OpenAI Compatible Providers

You can use the OpenAI Compatible Provider package to use language model providers that implement the OpenAI API.

Below we focus on the general setup and provider instance creation. You can also write a custom provider package leveraging the OpenAI Compatible package.

We provide detailed documentation for the following OpenAI compatible providers:

The general setup and provider instance creation is the same for all of these providers.

Setup

The OpenAI Compatible provider is available via the @ai-sdk/openai-compatible module. You can install it with:

Provider Instance

To use an OpenAI compatible provider, you can create a custom provider instance with the createOpenAICompatible function from @ai-sdk/openai-compatible:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
  includeUsage: true, // Include usage information in streaming responses
});

You can use the following optional settings to customize the provider instance:

baseURL string

Set the URL prefix for API calls.
apiKey string

API key for authenticating requests. If specified, adds an Authorization header to request headers with the value Bearer <apiKey>. This will be added before any headers potentially specified in the headers option.
headers Record<string,string>

Optional custom headers to include in requests. These will be added to request headers after any headers potentially added by use of the apiKey option.
queryParams Record<string,string>

Optional custom url query parameters to include in request urls.
fetch (input: RequestInfo, init?: RequestInit) => Promise<Response>

Custom fetch implementation. Defaults to the global fetch function. You can use it as a middleware to intercept requests, or to provide a custom fetch implementation for e.g. testing.
includeUsage boolean

Include usage information in streaming responses. When enabled, usage data will be included in the response metadata for streaming requests. Defaults to undefined (false).
supportsStructuredOutputs boolean

Set to true if the provider supports structured outputs. Only relevant for provider(), provider.chatModel(), and provider.languageModel().
transformRequestBody (args: Record<string, any>) => Record<string, any>

Optional function to transform the request body before sending it to the API. This is useful for proxy providers that may require a different request format than the official OpenAI API.
metadataExtractor MetadataExtractor

Optional metadata extractor to capture provider-specific metadata from API responses. See Custom Metadata Extraction for details.

Language Models

You can create provider models using a provider instance. The first argument is the model id, e.g. model-id.

const model = provider('model-id');

You can also use the following factory methods:

provider.languageModel('model-id') - creates a chat language model (same as provider('model-id'))
provider.chatModel('model-id') - creates a chat language model

Supported Capabilities

Chat models created with this provider support the following capabilities:

Text generation - Generate text completions
Streaming - Stream text responses in real-time
Tool calling - Call tools/functions with streaming support
Structured outputs - Generate JSON with schema validation (when supportsStructuredOutputs is enabled)
Reasoning content - Support for models that return reasoning/thinking tokens (e.g., DeepSeek R1)
System messages - Support for system prompts
Multi-modal inputs - Support for images and other content types (provider-dependent)

Example

You can use provider language models to generate text with the generateText function:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateText } from 'ai';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const { text } = await generateText({
  model: provider('model-id'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

Including model ids for auto-completion

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateText } from 'ai';

type ExampleChatModelIds =
  | 'meta-llama/Llama-3-70b-chat-hf'
  | 'meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo'
  | (string & {});

type ExampleCompletionModelIds =
  | 'codellama/CodeLlama-34b-Instruct-hf'
  | 'Qwen/Qwen2.5-Coder-32B-Instruct'
  | (string & {});

type ExampleEmbeddingModelIds =
  | 'BAAI/bge-large-en-v1.5'
  | 'bert-base-uncased'
  | (string & {});

type ExampleImageModelIds = 'dall-e-3' | 'stable-diffusion-xl' | (string & {});

const model = createOpenAICompatible<
  ExampleChatModelIds,
  ExampleCompletionModelIds,
  ExampleEmbeddingModelIds,
  ExampleImageModelIds
>({
  name: 'example',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.example.com/v1',
});

// Subsequent calls to e.g. `model.chatModel` will auto-complete the model id
// from the list of `ExampleChatModelIds` while still allowing free-form
// strings as well.

const { text } = await generateText({
  model: model.chatModel('meta-llama/Llama-3-70b-chat-hf'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

Custom query parameters

Some providers may require custom query parameters. An example is the Azure AI Model Inference API which requires an api-version query parameter.

You can set these via the optional queryParams provider setting. These will be added to all requests made by the provider.

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
  queryParams: {
    'api-version': '1.0.0',
  },
});

For example, with the above configuration, API requests would include the query parameter in the URL like: https://api.provider.com/v1/chat/completions?api-version=1.0.0.

Image Models

You can create image models using the .imageModel() factory method:

const model = provider.imageModel('model-id');

Basic Image Generation

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateImage } from 'ai';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const { images } = await generateImage({
  model: provider.imageModel('model-id'),
  prompt: 'A futuristic cityscape at sunset',
  size: '1024x1024',
});

Image Editing

The OpenAI Compatible provider supports image editing through the /images/edits endpoint. Pass input images via prompt.images to transform or edit existing images.

Basic Image Editing

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateImage } from 'ai';
import fs from 'fs';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const imageBuffer = fs.readFileSync('./input-image.png');

const { images } = await generateImage({
  model: provider.imageModel('model-id'),
  prompt: {
    text: 'Turn the cat into a dog but retain the style of the original image',
    images: [imageBuffer],
  },
});

Inpainting with Mask

Edit specific parts of an image using a mask:

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateImage } from 'ai';
import fs from 'fs';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const image = fs.readFileSync('./input-image.png');
const mask = fs.readFileSync('./mask.png');

const { images } = await generateImage({
  model: provider.imageModel('model-id'),
  prompt: {
    text: 'A sunlit indoor lounge area with a pool containing a flamingo',
    images: [image],
    mask,
  },
});

<Note> Input images can be provided as `Buffer`, `ArrayBuffer`, `Uint8Array`, base64-encoded strings, or URLs. The provider will automatically download URL-based images and convert them to the appropriate format. </Note>

Embedding Models

You can create embedding models using the .embeddingModel() factory method:

const model = provider.embeddingModel('model-id');

Example

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { embed } from 'ai';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const { embedding } = await embed({
  model: provider.embeddingModel('text-embedding-model'),
  value: 'The quick brown fox jumps over the lazy dog',
});

Embedding Model Options

The following provider options are available for embedding models via providerOptions:

dimensions number

The number of dimensions the resulting output embeddings should have. Only supported in models that allow dimension configuration.
user string

A unique identifier representing your end-user, which can help providers to monitor and detect abuse.

const { embedding } = await embed({
  model: provider.embeddingModel('text-embedding-model'),
  value: 'The quick brown fox jumps over the lazy dog',
  providerOptions: {
    providerName: {
      dimensions: 512,
      user: 'user-123',
    },
  },
});

Completion Models

You can create completion models (for text completion, not chat) using the .completionModel() factory method:

const model = provider.completionModel('model-id');

Example

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { generateText } from 'ai';

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const { text } = await generateText({
  model: provider.completionModel('completion-model-id'),
  prompt: 'The quick brown fox',
});

Completion Model Options

The following provider options are available for completion models via providerOptions:

echo boolean

Echo back the prompt in addition to the completion.
logitBias Record<string, number>

Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID) to an associated bias value from -100 to 100.
suffix string

The suffix that comes after a completion of inserted text.
user string

A unique identifier representing your end-user, which can help providers to monitor and detect abuse.

const { text } = await generateText({
  model: provider.completionModel('completion-model-id'),
  prompt: 'The quick brown fox',
  providerOptions: {
    providerName: {
      echo: true,
      suffix: ' The end.',
      user: 'user-123',
    },
  },
});

Chat Model Options

The following provider options are available for chat models via providerOptions:

user string

A unique identifier representing your end-user, which can help the provider to monitor and detect abuse.
reasoningEffort string

Reasoning effort for reasoning models. The exact values depend on the provider.
textVerbosity string

Controls the verbosity of the generated text. The exact values depend on the provider.
strictJsonSchema boolean

Whether to use strict JSON schema validation. When true, the model uses constrained decoding to guarantee schema compliance. Only used when the provider supports structured outputs and a schema is provided. Defaults to true.

const { text } = await generateText({
  model: provider('model-id'),
  prompt: 'Solve this step by step: What is 15 * 23?',
  providerOptions: {
    providerName: {
      user: 'user-123',
      reasoningEffort: 'high',
    },
  },
});

Provider-specific options

The OpenAI Compatible provider supports adding provider-specific options to the request body. These are specified with the providerOptions field in the request body.

For example, if you create a provider instance with the name providerName, you can add a customOption field to the request body like this:

const provider = createOpenAICompatible({
  name: 'providerName',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
});

const { text } = await generateText({
  model: provider('model-id'),
  prompt: 'Hello',
  providerOptions: {
    providerName: { customOption: 'magic-value' },
  },
});

Note that the providerOptions key will be in camelCase. If you set the provider name to provider-name, the options still need to be set on providerOptions.providerName.

The request body sent to the provider will include the customOption field with the value magic-value. This gives you an easy way to add provider-specific options to requests without having to modify the provider or AI SDK code.

Custom Metadata Extraction

The OpenAI Compatible provider supports extracting provider-specific metadata from API responses through metadata extractors. These extractors allow you to capture additional information returned by the provider beyond the standard response format.

Metadata extractors receive the raw, unprocessed response data from the provider, giving you complete flexibility to extract any custom fields or experimental features that the provider may include. This is particularly useful when:

Working with providers that include non-standard response fields
Experimenting with beta or preview features
Capturing provider-specific metrics or debugging information
Supporting rapid provider API evolution without SDK changes

Metadata extractors work with both streaming and non-streaming chat completions and consist of two main components:

A function to extract metadata from complete responses
A streaming extractor that can accumulate metadata across chunks in a streaming response

Here's an example metadata extractor that captures both standard and custom provider data:

typescript

import { MetadataExtractor } from '@ai-sdk/openai-compatible';

const myMetadataExtractor: MetadataExtractor = {
  // Process complete, non-streaming responses
  extractMetadata: ({ parsedBody }) => {
    // You have access to the complete raw response
    // Extract any fields the provider includes
    return {
      myProvider: {
        standardUsage: parsedBody.usage,
        experimentalFeatures: parsedBody.beta_features,
        customMetrics: {
          processingTime: parsedBody.server_timing?.total_ms,
          modelVersion: parsedBody.model_version,
          // ... any other provider-specific data
        },
      },
    };
  },

  // Process streaming responses
  createStreamExtractor: () => {
    let accumulatedData = {
      timing: [],
      customFields: {},
    };

    return {
      // Process each chunk's raw data
      processChunk: parsedChunk => {
        if (parsedChunk.server_timing) {
          accumulatedData.timing.push(parsedChunk.server_timing);
        }
        if (parsedChunk.custom_data) {
          Object.assign(accumulatedData.customFields, parsedChunk.custom_data);
        }
      },
      // Build final metadata from accumulated data
      buildMetadata: () => ({
        myProvider: {
          streamTiming: accumulatedData.timing,
          customData: accumulatedData.customFields,
        },
      }),
    };
  },
};

You can provide a metadata extractor when creating your provider instance:

typescript

const provider = createOpenAICompatible({
  name: 'my-provider',
  apiKey: process.env.PROVIDER_API_KEY,
  baseURL: 'https://api.provider.com/v1',
  metadataExtractor: myMetadataExtractor,
});

The extracted metadata will be included in the response under the providerMetadata field:

typescript

const { text, providerMetadata } = await generateText({
  model: provider('model-id'),
  prompt: 'Hello',
});

console.log(providerMetadata.myProvider.customMetric);

This allows you to access provider-specific information while maintaining a consistent interface across different providers.