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Ai Persistence/Build Prisma Adapter

OrganizationPopular
TanStack
ai-persistence/build-prisma-adapter

Use when an app already runs Prisma and needs TanStack AI chat persistence — writes a chat-persistence.ts into the app against its existing PrismaClient and schema.prisma. Covers the four models, BigInt timestamps, JSON-as-string columns, upsert-with-empty-update idempotency, and model renaming.

Overview

PublisherTanStack
Repositoryai
Skill nameai-persistence/build-prisma-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 Prisma 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-prisma-adapter .claude/skills/tanstack-ai-persistence-build-prisma-adapter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Persistence/Build Prisma 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 Prisma 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 Prisma 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.

Prisma Chat Persistence

The deliverable is one file in the appsrc/lib/chat-persistence.ts — exporting a ChatPersistence built from the app's existing PrismaClient. Plus four models added to the app's existing schema.prisma and a migration created with the app's own prisma migrate.

Do not create a package, a second client, a datasource block, a generator, or a hand-written SQL migration. The app has those.

Read the Store Reference (docs/persistence/store-reference.md) for the store contracts and invariants, and ai-persistence/stores for the shape rules. Every store below mirrors the reference in-memory backend in @tanstack/ai-persistence (memory.ts); the shared conformance testkit is the proof.

1. Read the app before writing anything

FindWhere to lookWhat it decides
Schema locationprisma/schema.prisma, or a multi-file prisma/schema/ dirAppend to the existing file, or add one new .prisma file
Providerthe datasource blockWhether Json is available; nothing else changes
Client singletonsrc/lib/prisma.ts, src/db.ts, globalThis dev cacheWhat chat-persistence.ts imports — never new PrismaClient()
Generated clientthe generator client block (output, prisma-client-js vs prisma-client)Where ChatRun/ChatInterrupt row types come from
Existing model namesthe schemaWhether Message/Run are taken — prefix if so
Migration flowprisma/migrations/, or db push in scriptsprisma migrate dev vs prisma db push

Prisma 6 and 7 both work: the delegate query API (findUnique, upsert, update, findMany, delete) is unchanged, so it does not matter which client the app generated.

Never invent a migration path. Add the models, then have the user run their own npx prisma migrate dev --name chat-persistence (or db push) and prisma generate.

2. Add the models to their schema

IDs are String, timestamps are BigInt (portable epoch ms — Int overflows in 2038, DateTime forces a conversion at every boundary), JSON payloads are String. Use @map/@@map to match the app's database naming.

prisma
model ChatThread {
  threadId     String @id @map("thread_id")
  messagesJson String @map("messages_json")
  updatedAt    BigInt @map("updated_at")

  @@map("chat_threads")
}

model ChatRun {
  runId           String  @id @map("run_id")
  threadId        String  @map("thread_id")
  status          String
  startedAt       BigInt  @map("started_at")
  finishedAt      BigInt? @map("finished_at")
  error           String?
  errorCode       String? @map("error_code")
  usageJson       String? @map("usage_json")
  sandboxKey      String? @map("sandbox_key")
  detachedSince   BigInt? @map("detached_since")
  cancelRequested Boolean? @map("cancel_requested")
  driverEpoch     Int?     @map("driver_epoch")

  @@index([threadId, status])
  @@index([threadId, startedAt])
  // Powers listReclaimable: status = 'running' AND detachedSince <= cutoff.
  @@index([status, detachedSince])
  @@map("chat_runs")
}

model ChatInterrupt {
  interruptId  String  @id @map("interrupt_id")
  runId        String  @map("run_id")
  threadId     String  @map("thread_id")
  status       String
  requestedAt  BigInt  @map("requested_at")
  resolvedAt   BigInt? @map("resolved_at")
  payloadJson  String  @map("payload_json")
  responseJson String? @map("response_json")

  @@index([threadId, requestedAt])
  @@map("chat_interrupts")
}

model ChatMetadata {
  namespace String
  key       String
  valueJson String @map("value_json")

  @@id([namespace, key])
  @@map("chat_metadata")
}

Rename models freely to fit the app — the store code below is the only thing that references them. Extra app-owned fields (a userId, audit columns) are fine as long as they are optional or defaulted, so the stores' creates still succeed. namespace is the MetadataStore first argument; the stock SQL in the guide calls the same column scope.

RunRecord.error is a structured RunError ({ message: string, code?: string }), so it gets two columns rather than one JSON blob: error for the provider's prose and errorCode for the stable classification an operator filters and groups by. error and errorCode always move together in update, so a later code-less failure can never leave a stale code from an earlier one behind.

On Postgres or MySQL you can switch the *Json fields to Prisma's Json type and drop the JSON.stringify/parse in the mappers below. Keep String if the app targets SQLite or if it is multi-provider.

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

Two conversions the SQL backends do not need: BigInt timestamps in and out, and JSON as strings. Everything else is the shared invariant set.

ts
import { defineAIPersistence } from '@tanstack/ai-persistence'
import type {
  ChatInterrupt,
  ChatRun,
  Prisma,
  PrismaClient,
} from '@prisma/client'
import type { ModelMessage, TokenUsage } from '@tanstack/ai'
import type {
  ChatPersistence,
  InterruptRecord,
  InterruptStatus,
  InterruptStore,
  MessageStore,
  MetadataStore,
  RunRecord,
  RunStatus,
  RunStore,
} from '@tanstack/ai-persistence'

import { prisma } from '@/lib/prisma'

// Trusts the shape the stores themselves wrote — nothing else writes these
// columns.
function parseJson<T>(raw: string): T {
  return JSON.parse(raw)
}

const RUN_STATUSES: ReadonlyArray<RunStatus> = [
  'running',
  'interrupted',
  'completed',
  'failed',
  'aborted',
]
const INTERRUPT_STATUSES: ReadonlyArray<InterruptStatus> = [
  'pending',
  'resolved',
  'cancelled',
]

// The column is a plain String, so narrow instead of trusting it.
function toRunStatus(value: string): RunStatus {
  const status = RUN_STATUSES.find((candidate) => candidate === value)
  if (!status) throw new Error(`Unknown run status: ${value}`)
  return status
}

function toInterruptStatus(value: string): InterruptStatus {
  const status = INTERRUPT_STATUSES.find((candidate) => candidate === value)
  if (!status) throw new Error(`Unknown interrupt status: ${value}`)
  return status
}

// Records omit absent optionals so they compare cleanly against the reference
// in-memory backend.
function mapRun(row: ChatRun): RunRecord {
  return {
    runId: row.runId,
    threadId: row.threadId,
    status: toRunStatus(row.status),
    startedAt: Number(row.startedAt),
    ...(row.finishedAt != null ? { finishedAt: Number(row.finishedAt) } : {}),
    ...(row.error != null
      ? {
          error: {
            message: row.error,
            ...(row.errorCode != null ? { code: row.errorCode } : {}),
          },
        }
      : {}),
    ...(row.usageJson != null
      ? { usage: parseJson<TokenUsage>(row.usageJson) }
      : {}),
    ...(row.sandboxKey != null ? { sandboxKey: row.sandboxKey } : {}),
    ...(row.detachedSince != null
      ? { detachedSince: Number(row.detachedSince) }
      : {}),
    ...(row.cancelRequested != null
      ? { cancelRequested: row.cancelRequested }
      : {}),
    ...(row.driverEpoch != null ? { driverEpoch: row.driverEpoch } : {}),
  }
}

function mapInterrupt(row: ChatInterrupt): InterruptRecord {
  return {
    interruptId: row.interruptId,
    runId: row.runId,
    threadId: row.threadId,
    status: toInterruptStatus(row.status),
    requestedAt: Number(row.requestedAt),
    payload: parseJson<Record<string, unknown>>(row.payloadJson),
    ...(row.resolvedAt != null ? { resolvedAt: Number(row.resolvedAt) } : {}),
    ...(row.responseJson != null
      ? { response: parseJson<unknown>(row.responseJson) }
      : {}),
  }
}

function createMessageStore(db: PrismaClient): MessageStore {
  return {
    async loadThread(threadId) {
      const row = await db.chatThread.findUnique({ where: { threadId } })
      // Unknown thread is [], never null.
      return row ? parseJson<Array<ModelMessage>>(row.messagesJson) : []
    },
    // Full overwrite — `messages` is the complete authoritative transcript.
    async saveThread(threadId, messages) {
      const messagesJson = JSON.stringify(messages)
      const updatedAt = BigInt(Date.now())
      await db.chatThread.upsert({
        where: { threadId },
        create: { threadId, messagesJson, updatedAt },
        update: { messagesJson, updatedAt },
      })
    },
  }
}

function createRunStore(db: PrismaClient): RunStore {
  return {
    async get(runId) {
      const row = await db.chatRun.findUnique({ where: { runId } })
      return row ? mapRun(row) : null
    },
    // An empty `update` is Prisma's ON CONFLICT DO NOTHING: an existing runId
    // comes back untouched, so resume and double-submit are safe.
    async createOrResume({ runId, threadId, startedAt, status }) {
      const row = await db.chatRun.upsert({
        where: { runId },
        create: {
          runId,
          threadId,
          status: status ?? 'running',
          startedAt: BigInt(startedAt),
        },
        update: {},
      })
      return mapRun(row)
    },
    // Patching an unknown runId is a no-op: never throws, never inserts.
    async update(runId, patch) {
      const data: Prisma.ChatRunUpdateManyMutationInput = {}
      if (patch.status !== undefined) data.status = patch.status
      if (patch.finishedAt !== undefined) {
        data.finishedAt = BigInt(patch.finishedAt)
      }
      // Both columns move together, so a later code-less failure cannot
      // leave a stale errorCode from an earlier one behind.
      if (patch.error !== undefined) {
        data.error = patch.error.message
        data.errorCode = patch.error.code ?? null
      }
      if (patch.usage !== undefined)
        data.usageJson = JSON.stringify(patch.usage)
      // The four durable-run fields use `'field' in patch`, NOT
      // `!== undefined`: a reattach clears `detachedSince` by passing it
      // explicitly as `undefined`, and that must still write NULL, not be
      // silently dropped from the update. Checking `!== undefined` cannot
      // tell "clear this" from "didn't mention this", and would leave every
      // reattached run looking permanently detached to the reaper. Same
      // reasoning for `cancelRequested` (`false` is a meaningful value).
      if ('sandboxKey' in patch) data.sandboxKey = patch.sandboxKey ?? null
      if ('detachedSince' in patch) {
        data.detachedSince =
          patch.detachedSince === undefined ? null : BigInt(patch.detachedSince)
      }
      if ('cancelRequested' in patch)
        data.cancelRequested = patch.cancelRequested ?? null
      if ('driverEpoch' in patch) data.driverEpoch = patch.driverEpoch ?? null
      if (Object.keys(data).length === 0) return

      await db.chatRun.updateMany({ where: { runId }, data })
    },
    // Optional in the contract; enables reconnect without a client-held run id.
    async findActiveRun(threadId) {
      const row = await db.chatRun.findFirst({
        where: { threadId, status: 'running' },
        orderBy: { startedAt: 'desc' },
      })
      return row ? mapRun(row) : null
    },
    // Optional; every run for the thread, ascending by startedAt. Uses the
    // (threadId, startedAt) index.
    async listByThread(threadId) {
      const rows = await db.chatRun.findMany({
        where: { threadId },
        orderBy: { startedAt: 'asc' },
      })
      return rows.map(mapRun)
    },
    // Optional; still-running runs detached at or before the cutoff. Uses
    // the (status, detachedSince) index. The cutoff is inclusive.
    async listReclaimable({ now, ttlMs }) {
      const cutoff = BigInt(now - ttlMs)
      const rows = await db.chatRun.findMany({
        where: {
          status: 'running',
          detachedSince: { not: null, lte: cutoff },
        },
      })
      return rows.map(mapRun)
    },
  }
}

function createInterruptStore(db: PrismaClient): InterruptStore {
  // Every listing is ordered by requestedAt ascending.
  const listWhere = async (where: Prisma.ChatInterruptWhereInput) => {
    const rows = await db.chatInterrupt.findMany({
      where,
      orderBy: { requestedAt: 'asc' },
    })
    return rows.map(mapInterrupt)
  }

  return {
    // Insert-if-absent: a duplicate create must never clobber a resolved
    // interrupt back to pending.
    async create(record) {
      await db.chatInterrupt.upsert({
        where: { interruptId: record.interruptId },
        create: {
          interruptId: record.interruptId,
          runId: record.runId,
          threadId: record.threadId,
          status: 'pending',
          requestedAt: BigInt(record.requestedAt),
          payloadJson: JSON.stringify(record.payload),
          ...(record.response !== undefined
            ? { responseJson: JSON.stringify(record.response) }
            : {}),
        },
        update: {},
      })
    },
    async resolve(interruptId, response) {
      await db.chatInterrupt.updateMany({
        where: { interruptId },
        data: {
          status: 'resolved',
          resolvedAt: BigInt(Date.now()),
          ...(response !== undefined
            ? { responseJson: JSON.stringify(response) }
            : {}),
        },
      })
    },
    async cancel(interruptId) {
      await db.chatInterrupt.updateMany({
        where: { interruptId },
        data: { status: 'cancelled', resolvedAt: BigInt(Date.now()) },
      })
    },
    async get(interruptId) {
      const row = await db.chatInterrupt.findUnique({ where: { interruptId } })
      return row ? mapInterrupt(row) : null
    },
    list: (threadId) => listWhere({ threadId }),
    listPending: (threadId) => listWhere({ threadId, status: 'pending' }),
    listByRun: (runId) => listWhere({ runId }),
    listPendingByRun: (runId) => listWhere({ runId, status: 'pending' }),
  }
}

function createMetadataStore(db: PrismaClient): MetadataStore {
  return {
    async get(namespace, key) {
      const row = await db.chatMetadata.findUnique({
        where: { namespace_key: { namespace, key } },
      })
      return row ? parseJson<unknown>(row.valueJson) : null
    },
    async set(namespace, key, value) {
      // JSON.stringify(undefined) is undefined, which Prisma rejects against a
      // required column with an opaque error. Fail clearly instead.
      if (value == null) {
        throw new TypeError(
          `Cannot store ${value} for (${namespace}, ${key}) — use delete() to clear metadata.`,
        )
      }
      const valueJson = JSON.stringify(value)
      await db.chatMetadata.upsert({
        where: { namespace_key: { namespace, key } },
        create: { namespace, key, valueJson },
        update: { valueJson },
      })
    },
    async delete(namespace, key) {
      await db.chatMetadata.deleteMany({ where: { namespace, key } })
    },
  }
}

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

Notes that bite:

  • updateMany, not update, for patches. update throws P2025 on a missing row; the contract says a patch to an unknown id is a silent no-op.
  • namespace_key is Prisma's generated alias for the @@id([namespace, key]) composite. If you rename the fields, the alias name changes with them.
  • Annotate ChatPersistence — bare AIPersistence is the all-optional bag and withPersistence rejects it. There is no locks store: stores accepts only those four keys, and coordination is wired separately with withLocks (see ai-core/locks).
  • If the app renamed the models, the delegate accessors are camelCase (prisma.chatThread for model ChatThread), and the row types imported from the client are PascalCase.

4. 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.

5. Verify

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

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

Point the client at a throwaway database with the migration applied (a scratch SQLite file is enough) and reset it between runs. All four state stores are provided; the suite also covers the three generation stores, so declare those as skipped until you add them. skip never accepts 'locks', which is not a store.

If your recipe leaves an optional runs method (listByThread/listReclaimable) unimplemented, declare it with skipMethods, e.g. { skipMethods: ['runs.listByThread'] }. An omitted method that is not declared fails the suite instead of silently passing.

Only if you are publishing this as a package

Everything above assumes the file lives in the app. For a reusable npm adapter, the same store bodies apply, plus:

  • Peer dep @prisma/client >=6.7.0. Ship no datasource, generator, connection URL, or prebuilt SQL migration — those stay in the consumer's schema.
  • Type the client structurally (a PrismaClientLike shape) and read model delegates off it at runtime, so Prisma 6 and 7 clients both satisfy it regardless of where they were generated.
  • Ship the models as a raw string asset plus a CLI (tanstack-ai-prisma-models) that copies a provider-neutral fragment into the consumer's multi-file schema directory. They then run prisma migrate.
  • Let consumers rename: prismaPersistence(prisma, { models: { messages: 'chatMessage' } }), where map values are the camelCase client accessors. Throw a PrismaModelError naming every store whose delegate cannot be found. Keep the field surface and the composite-id alias fixed; database names and extra app-owned fields are theirs.
  • Run runPersistenceConformance over a temporary SQLite database generated from the fragment.

Frequently asked questions

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

Use when an app already runs Prisma and needs TanStack AI chat persistence — writes a chat-persistence.ts into the app against its existing PrismaClient and schema.prisma. Covers the four models, BigInt timestamps, JSON-as-string columns, upsert-with-empty-update idempotency, and model renaming.

Why use Ai Persistence/Build Prisma Adapter on TypingMind?

Because you install it once and use it with any model. Ai Persistence/Build Prisma 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 Prisma 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-prisma-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 Prisma 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 Prisma Adapter?

As many as you like. As long as a model supports skills, you can use Ai Persistence/Build Prisma 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 Prisma 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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