138 lines
4.7 KiB
Plaintext
138 lines
4.7 KiB
Plaintext
---
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title: "Adding a custom LLM provider"
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description: "Create a custom provider definition in OpenWork Cloud and import it into the desktop app"
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---
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Use this when the provider is not in the catalog. The Cloud dashboard stores the shared credential once and then lets desktop workspaces import it.
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<Frame>
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</Frame>
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## Create the custom provider
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1. Open `LLM Providers`.
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2. Click `Add Provider`.
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3. Switch to `Custom provider`.
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4. Give it a `Name` (the `Provider ID` fills in automatically).
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5. Enter the `Base URL` of the OpenAI-compatible endpoint (for Azure AI Foundry this looks like `https://my-resource.openai.azure.com/openai/v1`).
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6. List the `Model IDs` the endpoint serves — on Azure AI Foundry these are your deployment names.
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7. Paste the shared `API key / credential`.
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8. Choose `People access` and/or `Team access`.
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9. Click `Create Provider`.
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That's it — no JSON required for OpenAI-compatible endpoints.
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### Advanced: raw JSON config
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If the provider needs more than the form covers (a different AI SDK package, per-model limits or modalities, custom options), click `Advanced: edit as JSON` and provide a models.dev-style definition. The JSON must include `id`, `name`, `npm`, `env`, and `models`; `api` is optional, but most OpenAI-compatible providers use it. The editor requires valid JSON, at least one environment variable, and at least one model.
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```json
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{
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"id": "custom-provider",
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"name": "Custom Provider",
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"npm": "@ai-sdk/openai-compatible",
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"env": [
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"CUSTOM_PROVIDER_API_KEY"
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],
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"doc": "https://example.com/docs/models",
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"api": "https://api.example.com/v1",
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"models": [
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{
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"id": "custom-provider/example-model",
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"name": "Example Model",
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"attachment": false,
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"reasoning": false,
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"tool_call": true,
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"structured_output": true,
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"temperature": true,
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"release_date": "2026-01-01",
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"last_updated": "2026-01-01",
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"open_weights": false,
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"limit": {
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"context": 128000,
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"input": 128000,
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"output": 8192
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},
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"modalities": {
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"input": [
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"text"
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],
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"output": [
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"text"
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]
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}
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}
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]
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}
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```
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## Import it into the desktop app
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1. Open `Settings > Cloud Providers`.
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2. Choose the correct `Active org`.
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3. Under `Cloud providers`, click `Import`.
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4. Reload the workspace when OpenWork asks.
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### Functional example: an LLM gateway (Infron)
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An LLM gateway is one OpenAI-compatible endpoint that fans out to many model providers. If you want to run the routing yourself, [LiteLLM](https://infron.ai/docs/frameworks-and-integrations/litellm) is a good starting point. [Infron](https://infron.ai) is the hosted option: one API key gets you every model in its [marketplace](https://infron.ai/models) with automatic provider fallbacks and a single invoice, so adding a new LLM to OpenWork is just another entry under `models`.
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Grab a key from the [API Keys dashboard](https://infron.ai/dashboard/apiKeys). If you don't have an account yet, sign up at [infron.ai/login](https://infron.ai/login) and their [quickstart](https://infron.ai/docs/overview/quickstart/text) walks you through your first request.
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Paste the key into `API key / credential` and use this JSON:
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```json
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{
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"id": "infron",
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"name": "Infron",
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"npm": "@ai-sdk/openai-compatible",
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"env": [
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"INFRON_API_KEY"
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],
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"doc": "https://infron.ai/docs/frameworks-and-integrations/openwork",
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"api": "https://llm.onerouter.pro/v1",
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"models": [
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{
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"id": "deepseek/deepseek-v3.2",
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"name": "DeepSeek V3.2",
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"attachment": false,
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"reasoning": true,
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"tool_call": true,
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"structured_output": true,
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"temperature": true,
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"release_date": "2025-09-29",
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"last_updated": "2025-09-29",
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"open_weights": true,
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"limit": {
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"context": 128000,
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"input": 128000,
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"output": 8192
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},
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"modalities": {
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"input": ["text"],
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"output": ["text"]
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}
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}
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]
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}
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```
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Once imported, Infron models show up in the Chat model picker alongside everything else:
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<Frame>
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</Frame>
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Pick the model and it's live in the session footer:
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<Frame>
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</Frame>
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Add more entries under `models` to expose other routes the gateway supports (e.g. `openai/gpt-5.4`, `google/gemini-2.5-flash`).
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## When to use a cloud provider
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Use a Cloud provider when the setup is meant to be shared across an org or team. For solo use, configuring it directly in the desktop app is simpler.
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