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The schemaTask function allows you to define a task with a runtime payload schema. This schema is used to validate the payload before running the task or when triggering a task directly. If the payload does not match the schema, the task will not execute.

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Usage

import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";

const myTask = schemaTask({
  id: "my-task",
  schema: z.object({
    name: z.string(),
    age: z.number(),
  }),
  run: async (payload) => {
    console.log(payload.name, payload.age);
  },
});
schemaTask takes all the same options as task, with the addition of a schema field. The schema field is a schema parser function from a schema library or or a custom parser function.
We will probably eventually combine task and schemaTask into a single function, but because that would be a breaking change, we are keeping them separate for now.
When you trigger the task directly, the payload will be validated against the schema before the run is created: 
import { tasks } from "@trigger.dev/sdk";
import { myTask } from "./trigger/myTasks";

// This will call the schema parser function and validate the payload
await myTask.trigger({ name: "Alice", age: "oops" }); // this will throw an error

// This will NOT call the schema parser function
await tasks.trigger<typeof myTask>("my-task", { name: "Alice", age: "oops" }); // this will not throw an error
The error thrown when the payload does not match the schema will be the same as the error thrown by the schema parser function. For example, if you are using Zod, the error will be a ZodError. We will also validate the payload every time before the task is run, so you can be sure that the payload is always valid. In the example above, the task would fail with a TaskPayloadParsedError error and skip retrying if the payload does not match the schema.

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Input/output schemas

Certain schema libraries, like Zod, split their type inference into “schema in” and “schema out”. This means that you can define a single schema that will produce different types when triggering the task and when running the task. For example, you can define a schema that has a default value for a field, or a string coerced into a date: 
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";

const myTask = schemaTask({
  id: "my-task",
  schema: z.object({
    name: z.string().default("John"),
    age: z.number(),
    dob: z.coerce.date(),
  }),
  run: async (payload) => {
    console.log(payload.name, payload.age);
  },
});
In this case, the trigger payload type is { name?: string, age: number; dob: string }, but the run payload type is { name: string, age: number; dob: Date }. So you can trigger the task with a payload like this: 
await myTask.trigger({ age: 30, dob: "2020-01-01" }); // this is valid
await myTask.trigger({ name: "Alice", age: 30, dob: "2020-01-01" }); // this is also valid

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ai.tool

The ai.tool function allows you to create an AI tool from an existing schemaTask to use with the Vercel AI SDK: 
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";
import { generateText } from "ai";

const myToolTask = schemaTask({
  id: "my-tool-task",
  schema: z.object({
    foo: z.string(),
  }),
  run: async (payload: any, { ctx }) => {},
});

const myTool = ai.tool(myToolTask);

export const myAiTask = schemaTask({
  id: "my-ai-task",
  schema: z.object({
    text: z.string(),
  }),
  run: async (payload, { ctx }) => {
    const { text } = await generateText({
      prompt: payload.text,
      model: openai("gpt-4o"),
      tools: {
        myTool,
      },
    });
  },
});
You can also pass the experimental_toToolResultContent option to the ai.tool function to customize the content of the tool result: 
import { openai } from "@ai-sdk/openai";
import { Sandbox } from "@e2b/code-interpreter";
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { generateObject } from "ai";
import { z } from "zod";

const chartTask = schemaTask({
  id: "chart",
  description: "Generate a chart using natural language",
  schema: z.object({
    input: z.string().describe("The chart to generate"),
  }),
  run: async ({ input }) => {
    const code = await generateObject({
      model: openai("gpt-4o"),
      schema: z.object({
        code: z.string().describe("The Python code to execute"),
      }),
      system: `
        You are a helpful assistant that can generate Python code to be executed in a sandbox, using matplotlib.pyplot.

        For example: 
        
        import matplotlib.pyplot as plt
        plt.plot([1, 2, 3, 4])
        plt.ylabel('some numbers')
        plt.show()
        
        Make sure the code ends with plt.show()
      `,
      prompt: input,
    });

    const sandbox = await Sandbox.create();

    const execution = await sandbox.runCode(code.object.code);

    const firstResult = execution.results[0];

    if (firstResult.png) {
      return {
        chart: firstResult.png,
      };
    } else {
      throw new Error("No chart generated");
    }
  },
});

// This is useful if you want to return an image from the tool
export const chartTool = ai.tool(chartTask, {
  experimental_toToolResultContent: (result) => {
    return [
      {
        type: "image",
        data: result.chart,
        mimeType: "image/png",
      },
    ];
  },
});
You can access the current tool execution options inside the task run function using the ai.currentToolOptions() function: 
import { ai } from "@trigger.dev/sdk/ai";
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";

const myToolTask = schemaTask({
  id: "my-tool-task",
  schema: z.object({
    foo: z.string(),
  }),
  run: async (payload, { ctx }) => {
    const toolOptions = ai.currentToolOptions();
    console.log(toolOptions);
  },
});

export const myAiTask = ai.tool(myToolTask);
See the AI SDK tool execution options docs for more details on the tool execution options.
ai.tool is compatible with schemaTask’s defined with Zod and ArkType schemas, or any schemas that implement a .toJsonSchema() function.

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Supported schema types

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Zod

You can use the Zod schema library to define your schema. The schema will be validated using Zod’s parse function. 
import { schemaTask } from "@trigger.dev/sdk";
import { z } from "zod";

export const zodTask = schemaTask({
  id: "types/zod",
  schema: z.object({
    bar: z.string(),
    baz: z.string().default("foo"),
  }),
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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Yup

import { schemaTask } from "@trigger.dev/sdk";
import * as yup from "yup";

export const yupTask = schemaTask({
  id: "types/yup",
  schema: yup.object({
    bar: yup.string().required(),
    baz: yup.string().default("foo"),
  }),
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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Superstruct

import { schemaTask } from "@trigger.dev/sdk";
import { object, string } from "superstruct";

export const superstructTask = schemaTask({
  id: "types/superstruct",
  schema: object({
    bar: string(),
    baz: string(),
  }),
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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ArkType

import { schemaTask } from "@trigger.dev/sdk";
import { type } from "arktype";

export const arktypeTask = schemaTask({
  id: "types/arktype",
  schema: type({
    bar: "string",
    baz: "string",
  }).assert,
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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@effect/schema

import { schemaTask } from "@trigger.dev/sdk";
import * as Schema from "@effect/schema/Schema";

// For some funny typescript reason, you cannot pass the Schema.decodeUnknownSync directly to schemaTask
const effectSchemaParser = Schema.decodeUnknownSync(
  Schema.Struct({ bar: Schema.String, baz: Schema.String })
);

export const effectTask = schemaTask({
  id: "types/effect",
  schema: effectSchemaParser,
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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runtypes

import { schemaTask } from "@trigger.dev/sdk";
import * as T from "runtypes";

export const runtypesTask = schemaTask({
  id: "types/runtypes",
  schema: T.Record({
    bar: T.String,
    baz: T.String,
  }),
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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valibot

import { schemaTask } from "@trigger.dev/sdk";

import * as v from "valibot";

// For some funny typescript reason, you cannot pass the v.parser directly to schemaTask
const valibotParser = v.parser(
  v.object({
    bar: v.string(),
    baz: v.string(),
  })
);

export const valibotTask = schemaTask({
  id: "types/valibot",
  schema: valibotParser,
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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typebox

import { schemaTask } from "@trigger.dev/sdk";
import { Type } from "@sinclair/typebox";
import { wrap } from "@typeschema/typebox";

export const typeboxTask = schemaTask({
  id: "types/typebox",
  schema: wrap(
    Type.Object({
      bar: Type.String(),
      baz: Type.String(),
    })
  ),
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});

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Custom parser function

You can also define a custom parser function that will be called with the payload before the task is run. The parser function should return the parsed payload or throw an error if the payload is invalid. 
import { schemaTask } from "@trigger.dev/sdk";

export const customParserTask = schemaTask({
  id: "types/custom-parser",
  schema: (data: unknown) => {
    // This is a custom parser, and should do actual parsing (not just casting)
    if (typeof data !== "object") {
      throw new Error("Invalid data");
    }

    const { bar, baz } = data as { bar: string; baz: string };

    return { bar, baz };
  },
  run: async (payload) => {
    console.log(payload.bar, payload.baz);
  },
});