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Tanstack Ai Memory Redis

OrganizationPopular
TanStack
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.

Overview

PublisherTanStack
Repositoryai
Skill nametanstack-ai-memory-redis
Stars
3.1K
Forks
330
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by TanStack on GitHub. Read the source before you install it.

Installation

Install the Tanstack Ai Memory Redis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TanStack/ai.git /tmp/ai
mkdir -p .claude/skills
cp -r /tmp/ai/packages/ai-memory/skills/tanstack-ai-memory-redis .claude/skills/tanstack-ai-memory-redis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tanstack Ai Memory Redis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tanstack Ai Memory Redis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tanstack Ai Memory Redis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

ts
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+)

ts
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

text
{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.

Hard cut: there is no dual-read of older index layouts. If you previously wrote under a different shape (e.g. without tenantId), reindex or wipe — old keys are orphaned.

Always pass the same tenantId/userId/threadId on write and read: missing optional dims become _, so omit ≠ "match any".

Ranking limits

Ranking is client-side: recall loads every record for the scope into Node and scores it. Fine up to ~10k records per scope. Beyond that, write a vector-index-aware adapter against the same recall/save contract.

Troubleshooting

  • Records not visible across processes: ensure every process uses the same REDIS_URL and prefix.
  • Malformed JSON rows: a row whose JSON won't parse is skipped on read and left in place (never deleted) — the signal is a one-time console.warn per bad id. Fix or delete the offending {prefix}:record:{id} key to remediate.

Frequently asked questions

What does the Tanstack Ai Memory Redis AI skill do?

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.

Why use Tanstack Ai Memory Redis on TypingMind?

Because you install it once and use it with any model. Tanstack Ai Memory Redis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tanstack Ai Memory Redis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TanStack/ai/tree/main/packages/ai-memory/skills/tanstack-ai-memory-redis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tanstack Ai Memory Redis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tanstack Ai Memory Redis?

As many as you like. As long as a model supports skills, you can use Tanstack Ai Memory Redis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tanstack Ai Memory Redis AI skill free?

Yes. It is published on GitHub by TanStack under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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