@convex-dev/ai-sdk-provider

Use the Convex AI gateway with the AI SDK from a Convex action.

"use node";

import { convexGateway } from "@convex-dev/ai-sdk-provider";
import { embedMany, generateText } from "ai";
import { action } from "./_generated/server";
import { v } from "convex/values";

export const chat = action({
  args: { prompt: v.string() },
  handler: async (_ctx, { prompt }) => {
    const { text } = await generateText({
      model: convexGateway("anthropic/claude-sonnet-4.5"),
      prompt,
    });
    return text;
  },
});

export const embed = action({
  args: { values: v.array(v.string()) },
  handler: async (_ctx, { values }) => {
    const { embeddings } = await embedMany({
      model: convexGateway.embeddingModel("openai/text-embedding-3-small"),
      values,
    });
    return embeddings;
  },
});

Embedding batches larger than the gateway's 512-input limit are split into multiple requests by the AI SDK.

Choose a model interface

For text generation, start with convexGateway(model). It works across model providers and is the recommended default. Use an endpoint-specific model only when you need features unique to Anthropic Messages or OpenAI Responses:

convexGateway.messages("anthropic/claude-sonnet-4.5");
convexGateway.responses("openai/gpt-5");

Structured decisions

Use provider version 0.2.1 or later for correct validation of Jev's rounded scores and probabilities.

Jev evaluates choice, score, and boolean questions about the context you provide in state. Call AI SDK's experimental evaluate from an action:

import { experimental_evaluate as evaluate } from "ai";
import { convexGateway } from "@convex-dev/ai-sdk-provider";

const decision = await evaluate({
  model: convexGateway.evaluationModel("typesafe/jev-1.13"),
  state: { ticket: "Customer cannot sign in" },
  questions: {
    priority: {
      type: "choice",
      instructions: "Choose the response priority",
      criteria: {
        urgent: "Respond now",
        normal: "Respond today",
      },
    },
    needsReview: {
      type: "boolean",
      instructions: "Does a human need to review this?",
    },
  },
});

console.log(decision.answers.priority.choice);
console.log(decision.answers.needsReview.probability);

This requires AI SDK 7.0.105 or later. The evaluation interface and /alpha/decisions endpoint are experimental. Authentication is handled automatically. Pass abortSignal to evaluate to cancel a request. Dollar cost is available in decision.providerMetadata?.convexGateway?.cost. The original gateway response, including provider-specific fields such as confidence, is available in decision.response.body.

getServiceToken("ai-gateway") supplies a short-lived deployment JWT. The action runtime caches and refreshes the credential as needed, so convexGateway(...) is recommended to call it repeatedly.

Requires Convex 1.45 or later, AI SDK 7.0.105 or later, and Node.js 22 or later.

Generate images

Image generation is in alpha. The request and response format may change.

import { generateImage } from "ai";
import { convexGateway } from "@convex-dev/ai-sdk-provider";

const { images } = await generateImage({
  model: convexGateway.imageModel("openai/gpt-image-1"),
  prompt: "A mountain lake at sunrise",
});

Generate videos

Video generation is in alpha. APIs may change, and completion callbacks are best effort. Save async operation handles to check status and retrieve results.

import { experimental_generateVideo as generateVideo } from "ai";
import { convexGateway } from "@convex-dev/ai-sdk-provider";

const { video, providerMetadata } = await generateVideo({
  model: convexGateway.videoModel("google/veo-3.1"),
  prompt: "A camera pan across a mountain lake",
  duration: 8,
  aspectRatio: "16:9",
  resolution: "1280x720",
});

const bytes = video.uint8Array;
const cost = providerMetadata?.convexGateway?.cost;

The call waits for generation and download, with a ten-minute default timeout and a 64 MiB limit per video. Store the returned bytes in Convex file storage. The AI SDK splits n > 1 into separate requests. Cancelling a request does not cancel the upstream job and can still incur a charge.

An image in the prompt becomes the first frame. Use frameImages for explicit frames, inputReferences for image/audio/video references, and generateAudio for audio. fps returns an unsupported-option warning.

providerOptions.convexGateway accepts resolution (such as 720p), generate_audio, frame_images, and input_references. Standard SDK options take precedence. Supported values depend on the OpenRouter model.

Async videos

With AI SDK 7.0.83, use experimental_startVideo to submit a job without waiting for the video. See the async availability requirements before using these routes.

Create an application job first. Use its ID as requestId in the callback URL so the receiver can find the job and its saved secret.

import { experimental_startVideo as startVideo } from "ai";
import { convexGateway } from "@convex-dev/ai-sdk-provider";

async function startJob(requestId: string) {
  const modelId = "google/veo-3.1";
  const started = await startVideo({
    model: convexGateway.videoModel(modelId),
    prompt: "A camera pan across a mountain lake",
    duration: 8,
    webhookUrl: `${process.env.CONVEX_SITE_URL}/video-complete?requestId=${requestId}`,
    maxRetries: 0,
  });

  return {
    modelId,
    operation: started.operation,
    inferenceId: started.providerMetadata?.convexGateway?.inferenceId,
    webhookSecret: started.providerMetadata?.convexGateway?.webhookSecret,
  };
}

Save the returned job in private application storage before the action returns. Automatic submission retries are disabled because a lost response can still mean a paid job was accepted. Callback URLs must use the deployment's own HTTPS <deployment>.convex.site origin; redirects are rejected. If CONVEX_SITE_URL uses a custom domain, use the deployment's default convex.site origin in the example instead.

Receive a callback

Load the saved job and verify the raw request body before updating application state:

import { verifyVideoWebhook } from "@convex-dev/ai-sdk-provider";

const event = await verifyVideoWebhook({
  body: await request.text(),
  signature: request.headers.get("x-convex-video-signature"),
  secret: savedJob.webhookSecret,
});

if (event.id !== savedJob.inferenceId) {
  return new Response("Wrong job", { status: 400 });
}

Deduplicate (event.id, event.status) in the same mutation that saves the event. For completed, schedule an action to download the video. Record failed, cancelled, or expired as terminal failures. Return 204 after saving the event; return a non-2xx response if the job's secret has not been saved yet.

Retrieve the video

In a later action, use the saved operation to check status and download:

const model = convexGateway.videoModel(savedJob.modelId);
const status = await model.getStatus({ operation: savedJob.operation });

if (status.status === "completed") {
  const result = await model.download({ operation: savedJob.operation });
  const video = result.videos[0];
  const cost = result.providerMetadata?.convexGateway?.cost;
}

getStatus returns pending, completed, or error. When the status is error, the error field contains the error message. Store the downloaded video in application storage, since each download call fetches it again. Operations expire after seven days; upstream video retention may be shorter.

Omit webhookUrl to use status checks alone. If a completion callback is missed, use the saved operation to check the job and retrieve its result. Applications that need automatic recovery can periodically check unfinished jobs.