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Ai Persistence/Build Custom Adapter

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TanStack
ai-persistence/build-custom-adapter

Use when an app needs TanStack AI chat persistence on a database with no dedicated recipe — raw Postgres (pg/postgres.js), Kysely, node:sqlite, MongoDB, Supabase, Redis. Writes a chat-persistence.ts against the app's existing client, covering the four stores, the idempotency invariants, and the conformance gate. Route to the Drizzle, Prisma, or Cloudflare skills instead when one of those matches.

Overview

PublisherTanStack
Repositoryai
Skill nameai-persistence/build-custom-adapter
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 Ai Persistence/Build Custom Adapter 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-persistence/skills/ai-persistence/build-custom-adapter .claude/skills/tanstack-ai-persistence-build-custom-adapter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Persistence/Build Custom Adapter 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 Ai Persistence/Build Custom Adapter 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 Ai Persistence/Build Custom Adapter 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.

Custom Chat Persistence

The deliverable is one file in the appsrc/lib/chat-persistence.ts — exporting a ChatPersistence built from the database client the app already has. Plus whatever DDL that database needs, added through the app's existing migration flow.

Do not create a package, a second client, or a migration runner.

Route first. If the app already runs one of these, stop and use that skill — it has the driver-specific code:

App runsUse
Drizzle ORM (any dialect)ai-persistence/build-drizzle-adapter
Prismaai-persistence/build-prisma-adapter
Cloudflare Workers + D1ai-persistence/build-cloudflare-adapter

Everything else lands here. The full contracts and their invariants are in ai-persistence/stores and docs/persistence/store-reference.md; the complete worked node:sqlite walkthrough is docs/persistence/build-your-own-chat-adapter.md and examples/ts-react-chat/src/lib/sqlite-persistence.ts.

1. Read the app before writing anything

FindWhere to lookWhat it decides
The clientsrc/db.ts, src/lib/db.ts, src/server/db.tsWhat the file imports — never construct a second pool
Client lifetimemodule singleton vs per-request factory (getDb(), bindings)export const chatPersistence vs export function
Migration flowmigrations/, drizzle/, supabase/migrations/, an ORM CLIHow the DDL gets applied — use theirs, add nothing new
Naming conventionsexisting tables/collectionsPrefix (chat_*) so nothing collides
JSON supportjsonb (Postgres), json (MySQL 5.7+), text (SQLite)Whether mappers stringify/parse
Import aliastsconfig.json paths@/db, ~/db, #/db, or a relative path

2. Shape the storage

Four logical records. Whatever the engine, keep these keys — the store methods look records up by exactly these:

RecordKeyFields
threadthreadIdmessages (array, full transcript)
runrunIdthreadId, status, startedAt, finishedAt?, error?, usage?, sandboxKey?, detachedSince?, cancelRequested?, driverEpoch?
interruptinterruptIdrunId, threadId, status, requestedAt, resolvedAt?, payload, response?
metadata(namespace, key)value
  • Timestamps are epoch milliseconds (number) in records. Store them however the engine prefers and convert in the mapper.
  • (namespace, key) is a composite key. Never join with a separator — ('a:b','c') and ('a','b:c') must stay distinct records, and the conformance suite checks it.
  • Index runs(threadId, status), runs(threadId, startedAt), and interrupts(threadId, requestedAt) for the listing paths. If the backend implements listReclaimable, also index runs(status, detachedSince); that is the query it runs.
  • run.error is a structured RunError ({ message: string, code?: string }), not a bare string. message is the provider's prose; code is the stable, machine-branchable classification an operator filters and groups by. In a SQL-backed table, store it as two columns (error, error_code) rather than one JSON blob, moved together in update so a later code-less failure can never leave a stale code from an earlier one behind. run.status is one of 'running' | 'interrupted' | 'completed' | 'failed' | 'aborted'; 'interrupted' is a pause, not terminal, and only 'completed' | 'failed' | 'aborted' are terminal.
  • Extra app-owned columns are fine (a userId, audit columns) as long as they are nullable or defaulted. The stores never read columns they do not know about.

3. The invariants

Getting one of these wrong is the usual source of stuck approvals and wiped history. They are engine-independent:

  1. saveThread is a full overwrite, never an append. The argument is the complete authoritative transcript.
  2. loadThread returns [] for an unknown thread, never null.
  3. createOrResume is insert-if-absent — an existing runId comes back unchanged, ignoring the new field values. Resume and double-submit depend on it. After a racy insert, re-read rather than trusting your own write.
  4. runs.update on an unknown id is a silent no-op — it must not throw and must not insert. (Drivers that throw on zero rows affected need the updateMany-style call, not the update-one-or-throw call.)
  5. runs.update distinguishes "field omitted" from "field explicitly cleared" for the durable-run fields (sandboxKey, detachedSince, cancelRequested, driverEpoch). A reattach clears detachedSince by passing it explicitly as undefinedupdate(runId, { detachedSince: undefined }) — and that must write NULL, not be silently dropped. Check 'detachedSince' in patch, never patch.detachedSince !== undefined; the latter cannot tell a clear from an omission and leaves every reattached run looking permanently detached to the reaper. Same rule for cancelRequested (false is a real value, not "unset") and for sandboxKey / driverEpoch. See examples/ts-react-chat/src/lib/sqlite-persistence.ts for the pattern.
  6. interrupts.create is insert-if-absent — never clobber a resolved interrupt back to pending. Every list* is ordered by requestedAt ascending.
  7. runs.listReclaimable uses an inclusive cutoff (if implemented): status === 'running' AND detachedSince <= now - ttlMs. It is a query, not automatic reclamation: reapDetachedRuns from @tanstack/ai-sandbox is the sweep that consumes it, and the application schedules that sweep. A store without this method cannot be reaped. runs.findActiveRun is required; runs.listByThread / runs.listReclaimable are optional: implement only what the app needs and leave the rest off the object.

Row mappers omit absent optionals (...(row.sandbox_key != null ? { sandboxKey: row.sandbox_key } : {})) so records compare cleanly against the reference in-memory backend. For a two-column error/error_code layout, the mapper is ...(row.error != null ? { error: { message: row.error, ...(row.error_code != null ? { code: row.error_code } : {}) } } : {}).

4. Write src/lib/chat-persistence.ts

Four factories and one assembly. Postgres via pg shown here; the shape is the same for any driver.

ts
import { defineAIPersistence } from '@tanstack/ai-persistence'
import type { Pool } from 'pg'
import type {
  ChatPersistence,
  MessageStore,
  RunStore,
} from '@tanstack/ai-persistence'

import { pool } from '@/db'

function createMessageStore(db: Pool): MessageStore {
  return {
    async loadThread(threadId) {
      const { rows } = await db.query(
        'SELECT messages_json FROM chat_threads WHERE thread_id = $1',
        [threadId],
      )
      return rows[0]?.messages_json ?? []
    },
    // Full overwrite — `messages` is the complete authoritative transcript.
    async saveThread(threadId, messages) {
      await db.query(
        `INSERT INTO chat_threads (thread_id, messages_json, updated_at)
         VALUES ($1, $2, $3)
         ON CONFLICT (thread_id)
         DO UPDATE SET messages_json = EXCLUDED.messages_json,
                       updated_at = EXCLUDED.updated_at`,
        [threadId, JSON.stringify(messages), Date.now()],
      )
    },
  }
}

function createRunStore(db: Pool): RunStore {
  async function get(runId: string) {
    const { rows } = await db.query(
      'SELECT * FROM chat_runs WHERE run_id = $1',
      [runId],
    )
    return rows[0] ? mapRun(rows[0]) : null
  }

  return {
    get,
    // Idempotent: an existing runId is returned untouched.
    async createOrResume({ runId, threadId, startedAt, status }) {
      const existing = await get(runId)
      if (existing) return existing

      await db.query(
        `INSERT INTO chat_runs (run_id, thread_id, status, started_at)
         VALUES ($1, $2, $3, $4)
         ON CONFLICT (run_id) DO NOTHING`,
        [runId, threadId, status ?? 'running', startedAt],
      )
      // Re-read: a concurrent createOrResume may have won the race, and that
      // row is the authoritative one.
      const stored = await get(runId)
      return (
        stored ?? { runId, threadId, status: status ?? 'running', startedAt }
      )
    },
    // ... update (no-op on unknown id; sandboxKey/detachedSince/
    // cancelRequested/driverEpoch are checked with `'field' in patch`, not
    // `patch.field !== undefined`, so an explicit `undefined` (a clear) still
    // writes NULL instead of being silently dropped — status/finishedAt/usage
    // can use the simpler `!== undefined` check since they are never
    // explicitly cleared; writes patch.error as two columns,
    // error = patch.error.message and error_code = patch.error.code ?? null,
    // together in the same call),
    // findActiveRun (latest 'running', required), listByThread (ascending
    // by startedAt, optional), listReclaimable (status = 'running' AND
    // detachedSince <= now - ttlMs, inclusive cutoff, optional)
  }
}

/** The four chat state stores backed by the app's database. */
export const chatPersistence: ChatPersistence = defineAIPersistence({
  stores: {
    messages: createMessageStore(pool),
    runs: createRunStore(pool),
    interrupts: createInterruptStore(pool),
    metadata: createMetadataStore(pool),
  },
})

Annotate ChatPersistence — bare AIPersistence is the all-optional bag and withPersistence rejects it. There is no locks store: stores accepts only messages, runs, interrupts, metadata, and anything else throws Unknown AIPersistence store key. Coordination is wired separately with withLocks (see ai-core/locks).

If the client is per-request (Workers bindings, request-scoped transactions), export a chatPersistence() factory instead of a const and call it inside the handler.

Engine notes

Postgres (pg, postgres.js, Neon, Supabase)jsonb columns round-trip objects, so skip the JSON.stringify on read paths (pg parses jsonb for you; check what the driver returns before assuming). bigint columns come back as strings in pg — use bigint with an explicit Number() in the mapper, or store epoch ms in a double precision/bigint and convert once. Composite key is PRIMARY KEY (namespace, key).

Kysely — define the four tables in the app's Database interface, then the stores are db.insertInto('chat_runs').values(...).onConflict((oc) => oc.column('run_id').doNothing()) and .executeTakeFirst(). updateTable(...).execute() is already a no-op on zero matches, so invariant 4 comes free.

node:sqlite / better-sqlite3 — the complete implementation is in the guide and in examples/ts-react-chat/src/lib/sqlite-persistence.ts. Prepared statements at factory scope, INSERT ... ON CONFLICT, JSON as text, epoch ms as integer. Wrap sync calls in async methods; the contracts are promise-based.

MongoDB — one collection per record type, _id set to the natural key (threadId, runId, interruptId). For metadata, use _id: { namespace, key } — a compound _id subdocument, or a unique index on { namespace, key } — never a delimiter-joined string. Invariant: ('a:b','c') and ('a','b:c') must stay distinct records, and the conformance suite checks it. createOrResume is updateOne({ _id }, { $setOnInsert: doc }, { upsert: true }) then a findOne$setOnInsert is the insert-if-absent primitive. Guard the E11000 duplicate-key race and re-read. list* need .sort({ requestedAt: 1 }).

Redis / Upstash — workable for metadata and excellent for LockStore, but think before putting interrupts there: the listings need ordered secondary indexes you have to maintain by hand (a sorted set per thread and per run, scored by requestedAt). A common split is Postgres for messages/runs/ interrupts and Redis for locks; compose them with composePersistence.

Anything else — you only need the seven invariants above. The core never inspects your storage.

Adopt part of it

You rarely need all four stores at once. Implement what you own and fill the rest from another base:

ts
import { composePersistence, memoryPersistence } from '@tanstack/ai-persistence'
import { messages, runs } from './my-stores'

export const chatPersistence = composePersistence(memoryPersistence(), {
  overrides: { messages, runs },
})

Only listed keys move. There is no cross-store transaction — if messages lives in Postgres and interrupts in Redis, a write touching both is two writes. The idempotency invariants are exactly what make those retries safe.

Wire it into the chat route

ts
import {
  chat,
  chatParamsFromRequest,
  toServerSentEventsResponse,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { withPersistence } from '@tanstack/ai-persistence'
import { chatPersistence } from '@/lib/chat-persistence'

export async function POST(request: Request) {
  const params = await chatParamsFromRequest(request)
  const stream = chat({
    adapter: openaiText('gpt-5.5'),
    messages: params.messages,
    threadId: params.threadId,
    runId: params.runId,
    ...(params.resume ? { resume: params.resume } : {}),
    middleware: [withPersistence(chatPersistence)],
  })
  return toServerSentEventsResponse(stream)
}

threadId is a bare string to the stores. Authorize thread access at the route — derive the user from the session, never trust a client-supplied id.

Verify (required)

This matters more here than anywhere else: there is no reference driver to compare against, so the testkit is the only thing standing between a subtle idempotency bug and stuck approvals in production.

ts
import { runPersistenceConformance } from '@tanstack/ai-persistence/testkit'
import { chatPersistence } from '../src/lib/chat-persistence'

runPersistenceConformance('app-custom', () => chatPersistence, {
  skip: ['generationRuns', 'artifacts', 'blobs'],
})

Point it at a throwaway database and reset between runs. The suite covers all seven stores, so declare every intentional omission — a chat adapter skips the generation half above, and adds e.g. 'metadata' if it drops that too. skip never accepts 'locks', which is not a store.

If your recipe leaves an optional runs method (listByThread/ listReclaimable) unimplemented, declare it separately with skipMethods, e.g. { skipMethods: ['runs.listByThread'] }. An omitted method that is not declared fails the suite instead of silently passing. findActiveRun is not in that set — it is required, so there is nothing to declare.

Frequently asked questions

What does the Ai Persistence/Build Custom Adapter AI skill do?

Use when an app needs TanStack AI chat persistence on a database with no dedicated recipe — raw Postgres (pg/postgres.js), Kysely, node:sqlite, MongoDB, Supabase, Redis. Writes a chat-persistence.ts against the app's existing client, covering the four stores, the idempotency invariants, and the conformance gate. Route to the Drizzle, Prisma, or Cloudflare skills instead when one of those matches.

Why use Ai Persistence/Build Custom Adapter on TypingMind?

Because you install it once and use it with any model. Ai Persistence/Build Custom Adapter 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 Ai Persistence/Build Custom Adapter in TypingMind?

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

Which AI models can use Ai Persistence/Build Custom Adapter?

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 Ai Persistence/Build Custom Adapter?

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

Is the Ai Persistence/Build Custom Adapter 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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