) and are routing destinations. The 4 shared-* folders (shared-foundation, shared-ux, shared-security, shared-runtime) are cross-cutting material loaded on demand, not routing destinations.

Guardrails

PriorityRule
CRITICALVector search MUST use node-based Valkey 8.2 or above. Serverless does NOT support vector search. Never suggest serverless for vector search. Apply this regardless of which sub-skill activates.
CRITICALDo not invent price points or version constraints. Use scripts/price_calculator.py and current AWS docs when precision matters.
HIGHDo not recommend Memcached when the user needs persistence, replication, RBAC or IAM auth, sorted sets, streams, pub/sub, or vector search.
HIGHDo not assume local laptop access works directly. ElastiCache is VPC-centric; explain VPC, tunnel, or jump-host access when needed.
STANDARDDo not trigger on every generic Redis mention. Trigger when the user is clearly asking about AWS, managed caching, migration, connectivity, pricing, operations, or AWS service integration.
STANDARDFor ambiguous "cache" requests inside AWS contexts, activate this skill and start with requirements.

Product truths

  • ElastiCache Serverless deploys in under a minute and removes infrastructure management.

  • Valkey serverless pricing is 33% lower than other supported engines; node-based Valkey pricing is 20% lower.

  • Serverless caches have in-transit encryption always enabled (cannot be disabled).

  • IAM auth is available for all ElastiCache Valkey versions (7.2 is the baseline Valkey version on ElastiCache) and Redis OSS 7.0+.

  • Valkey version ladder: 7.2 (baseline), 8.0 (20% more data per node (capacity improvement), per-slot metrics), 8.1 (Bloom filters, COMMANDLOG, SET IFEQ, 20% less memory via new hash table (efficiency improvement)), 8.2 (vector search), 9.0 (recommended default for new clusters). Recommend Valkey 9.0 for new clusters unless a specific feature dictates otherwise.

  • Vector search is available for Valkey 8.2 or above on node-based clusters (recommend 9.0).

  • Global Datastore is available for node-based clusters only. It does not support IPv6 or Local Zones. Global Datastore supports AUTH and RBAC. Cross-region failover must be promoted manually (no autofailover across regions). At-rest encryption must be enabled on all clusters in the Global Datastore, but each cluster can use a separate KMS key per region.

  • Online migration from self-managed Redis to ElastiCache requires: (source) AUTH must not be enabled, protected-mode set to no, replication and administrative commands must not be renamed (e.g., sync, psync, info, config, command, cluster); (target) encryption in-transit disabled, Multi-AZ enabled, engine version Redis OSS 5.0.6+ or Valkey 7.2+, not part of a Global Datastore, data tiering disabled. Shard counts must match between source and target. All source Redis instances must use the same port. Online migration is not supported for serverless caches (node-based targets only). See references/migration/topology-validation.md for the full checklist.

Files

references/shared-runtime/eks.md

EKS Integration with ElastiCache

VPC CNI: Pods Get VPC IPs

With the Amazon VPC CNI plugin (default on EKS), each pod receives a VPC IP address. This means pods can reach ElastiCache endpoints directly without NAT or proxies, as long as security groups allow the traffic.

Security Group Configuration

ApproachHow it works
Node security group (default)All pods on a node share the node's SG. Add an outbound rule from the node SG to the cache SG on port 6379.
Security groups for pods (recommended for production)Pods get their own SGs via the SecurityGroupPolicy CRD. Allows per-workload least-privilege access to the cache.
Node SG approach (simpler)

Add to the EKS node security group:

DirectionProtocolPortTargetPurpose
Node SG outboundTCP6379Cache SGCache endpoint (primary and reader)
Node SG outboundTCP6380Cache SGReader endpoint (serverless only)
Cache SG inboundTCP6379Node SGAllow from EKS nodes
Cache SG inboundTCP6380Node SGAllow from EKS nodes (serverless)
Security groups for pods (per-workload isolation)

First, attach the AmazonEKSVPCResourceController managed IAM policy to your EKS cluster role (required prerequisite). Then enable the VPC CNI ENABLE_POD_ENI setting and create a SecurityGroupPolicy:

# Attach the required managed policy to the cluster role
aws iam attach-role-policy \
  --role-name <your-eks-cluster-role> \
  --policy-arn arn:aws:iam::aws:policy/AmazonEKSVPCResourceController

# Enable pod ENI on the VPC CNI
kubectl set env daemonset aws-node -n kube-system ENABLE_POD_ENI=true

Then create a SecurityGroupPolicy:

apiVersion: vpcresources.k8s.aws/v1beta1
kind: SecurityGroupPolicy
metadata:
  name: cache-access
  namespace: myapp
spec:
  podSelector:
    matchLabels:
      cache-access: "true"
  securityGroups:
    groupIds:
      - sg-0123456789abcdef0  # Pod SG that allows outbound to cache

Add the label cache-access: "true" to pods that need cache access.

Secret Wiring

Recommended: IAM Roles for Service Accounts (IRSA) with IAM Auth

For IAM auth (no passwords needed), configure IRSA so the pod's service account can call elasticache:Connect. This is the preferred approach for Valkey 7.2+ / Redis OSS 7.0+ because it eliminates password management entirely.

apiVersion: v1
kind: ServiceAccount
metadata:
  name: myapp-sa
  namespace: myapp
  annotations:
    eks.amazonaws.com/role-arn: arn:aws:iam::123456789012:role/myapp-cache-role

The IAM role trust policy must allow the OIDC provider, and the role must have:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "elasticache:Connect",
      "Resource": [
        "arn:aws:elasticache:<region>:<account-id>:serverlesscache:<cache-name>",
        "arn:aws:elasticache:<region>:<account-id>:replicationgroup:<rg-id>",
        "arn:aws:elasticache:<region>:<account-id>:user:<user-id>"
      ]
    }
  ]
}

Use serverlesscache: for serverless caches, replicationgroup: for node-based clusters. Include both if the policy covers multiple deployment types.

Alternative: EKS Pod Identity with IAM Auth

EKS Pod Identity (available on EKS 1.28+ with platform version eks.4+) is a newer alternative to IRSA with simpler setup. Both work for ElastiCache IAM auth. See AWS documentation for Pod Identity configuration.

Alternative: External Secrets Operator (ESO) with RBAC Passwords

Use this when IAM auth is not available (older engine versions) or when the client library does not support IAM token generation. The External Secrets Operator syncs secrets from AWS Secrets Manager into Kubernetes secrets automatically.

Install ESO, then create an ExternalSecret:

apiVersion: external-secrets.io/v1
kind: ExternalSecret
metadata:
  name: cache-credentials
  namespace: myapp
spec:
  refreshInterval: 1h
  secretStoreRef:
    name: aws-secrets-manager
    kind: ClusterSecretStore
  target:
    name: cache-credentials
    creationPolicy: Owner
  data:
    - secretKey: username
      remoteRef:
        key: elasticache/myapp/appuser
        property: username
    - secretKey: password
      remoteRef:
        key: elasticache/myapp/appuser
        property: password
Alternative: AWS Secrets and Configuration Provider (ASCP) with RBAC Passwords

Mount secrets directly as files in the pod using the CSI Secrets Store driver with the AWS provider:

apiVersion: secrets-store.csi.x-k8s.io/v1
kind: SecretProviderClass
metadata:
  name: cache-credentials
  namespace: myapp
spec:
  provider: aws
  parameters:
    objects: |
      - objectName: "elasticache/myapp/appuser"
        objectType: "secretsmanager"
        jmesPath:
          - path: username
            objectAlias: cache-username
          - path: password
            objectAlias: cache-password
  secretObjects:
    - secretName: cache-credentials
      type: Opaque
      data:
        - objectName: cache-username
          key: username
        - objectName: cache-password
          key: password

Pod Deployment with Cache Access

Auth paths: The example below shows env vars for both IAM auth (via IRSA service account) and RBAC password auth (via Kubernetes secrets). In practice, choose one path: use serviceAccountName with IAM token generation and omit the CACHE_USERNAME/CACHE_PASSWORD secret refs for IAM auth, or use the secret refs and omit the IRSA service account annotation for RBAC password auth.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
  namespace: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
        cache-access: "true"  # For SecurityGroupPolicy
    spec:
      serviceAccountName: myapp-sa  # For IRSA (IAM auth) -- omit for RBAC password auth
      containers:
        - name: app
          image: myapp:latest
          env:
            - name: CACHE_ENDPOINT
              value: "my-cache.serverless.use1.cache.amazonaws.com"
            - name: CACHE_PORT
              value: "6379"
            # The following are for RBAC password auth only.
            # For IAM auth, generate a SigV4 token at runtime instead (see IAM Auth Token Generation below).
            - name: CACHE_USERNAME
              valueFrom:
                secretKeyRef:
                  name: cache-credentials
                  key: username
            - name: CACHE_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: cache-credentials
                  key: password

IAM Auth Token Generation

When using IRSA or EKS Pod Identity for IAM auth, the pod's service account provides AWS credentials automatically. Use them to generate a SigV4 presigned URL token:

import os
import botocore.session
from botocore.signers import RequestSigner
from botocore.model import ServiceId

def get_iam_auth_token(cache_name: str, user_id: str, is_serverless: bool = True) -> str:
    region = os.environ.get('AWS_REGION', 'us-east-1')
    session = botocore.session.get_session()
    creds = session.get_credentials().get_frozen_credentials()
    signer = RequestSigner(
        ServiceId("elasticache"), region, "elasticache", "v4",
        creds, session.get_component("event_emitter"),
    )
    query = f"Action=connect&User={user_id}"
    if is_serverless:
        query += "&ResourceType=ServerlessCache"
    url = signer.generate_presigned_url(
        {"method": "GET", "url": f"https://{cache_name}/?{query}",
         "body": {}, "headers": {}, "context": {}},
        operation_name="connect", expires_in=900, region_name=region,
    )
    return url[len("https://"):] if url.startswith("https://") else url

Pass the returned token as the password parameter when connecting, with user_id as username.

Connection Snippets

Connection code is the same as ECS. The key differences are in how secrets are injected (Kubernetes secrets vs ECS secrets) and how network access is configured (pod SG vs task SG).

Python (redis-py/valkey-py with TLS and RBAC password auth)

Note: The AWS-validated Python client is redis-py v4.1.2 (AWS-validated). The valkey-py package is the Valkey-specific fork with an API-compatible interface. Either can be used; the examples below use valkey-py but you can substitute import redis and redis.RedisCluster if preferred.

import os
import valkey

# Serverless caches operate in cluster mode enabled only.
# You MUST use a cluster-aware client.
cache = valkey.ValkeyCluster(
    host=os.environ['CACHE_ENDPOINT'],
    port=int(os.environ.get('CACHE_PORT', '6379')),
    username=os.environ.get('CACHE_USERNAME', 'appuser'),
    password=os.environ.get('CACHE_PASSWORD', ''),
    ssl=True,
    ssl_cert_reqs='required',
    decode_responses=True,
    max_connections=20,
    socket_connect_timeout=5,
    socket_timeout=5,
    socket_keepalive=True,
    retry_on_timeout=True,
    health_check_interval=30,
)
Node.js (iovalkey with TLS)
const { Cluster } = require('iovalkey');

const client = new Cluster(
  [{ host: process.env.CACHE_ENDPOINT, port: parseInt(process.env.CACHE_PORT || '6379', 10) }],
  {
    dnsLookup: (address, callback) => callback(null, address),
    slotsRefreshTimeout: 2000,
    redisOptions: {
      tls: {},
      username: process.env.CACHE_USERNAME || 'appuser',
      password: process.env.CACHE_PASSWORD,
      connectTimeout: 5000,
      commandTimeout: 5000,
      keepAlive: 30000,
    },
  }
);

Service Mesh Considerations

If using AWS App Mesh or Istio:

  • Envoy intercepts all outbound TCP by default. Envoy sidecar proxies intercept all outbound TCP traffic by default via iptables rules. ElastiCache/Redis traffic is passed through as opaque TCP (not HTTP-routed). This can cause issues with MOVED redirects in cluster mode and TLS termination. Consider excluding ElastiCache ports from sidecar interception if you experience connectivity issues.

  • mTLS: The mesh's mTLS does not apply to cache traffic. ElastiCache has its own TLS (always-on for serverless). Do not double-TLS.

  • Timeouts and retries: Configure these in your application client, not in the mesh, since the mesh does not manage the cache protocol.

  • Port exclusion: If you experience connectivity issues, add an outbound exclusion for the cache endpoint IP range or port 6379 (and port 6380 for serverless reader endpoint) in the mesh configuration.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

  • References to credential files
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/database-skills/amazon-elasticache · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by AWS or AWS.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download amazon-elasticache.zip