Redis Agent Skills is Redis's collection of packaged instructions and resources that teach AI coding agents how to work with Redis data structures, connections, search, caching, clustering, security, observability, and agent memory. It is for developers using coding agents to build or troubleshoot Redis-backed applications. The catalogue entries are the project's own agent skills, instructions, and plugin packaging.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add redis/agent-skills --skill iris-developmentgit clone --depth 1 https://github.com/redis/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/redis/agent-skills/iris-development)<a href="https://agentmods.dev/skills/redis/agent-skills/iris-development"><img src="https://agentmods.dev/badge/skills/redis/agent-skills/iris-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/redis/agent-skills/iris-development"><img src="https://agentmods.dev/badge/skills/redis/agent-skills/iris-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.00971 |
| Opus 5.5 | $0.00036 | $0.00388 |
| Sonnet 5.5 | $0.00018 | $0.00194 |
| Haiku 4.5 | $0.00009 | $0.00097 |
Grade A, and why
iris-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iris: Redis Agent Memory
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:
- Session memory — append-only conversation history per session (working memory).
- Long-term memory — semantically searchable records extracted from sessions (or created directly).
A background promotion worker — managed by Redis Cloud — extracts durable facts from session events and writes them into long-term memory.
Official SDKs
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.
When to Apply
Reference these guidelines when:
- Creating a memory service on Redis Cloud (https://cloud.redis.io/#/agent-memory)
- Wiring an agent to call
AgentMemory.add_session_event(...)/addSessionEvent(...) - Searching long-term memory with
search_long_term_memory(...)/searchLongTermMemory(...) - Choosing between session events and direct long-term memory writes
Rule Categories by Priority
| 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- |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ltm-bulk-create.md 3.9 KB
- references/ltm-organize.md 4.5 KB
- references/ltm-search.md 4.1 KB
- references/promotion-overview.md 4.3 KB
- references/session-add-event.md 4.9 KB
- references/session-retrieval.md 3.6 KB
- references/session-when-to-use.md 3.4 KB
- references/setup-auth-token.md 3.1 KB
- references/setup-cloud-service.md 2.8 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 92 lines · 90 tokens per session scan A bdfc8aa28c93
iris-development is a skill published in the GitHub repository redis/agent-skills (167 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 971 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
tanstack-ai-memory-redis
Use when wiring redis() from @tanstack/ai-memory/redis in production — covers client setup (ioredis or node-redis via fromNodeRedis), the storage model, client-side ranking limits, and troubleshooting.
redis-js
Work with the Upstash Redis JavaScript/TypeScript SDK for serverless Redis operations. Use for caching, session storage, rate limiting, leaderboards, full-text search (querying, filtering, aggregating with @upstash/redis search extension), and all Redis data structures. Supports automatic serialization/deserialization…
redis
Use when using Redis or any Redis-protocol store (Valkey, ElastiCache, Upstash, Dragonfly, Memorystore) as a cache, queue, rate limiter or distributed lock and it has to be CORRECT rather than merely connected — stampede-proof caching, locks that cannot release someone else's hold, race-free rate limits, and jobs that…
hermes-mnemosyne
Mnemosyne is Hermes' primary local-first memory engine — SQLite with vector + FTS5 hybrid search, 19+ tools, auto-consolidation, and a standalone CLI. It's a pip-installed plugin (not a built-in toolset) discovered via $HERMESHOME/plugins/mnemosyne/.
chroma-memory
Skill "chroma-memory" from iPythoning/b2b-sdr-agent-template, covering chroma-memory — per-turn conversation memory with chromadb, commands, usage, store a turn (normally auto-triggered by hook) and search history.
chromadb
Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).