skills/iris-development/SKILL.md
Iris 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.
npx skillsauth add redis/agent-skills iris-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Iris is the umbrella brand for Redis's AI-focused products. This skill currently covers one product in that family: Redis Agent Memory (RAM) — the persistent memory layer for AI agents, delivered as a managed service on Redis Cloud. Additional Iris products will be added as separate sections when they ship.
Redis Agent Memory exposes a REST/JSON data-plane API with two memory tiers:
A background promotion worker — managed by Redis Cloud — extracts durable facts from session events and writes them into long-term memory.
All code samples use the official SDKs:
| Language | Package | Class | Install |
| ---------- | -------------------------- | ------------- | ---------------------------------- |
| Python | redis-agent-memory | AgentMemory | pip install redis-agent-memory |
| TypeScript | @redis-iris/agent-memory | AgentMemory | npm add @redis-iris/agent-memory |
Both SDKs read the bearer token from AGENT_MEMORY_API_KEY and the default store ID from AGENT_MEMORY_STORE_ID. The production data-plane URL is https://gcp-us-east4.memory.redis.io; the exact URL for your service is also shown in the Cloud console after provisioning.
Reference these guidelines when:
AgentMemory.add_session_event(...) / addSessionEvent(...)search_long_term_memory(...) / searchLongTermMemory(...)| Priority | Category | Impact | Prefix |
| -------- | ----------------------- | ------ | ------------ |
| 1 | Setup & Cloud Service | HIGH | setup- |
| 2 | Session Memory / Events | HIGH | session- |
| 3 | Long-Term Memory | HIGH | ltm- |
| 4 | Memory Promotion | MEDIUM | promotion- |
setup-cloud-service - Create a Memory service on Redis Cloudsetup-auth-token - Authenticate the SDK with a store API keysession-when-to-use - Choose session events vs direct long-term memorysession-add-event - Append a session event correctlysession-retrieval - Retrieve session memory and individual eventsltm-bulk-create - Create long-term memories in bulk with idempotent IDsltm-search - Search long-term memory semantically with filtersltm-organize - Organize records with namespace, ownerId, topics, and memoryTypepromotion-overview - How background promotion worksRead individual rule files under references/ for detailed explanations and code examples:
references/setup-cloud-service.md
references/session-add-event.md
references/promotion-overview.md
Each rule file contains:
development
Redis 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.
development
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.
testing
Redis security guidance covering authentication (requirepass and ACL users), TLS, ACL-based least-privilege access control, restricting network exposure via bind and protected-mode, firewall rules, and disabling dangerous commands. Use when deploying Redis to production, defining ACL users for an application, configuring TLS connections, locking down a Redis instance behind a firewall, or auditing a Redis deployment for security hardening.
testing
Redis 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.