Anvia

Setup ​

Install the provider adapter beside Anvia core:

sh
pnpm add @anvia/core @anvia/openai

Set OPENAI_API_KEY in the server environment, then construct the client in server-only code:

ts
import { OpenAIClient } from '@anvia/openai'

export const openai = new OpenAIClient({
  apiKey: process.env.OPENAI_API_KEY!,
})

Do not place the client or API key in a browser bundle. Browser applications should call an application route that owns the model request.

Client options ​

apiKey authenticates an SDK client created by the adapter. baseUrl targets another OpenAI-shaped endpoint. headers adds endpoint-specific default headers. client injects an already-created OpenAI SDK instance. Select responses or chat with the required api property on completionModel().

The constructor requires either apiKey or client. Keep a preconfigured client useful when transport, organization-wide SDK configuration, or application dependency injection belongs elsewhere.

ts
import OpenAI from 'openai'
import { OpenAIClient } from '@anvia/openai'

const sdk = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY!,
})

export const openai = new OpenAIClient({ client: sdk })

Custom endpoints ​

Compatible endpoints often share request shapes without supporting the same tools, schemas, reasoning fields, streaming chunks, or media behavior.

ts
const compatible = new OpenAIClient({
  apiKey: process.env.COMPATIBLE_API_KEY!,
  baseUrl: 'https://provider.example.com/v1',
})

const compatibleModel = compatible.completionModel({
  modelId: 'provider/model-name',
  api: 'chat',
})

Use the dedicated Compatible APIs guide and smoke test every required workflow. The current documented @anvia/openai public surface does not provide a dedicated Azure OpenAI client, so this guide does not prescribe unverified Azure configuration.

Export models, not credentials ​

A small model module keeps provider details out of agents and business logic:

ts
export const supportModel = openai.completionModel({
    modelId: 'gpt-5.6-sol',
    api: 'responses',
})
export const knowledgeEmbeddings = openai.embeddingModel({
    modelId: 'text-embedding-3-small'
})

Model-name types provide autocomplete for known IDs while still allowing custom strings. Validate a configured model ID at startup and with a deployment smoke test; autocomplete is not availability discovery.

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