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Adapters

OpenAI-Compatible Adapter

Many providers expose the OpenAI Chat Completions API (/chat/completions) — DeepSeek, Moonshot/Kimi, Together, Fireworks, Cerebras, Alibaba Qwen, Perplexity, NVIDIA NIM, and local servers like LM Studio, Ollama, and vLLM. Instead of a dedicated package per provider, TanStack AI ships one generic adapter: point it at any compatible baseURL, give it your models, and you get the same type-safe chat() experience as the first-class adapters.

Use this when your provider speaks the OpenAI Chat Completions wire format but doesn't have its own @tanstack/ai-* package. If a dedicated adapter exists (OpenAI, Grok, Groq, OpenRouter), prefer it — those carry curated per-model metadata.

Installation

The adapter ships inside @tanstack/ai-openai under the /compatible subpath — no extra install:

sh
npm install @tanstack/ai-openai
npm install @tanstack/ai-openai

Basic Usage

Configure the provider once with openaiCompatible({ baseURL, apiKey, models }), then select a model per call. The returned model name is a type-safe union of the models you declared:

typescript
import { chat } from "@tanstack/ai";
import { openaiCompatible } from "@tanstack/ai-openai/compatible";

const deepseek = openaiCompatible({
  name: "deepseek", // optional label shown in devtools/errors (default: "openai-compatible")
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: ["deepseek-chat", "deepseek-reasoner"],
});

const stream = chat({
  adapter: deepseek("deepseek-chat"),
  messages: [{ role: "user", content: "Hello!" }],
});
import { chat } from "@tanstack/ai";
import { openaiCompatible } from "@tanstack/ai-openai/compatible";

const deepseek = openaiCompatible({
  name: "deepseek", // optional label shown in devtools/errors (default: "openai-compatible")
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: ["deepseek-chat", "deepseek-reasoner"],
});

const stream = chat({
  adapter: deepseek("deepseek-chat"),
  messages: [{ role: "user", content: "Hello!" }],
});

deepseek("deepseek-reasoner") is valid; deepseek("gpt-4o") is a type error — only declared models are accepted.

One-Shot Usage

For a single model, skip the provider-factory and build the adapter inline with openaiCompatibleText:

typescript
import { chat } from "@tanstack/ai";
import { openaiCompatibleText } from "@tanstack/ai-openai/compatible";

const stream = chat({
  adapter: openaiCompatibleText("deepseek-chat", {
    baseURL: "https://api.deepseek.com/v1",
    apiKey: process.env.DEEPSEEK_API_KEY!,
  }),
  messages: [{ role: "user", content: "Hello!" }],
});
import { chat } from "@tanstack/ai";
import { openaiCompatibleText } from "@tanstack/ai-openai/compatible";

const stream = chat({
  adapter: openaiCompatibleText("deepseek-chat", {
    baseURL: "https://api.deepseek.com/v1",
    apiKey: process.env.DEEPSEEK_API_KEY!,
  }),
  messages: [{ role: "user", content: "Hello!" }],
});

Declaring Models

The models array accepts two forms, which you can mix:

  • A bare string — gets optimistic defaults: text + image input, with streaming, function_calling, and structured_outputs support. Good for mainstream chat models.
  • A createModel(name, capabilities) definition — declares precise per-model capabilities so the types match reality (e.g. a reasoning model with no image input).
typescript
import { openaiCompatible } from "@tanstack/ai-openai/compatible";
import { createModel } from "@tanstack/ai";

const provider = openaiCompatible({
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: [
    "deepseek-chat", // string → optimistic defaults
    createModel("deepseek-reasoner", {
      input: ["text"], // text only
      features: ["reasoning", "structured_outputs"],
    }),
  ],
});
import { openaiCompatible } from "@tanstack/ai-openai/compatible";
import { createModel } from "@tanstack/ai";

const provider = openaiCompatible({
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: [
    "deepseek-chat", // string → optimistic defaults
    createModel("deepseek-reasoner", {
      input: ["text"], // text only
      features: ["reasoning", "structured_outputs"],
    }),
  ],
});

Capabilities are enforced at the type level. If a provider rejects a feature at runtime (e.g. tools on a model that doesn't support them), declare that model with createModel and omit the unsupported feature so the types stop you from calling it.

Configuration

openaiCompatible accepts every OpenAI SDK ClientOptions field besides apiKey/baseURL (which are required and promoted to the top level). The most useful are defaultHeaders and defaultQuery, for providers that need extra auth or routing parameters:

typescript
const provider = openaiCompatible({
  baseURL: "https://api.example.com/v1",
  apiKey: process.env.EXAMPLE_API_KEY!,
  models: ["some-model"],
  defaultHeaders: { "X-Custom-Header": "value" },
  defaultQuery: { "api-version": "2026-01-01" },
});
const provider = openaiCompatible({
  baseURL: "https://api.example.com/v1",
  apiKey: process.env.EXAMPLE_API_KEY!,
  models: ["some-model"],
  defaultHeaders: { "X-Custom-Header": "value" },
  defaultQuery: { "api-version": "2026-01-01" },
});

Chat Completions vs Responses

By default the adapter targets the Chat Completions API (/chat/completions) — the surface virtually every compatible provider implements. For the rare provider that also implements OpenAI's Responses API (e.g. Azure OpenAI), opt in with api: "responses":

typescript
const provider = openaiCompatible({
  baseURL: "https://my-resource.openai.azure.com/openai/v1",
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  models: ["gpt-4o"],
  api: "responses", // default is "chat-completions"
});
const provider = openaiCompatible({
  baseURL: "https://my-resource.openai.azure.com/openai/v1",
  apiKey: process.env.AZURE_OPENAI_API_KEY!,
  models: ["gpt-4o"],
  api: "responses", // default is "chat-completions"
});

Supported Providers

Any provider implementing the OpenAI Chat Completions API works. Common ones are below — verify the baseURL and model ids against each provider's current docs, since they change over time. Set the API key via the provider's own environment variable and pass it as apiKey.

ProviderbaseURLExample model
DeepSeekhttps://api.deepseek.com/v1deepseek-chat, deepseek-reasoner
Moonshot / Kimihttps://api.moonshot.ai/v1kimi-k2-0711-preview
Alibaba Qwen (DashScope, intl)https://dashscope-intl.aliyuncs.com/compatible-mode/v1qwen-max, qwen-plus
Alibaba Qwen (DashScope, China)https://dashscope.aliyuncs.com/compatible-mode/v1qwen-max
Together AIhttps://api.together.xyz/v1meta-llama/Llama-3.3-70B-Instruct-Turbo
Fireworks AIhttps://api.fireworks.ai/inference/v1accounts/fireworks/models/llama-v3p3-70b-instruct
Cerebrashttps://api.cerebras.ai/v1llama-3.3-70b
DeepInfrahttps://api.deepinfra.com/v1/openaimeta-llama/Llama-3.3-70B-Instruct
Perplexityhttps://api.perplexity.aisonar, sonar-pro
Mistralhttps://api.mistral.ai/v1mistral-large-latest
Nebiushttps://api.studio.nebius.ai/v1meta-llama/Llama-3.3-70B-Instruct
Z.AI (GLM)https://api.z.ai/api/paas/v4glm-4.6
Basetenhttps://inference.baseten.co/v1model-dependent
Hugging Face (router)https://router.huggingface.co/v1meta-llama/Llama-3.3-70B-Instruct
NVIDIA NIMhttps://integrate.api.nvidia.com/v1meta/llama-3.3-70b-instruct

Local & Self-Hosted Servers

Point the adapter at any local OpenAI-compatible server. The API key is usually a placeholder:

typescript
import { openaiCompatible } from "@tanstack/ai-openai/compatible";

// LM Studio
const lmstudio = openaiCompatible({
  name: "lmstudio",
  baseURL: "http://localhost:1234/v1",
  apiKey: "lm-studio",
  models: ["local-model"],
});

// vLLM
const vllm = openaiCompatible({
  name: "vllm",
  baseURL: "http://localhost:8000/v1",
  apiKey: "not-needed",
  models: ["meta-llama/Llama-3.3-70B-Instruct"],
});

// Ollama's OpenAI-compatible endpoint
const ollama = openaiCompatible({
  name: "ollama",
  baseURL: "http://localhost:11434/v1",
  apiKey: "ollama",
  models: ["llama3.3"],
});
import { openaiCompatible } from "@tanstack/ai-openai/compatible";

// LM Studio
const lmstudio = openaiCompatible({
  name: "lmstudio",
  baseURL: "http://localhost:1234/v1",
  apiKey: "lm-studio",
  models: ["local-model"],
});

// vLLM
const vllm = openaiCompatible({
  name: "vllm",
  baseURL: "http://localhost:8000/v1",
  apiKey: "not-needed",
  models: ["meta-llama/Llama-3.3-70B-Instruct"],
});

// Ollama's OpenAI-compatible endpoint
const ollama = openaiCompatible({
  name: "ollama",
  baseURL: "http://localhost:11434/v1",
  apiKey: "ollama",
  models: ["llama3.3"],
});

Ollama also has a dedicated adapter, @tanstack/ai-ollama, which understands its native API. Use openaiCompatible only if you specifically want Ollama's OpenAI-compatible surface.

Azure OpenAI

Azure uses a resource-scoped URL and a separate API-version. Use the /openai/v1 endpoint with defaultQuery for the version and defaultHeaders for the api-key header:

typescript
const azure = openaiCompatible({
  name: "azure",
  baseURL: "https://YOUR_RESOURCE.openai.azure.com/openai/v1",
  apiKey: process.env.AZURE_OPENAI_API_KEY!, // also sent as Bearer; Azure accepts the api-key header below
  models: ["gpt-4o"], // your Azure deployment name
  defaultQuery: { "api-version": "2026-01-01-preview" },
  defaultHeaders: { "api-key": process.env.AZURE_OPENAI_API_KEY! },
});
const azure = openaiCompatible({
  name: "azure",
  baseURL: "https://YOUR_RESOURCE.openai.azure.com/openai/v1",
  apiKey: process.env.AZURE_OPENAI_API_KEY!, // also sent as Bearer; Azure accepts the api-key header below
  models: ["gpt-4o"], // your Azure deployment name
  defaultQuery: { "api-version": "2026-01-01-preview" },
  defaultHeaders: { "api-key": process.env.AZURE_OPENAI_API_KEY! },
});

Confirm the current api-version and endpoint shape in Azure's documentation — Azure's API surface evolves independently of OpenAI's.

Example: With Tools

Tools work exactly as they do with any other adapter, for models that support function calling:

typescript
import { chat, toolDefinition } from "@tanstack/ai";
import { openaiCompatible } from "@tanstack/ai-openai/compatible";
import { z } from "zod";

const getWeatherDef = toolDefinition({
  name: "get_weather",
  description: "Get the current weather",
  inputSchema: z.object({ location: z.string() }),
});

const getWeather = getWeatherDef.server(async ({ location }) => {
  return { temperature: 72, conditions: "sunny" };
});

const deepseek = openaiCompatible({
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: ["deepseek-chat"],
});

const stream = chat({
  adapter: deepseek("deepseek-chat"),
  messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
  tools: [getWeather],
});
import { chat, toolDefinition } from "@tanstack/ai";
import { openaiCompatible } from "@tanstack/ai-openai/compatible";
import { z } from "zod";

const getWeatherDef = toolDefinition({
  name: "get_weather",
  description: "Get the current weather",
  inputSchema: z.object({ location: z.string() }),
});

const getWeather = getWeatherDef.server(async ({ location }) => {
  return { temperature: 72, conditions: "sunny" };
});

const deepseek = openaiCompatible({
  baseURL: "https://api.deepseek.com/v1",
  apiKey: process.env.DEEPSEEK_API_KEY!,
  models: ["deepseek-chat"],
});

const stream = chat({
  adapter: deepseek("deepseek-chat"),
  messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
  tools: [getWeather],
});

Next Steps

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