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add-agent-memory

Wire Redis Agent Memory into an existing agent with redis-iris so it remembers users across sessions. Asks five questions (what to remember, what to share, what never to keep, how long to keep it, whether users can be forgotten), writes the answers to service.yaml, provisions the service from the terminal with `iris agent-memory service create`, then wires recall and write-back into the agent's turn in any framework. Use when the agent forgets users, uses InMemoryStore or a checkpointer only, keeps memory in a dict or local DB, or the user asks for long-term memory, recall, or right-to-erasure.

v1.0.0New

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npx @skills-hub-ai/cli install redis-add-agent-memory

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~/.claude/skills/<skill>/SKILL.md

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npx @skills-hub-ai/cli install redis-add-agent-memory --target claude-code

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add-agent-playbooksAdd Redis Iris Agent Playbooks to an existing agent. Use when approved answers or repeatable procedures live in prompts, document retrieval, or a semantic response cache. Connect or provision a Playbook, publish FAQs and Skills, and verify the first application turn with optional review proposals.0 installs redis-connectionsRedis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), batching commands for throughput, eliminating per-request connection creation, iterating large keyspaces with SCAN, enabling client-side caching for read-heavy workloads, or setting connect and read timeouts.0 installs iris-developmentIris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.0 installs redis-docs-mcpWorking with the redis-docs MCP server (search / fetch / ask at redis.io/mcp) — how its index is shaped and how to read what it returns. Use when those tools are available and you are about to look something up in the Redis documentation, or when a result is confusing — fetch returned one short section instead of a page, several hits share one url, ids look like #sec:w1, metadata.version reads 1.0.0, a search came back empty, or ask was rate-limited or said the docs do not cover it. Also covers what redis.io does and does not index.0 installs add-context-gatewayPut the MCP servers an existing agent uses behind one Redis Iris Context Gateway with redis-iris, so the agent connects to one MCP endpoint with one agent key instead of configuring each server and its secret. Asks whether the Gateway is in Redis Cloud or self-managed, then five questions (which MCP servers and secrets the agent has today, how many tools it sees, which read-only tools may be cached, which tools return large results, whether the framework speaks MCP itself). Self-managed, it provisions the Gateway and its Upstreams from the terminal with `iris context-gateway` and sets cache and compression policies. In Redis Cloud the developer creates them in the console. Then it points the agent at the Gateway in any framework and verifies it with `tools list` and `tools call`. Use when the agent configures several MCP servers one by one, carries one secret per server, loads too many tool schemas into the prompt, or the user asks for Context Gateway, one MCP endpoint, tool caching, o0 installs redis-query-engineRedis Query Engine (RQE) guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR), DIALECT 2 query syntax, efficient FT.SEARCH and FT.AGGREGATE queries, zero-downtime index updates via aliases, and the SKIPINITIALSCAN option. Use when defining a search index on Hash or JSON documents, picking between TEXT and TAG for filtering, writing FT.SEARCH queries with filters and SORTBY, managing or swapping indexes in production, or troubleshooting slow searches with FT.PROFILE.0 installs

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redis-observabilityRedis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use when setting up monitoring or alerts for a Redis instance, diagnosing a performance regression, profiling a slow FT.SEARCH query, or wiring Redis metrics into Prometheus, Datadog, or similar.2 installs redis-clusteringRedis Cluster and replication guidance covering hash tags for multi-key operations, avoiding CROSSSLOT errors, and reading from replicas to scale read-heavy workloads. Use when designing keys for a sharded Redis Cluster, debugging CROSSSLOT errors on MGET / SDIFF / pipelines, configuring a multi-key transaction in a cluster, or routing reads to replicas for caches, analytics, or dashboards.2 installs redis-coreCore Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.2 installs redis-vector-searchRedis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.2 installs add-intent-routerReplace an existing agent's hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example phrasings and a score threshold. Asks five questions (what the routes are today, a few real phrasings per route, what each route dispatches to, what happens when nothing matches, which embedding model), writes the answers to intents.yaml, provisions the Router from the terminal with `iris intent-router service create`, loads the intents with `iris intent-router intents add`, then wires `router.match()` where the agent decides today. Use when the agent routes with keywords or an extra LLM call, or the user asks for intent detection, intent routing, or semantic routing.0 installs add-redis-irisAdd Redis Iris to an existing AI agent in any framework (LangGraph, LangChain, OpenAI Agents SDK, Google ADK, Pydantic AI, CrewAI, or a plain loop). Use when the user asks to add Iris, Redis Agent Memory, Agent Playbooks, Context Gateway, Intent Router, or Lens to their agent, or asks which Iris product their agent needs. Detects the framework and routes to the product skill.0 installs

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Frequently asked questions about add-agent-memory

What does the add-agent-memory skill do?

How do I install the add-agent-memory skill?

Run `npx @skills-hub-ai/cli install redis-add-agent-memory` from your terminal. The CLI writes the SKILL.md to the correct location for your AI tool (e.g. ~/.claude/skills/redis-add-agent-memory/ for Claude Code or ~/.cursor/skills/ for Cursor with --target cursor) and adds it to your project's .skills.json lockfile.

Which AI tools does add-agent-memory work with?

add-agent-memory runs in Claude Code. It follows the open Agent Skills standard (SKILL.md), so the same skill works in every supported tool without modification.

Is the add-agent-memory skill free?

Yes. Every skill on skills-hub.ai is free and open-source. There are no premium tiers, paywalls, or usage limits. You only pay for whatever AI assistant you're already using.

How do I use add-agent-memory after installing it?

In Claude Code, type `/redis-add-agent-memory` (or whatever slash command the skill registers) and the AI follows the skill's instructions immediately. You can also reference it by name in natural language, your AI loads the skill into context when relevant.

Can I share the add-agent-memory skill with my team?

Yes. Commit your project's .skills.json lockfile and teammates run `npx @skills-hub-ai/cli install` (no args) to install every skill at the exact version you pinned. Organization-scoped installs work via skills-hub.ai organizations.