---
title: "chat.local"
sidebarTitle: "chat.local"
description: "Typed, run-scoped data accessible from hooks, run(), tools, and subtasks. Survives across turns, auto-cleared between runs, auto-hydrated into subtasks."
---

import RcBanner from "/snippets/ai-chat-rc-banner.mdx";

<RcBanner />

Use `chat.local` to create typed, run-scoped data that persists across turns and is accessible from anywhere — the run function, tools, nested helpers. Each run gets its own isolated copy, and locals are automatically cleared between runs.

Lifecycle hooks and **`run`** also receive **`ctx`** ([`TaskRunContext`](/ai-chat/reference#task-context-ctx)) — the same object as on a standard `task()` — for tags, metadata, and cleanup that needs the full run record.

When a subtask is invoked via `ai.toolExecute()` (or the deprecated `ai.tool()`), initialized locals are automatically serialized into the subtask's metadata and hydrated on first access — no extra code needed. Subtask changes to hydrated locals are local to the subtask and don't propagate back to the parent.

## Declaring and initializing

Declare locals at module level with a unique `id`, then initialize them inside a lifecycle hook where you have context (chatId, clientData, etc.):

```ts
import { chat } from "@trigger.dev/sdk/ai";
import { streamText, tool, stepCountIs } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { z } from "zod";
import { db } from "@/lib/db";

// Declare at module level — each local needs a unique id
const userContext = chat.local<{
  userId: string;
  name: string;
  plan: "free" | "pro";
  messageCount: number;
}>({ id: "userContext" });

export const myChat = chat.agent({
  id: "my-chat",
  clientDataSchema: z.object({ userId: z.string() }),
  onBoot: async ({ clientData }) => {
    // Initialize with real data from your database
    const user = await db.user.findUnique({
      where: { id: clientData.userId },
    });
    userContext.init({
      userId: clientData.userId,
      name: user.name,
      plan: user.plan,
      messageCount: user.messageCount,
    });
  },
  run: async ({ messages, signal }) => {
    userContext.messageCount++;

    return streamText({
      model: anthropic("claude-sonnet-4-5"),
      system: `Helping ${userContext.name} (${userContext.plan} plan).`,
      messages,
      abortSignal: signal,
      stopWhen: stepCountIs(15),
    });
  },
});
```

<Warning>
  Initialize `chat.local` in [`onBoot`](/ai-chat/lifecycle-hooks#onboot), not `onChatStart`. `onBoot` fires on every fresh worker — including continuation runs (post-cancel, crash, `endRun`, `requestUpgrade`, OOM retry) — whereas `onChatStart` only fires on the chat's very first message. Initializing in `onChatStart` means `run()` will crash on continuation runs with `chat.local can only be modified after initialization`.
</Warning>

## Accessing from tools

Locals are accessible from anywhere during task execution — including AI SDK tools:

```ts
const userContext = chat.local<{ plan: "free" | "pro" }>({ id: "userContext" });

const premiumTool = tool({
  description: "Access premium features",
  inputSchema: z.object({ feature: z.string() }),
  execute: async ({ feature }) => {
    if (userContext.plan !== "pro") {
      return { error: "This feature requires a Pro plan." };
    }
    // ... premium logic
  },
});
```

## Accessing from subtasks

When you use `ai.toolExecute()` inside AI SDK `tool()` to expose a subtask, chat locals are automatically available read-only:

```ts
import { chat, ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { streamText, tool } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { z } from "zod";

const userContext = chat.local<{ name: string; plan: "free" | "pro" }>({ id: "userContext" });

export const analyzeDataTask = schemaTask({
  id: "analyze-data",
  schema: z.object({ query: z.string() }),
  run: async ({ query }) => {
    // userContext.name just works — auto-hydrated from parent metadata
    console.log(`Analyzing for ${userContext.name}`);
    // Changes here are local to this subtask and don't propagate back
  },
});

const analyzeData = tool({
  description: analyzeDataTask.description ?? "",
  inputSchema: analyzeDataTask.schema!,
  execute: ai.toolExecute(analyzeDataTask),
});

export const myChat = chat.agent({
  id: "my-chat",
  onBoot: async ({ clientData }) => {
    userContext.init({ name: "Alice", plan: "pro" });
  },
  run: async ({ messages, signal }) => {
    return streamText({
      model: anthropic("claude-sonnet-4-5"),
      messages,
      tools: { analyzeData },
      abortSignal: signal,
      stopWhen: stepCountIs(15),
    });
  },
});
```

<Note>
  Values must be JSON-serializable for subtask access. Non-serializable values (functions, class instances, etc.) will be lost during transfer.
</Note>

## Dirty tracking and persistence

The `hasChanged()` method returns `true` if any property was set since the last check, then resets the flag. Use it in lifecycle hooks to only persist when data actually changed:

```ts
onTurnComplete: async ({ chatId }) => {
  if (userContext.hasChanged()) {
    await db.user.update({
      where: { id: userContext.get().userId },
      data: {
        messageCount: userContext.messageCount,
      },
    });
  }
},
```

## API

| Method | Description |
|--------|-------------|
| `chat.local<T>({ id })` | Create a typed local with a unique id (declare at module level) |
| `local.init(value)` | Initialize with a value (call in hooks or `run`) |
| `local.hasChanged()` | Returns `true` if modified since last check, resets flag |
| `local.get()` | Returns a plain object copy (for serialization) |
| `local.property` | Direct property access (read/write via Proxy) |

<Note>
  Locals use shallow proxying. Nested object mutations like `local.prefs.theme = "dark"` won't trigger the dirty flag. Instead, replace the whole property: `local.prefs = { ...local.prefs, theme: "dark" }`.
</Note>

## See also

- [Lifecycle hooks](/ai-chat/lifecycle-hooks) — `onBoot` is the canonical init site for `chat.local`.
- [Database persistence pattern](/ai-chat/patterns/database-persistence) — full per-hook breakdown using `chat.local` alongside DB rows.
- [Code execution sandbox pattern](/ai-chat/patterns/code-sandbox) — example of using `chat.local` to hold a sandbox handle across turns.
- [Database connections](/database-connections) — why the database client and its connection pool belong at module scope, not in `chat.local`.
