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Tanstack Ai Memory

OrganizationPopular
TanStack
tanstack-ai-memory

Use when wiring memoryMiddleware from @tanstack/ai-memory into a chat() call — covers the recall/save adapter contract, scope shape and server-side scope security, the recall-inject / deferred-save lifecycle, choosing an adapter (inMemory, redis, hindsight, mem0, honcho), and devtools events.

Overview

PublisherTanStack
Repositoryai
Skill nametanstack-ai-memory
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 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 .claude/skills/tanstack-ai-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tanstack Ai Memory 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 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 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.

TanStack AI Memory Middleware

Use this when adding server-side memory to a chat() call. Everything lives in @tanstack/ai-memory. A memory adapter is a single contract with two verbs — recall and save — and the middleware is thin: it recalls into the system prompt before the model runs and defers save after the turn finishes.

When to reach for it

  • A user expects "remember what I told you last time."
  • Per-user or per-thread context that must survive across sessions.
  • A hosted memory service (mem0, Honcho, Hindsight).

Do NOT use this just to keep recent messages — that's the messages array on chat(). Memory is for cross-turn / cross-session recall, not within-turn history.

Wire it up

ts
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { memoryMiddleware } from '@tanstack/ai-memory'
import { inMemory } from '@tanstack/ai-memory/in-memory'
import { requireSession } from './auth'

const memory = inMemory() // dev/tests only — see the in-memory skill

export async function POST(request: Request) {
  const { messages } = await request.json()
  // Resolved by your auth layer from cookies/headers — never from the request body.
  const session = await requireSession(request)

  const stream = chat({
    adapter: openaiText('gpt-5.5'),
    messages,
    context: { session },
    middleware: [
      memoryMiddleware({
        adapter: memory,
        // Derive scope server-side from trusted session state.
        scope: () => ({ threadId: session.threadId, userId: session.userId }),
      }),
    ],
  })
  return toServerSentEventsResponse(stream)
}

memoryMiddleware options: adapter, scope (static or a function of ctx), role ('recall+save' default, or 'save-only'), and onRecall / onSave telemetry callbacks.

The contract

ts
import type {
  MemoryFact,
  MemoryScope,
  MemorySnapshot,
  MemoryTurn,
  RecallResult,
  SaveReceipt,
} from '@tanstack/ai-memory'

interface MemoryAdapter {
  readonly id: string
  recall: (scope: MemoryScope, query: string) => Promise<RecallResult> // { systemPrompt, fragments?, tools?, toolGuidance? }
  save: (scope: MemoryScope, turn: MemoryTurn) => Promise<Array<SaveReceipt>> // turn = { user, assistant }; extraction lives HERE
  inspect?: (scope: MemoryScope) => Promise<MemorySnapshot> // optional (devtools)
  listFacts?: (scope: MemoryScope) => Promise<Array<MemoryFact>> // optional (devtools)
}
  • recall decides relevance and renders a systemPrompt; it may also return tools + toolGuidance to hand the model direct control of memory (hindsight does this).
  • save owns extraction — turning the raw turn into whatever gets persisted.

Scope security

MemoryScope is an alias of the shared Scope type from @tanstack/ai: { threadId, userId?, tenantId?, namespace? }. It is the isolation boundary. Never trust a client-supplied userId/threadId. Resolve scope server-side from session/auth and pass the validated session through chat({ context: { session } }). If you accept a thread id from the request body, validate it belongs to the session user BEFORE using it.

Adapters

  • inMemory() / redis() — exact match on threadId + optional userId/tenantId (namespace ignored). Redis index keys include all three segments.
  • hindsight() — bank {tenant|_}__{user}__{threadId}.
  • mem0()user_id + run_id (threadId); no tenantId.
  • honcho() — session {tenant|_}__{threadId}; peer tenant-prefixed when set.
  • Custom — implement recall/save and run runMemoryAdapterContract from @tanstack/ai-memory/testkit.

Failure modes

Memory failures are non-fatal: a throwing recall or save emits memory:error and the run continues with degraded memory. Streaming is never blocked; a failed save never fails the turn.

Devtools

Five events on aiEventClient (from @tanstack/ai-event-client): memory:retrieve:started / :completed, memory:persist:started / :completed, memory:error (phase: 'recall' | 'save'). Payloads carry the adapter id and fragment/receipt counts, not full memory text. Error events include scope only when it was already resolved; if the resolver threw, scope is omitted.

Frequently asked questions

What does the Tanstack Ai Memory AI skill do?

Use when wiring memoryMiddleware from @tanstack/ai-memory into a chat() call — covers the recall/save adapter contract, scope shape and server-side scope security, the recall-inject / deferred-save lifecycle, choosing an adapter (inMemory, redis, hindsight, mem0, honcho), and devtools events.

Why use Tanstack Ai Memory on TypingMind?

Because you install it once and use it with any model. Tanstack Ai Memory 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 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. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tanstack Ai Memory?

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?

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

Is the Tanstack Ai Memory 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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