TanStack AI is a TypeScript SDK for building applications that stream AI responses, call tools, run agents, produce structured output, and handle multimodal or realtime interactions. Developers use it across frameworks and AI providers through shared typed interfaces and provider adapters. The catalogue entries are add-ons for working with this SDK.
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 TanStack/ai --skill tanstack-ai-memory-redisgit clone --depth 1 https://github.com/TanStack/aiWrote 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/tanstack/ai/tanstack-ai-memory-redis)<a href="https://agentmods.dev/skills/tanstack/ai/tanstack-ai-memory-redis"><img src="https://agentmods.dev/badge/skills/tanstack/ai/tanstack-ai-memory-redis/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/tanstack/ai/tanstack-ai-memory-redis"><img src="https://agentmods.dev/badge/skills/tanstack/ai/tanstack-ai-memory-redis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00052 | $0.00862 |
| Opus 5.5 | $0.00021 | $0.00345 |
| Sonnet 5.5 | $0.00010 | $0.00172 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
tanstack-ai-memory-redis 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Redis Memory Adapter
Production-grade recall/save adapter backed by plain Redis (no vector index
required). Ranks client-side (lexical + optional cosine + recency + importance).
Setup
Bring your own Redis client. ioredis wires in directly; redis (node-redis v4+) needs
a small wrapper.
Option A: ioredis
import Redis from 'ioredis'
import { memoryMiddleware } from '@tanstack/ai-memory'
import { redis } from '@tanstack/ai-memory/redis'
const client = new Redis(process.env.REDIS_URL ?? 'redis://localhost:6379')
const memory = redis({ redis: client, prefix: 'myapp:memory' })
// Resolve scope per request from the server-validated session — never from req.body.
function memoryFor(session: { userId: string; threadId: string }) {
return memoryMiddleware({
adapter: memory,
scope: { threadId: session.threadId, userId: session.userId },
})
}
Option B: redis (node-redis v4+)
import { createClient } from 'redis'
import { memoryMiddleware } from '@tanstack/ai-memory'
import { redis, fromNodeRedis } from '@tanstack/ai-memory/redis'
const client = createClient({ url: process.env.REDIS_URL })
await client.connect()
const memory = redis({
redis: fromNodeRedis(client),
prefix: 'myapp:memory',
})
function memoryFor(session: { userId: string; threadId: string }) {
return memoryMiddleware({
adapter: memory,
scope: { threadId: session.threadId, userId: session.userId },
})
}
node-redis exposes a camelCase API (sAdd, mGet); fromNodeRedis translates it
to the lowercase RedisLike shape. Passing a raw node-redis client without the wrapper
throws client.sadd is not a function.
redis() accepts the same topK / minScore / kinds / embedder / extract options
as inMemory().
Storage model
{prefix}:record:{id} -> JSON record
{prefix}:index:{tenantId or _}:{userId or _}:{threadId} -> Set<id>
save writes the record and adds it to the scope's index set; recall loads the set,
scores, and renders. Scope values are escaped (:, \, and _) so a delimiter or the
unset placeholder inside a dim can't collide two scopes.
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 Changed · +11 lines 1fdbbd83daa0
- 1mo ago First seen · 84 lines · 52 tokens per session scan A e919d86b5987
tanstack-ai-memory-redis is a skill published in the GitHub repository TanStack/ai (3,199 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 862 once invoked, about $0.0002 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.
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