AimableDocs
DocsAPI ReferenceRelease Notes

Admin LLM Providers

Updated 9 July 2026

Admin LLM Providers API

The admin-llm-providers domain in the Aimable Platform API (v0.1.0) enables platform administrators to manage and inspect available Large Language Model (LLM) providers and their supported models. This is essential for configuring AI capabilities across the platform, ensuring compatibility, monitoring performance, and enabling new AI integrations.

This API is designed for internal administrative use—typical consumers include platform operators or DevOps engineers who need visibility into LLM provider configurations and model availability.


Key Concepts

  • LLM Provider: A third-party service (e.g., OpenAI, Anthropic, Google) that offers language models via API.
    • Identified by id, name, and slug.
    • May include optional metadata like icon_url and favicon_url for UI display.
  • LLM Model: A specific model offered by a provider (e.g., gpt-4, claude-3-opus).
    • Belongs to one provider via provider_id.
    • Has a model_name, provider_model_id, and status (e.g., active, deprecated).

These endpoints are read-only and used to list providers and their associated models.


How to Use the Endpoints

1. List All LLM Providers

Retrieve all configured LLM providers in the system.

Endpoint:
GET /api/v1/admin/llm-providers

Example Request:

bash
curl -X GET "https://platform.example.com/api/v1/admin/llm-providers" \
  -H "X-API-Key: your-secret-api-key"

Example Response (200 OK):

json
{
  "data": [
    {
      "id": "a1b2c3d4-1234-5678-90ab-bf1a88f8c7e3",
      "name": "OpenAI",
      "slug": "openai",
      "provider_type": "openai",
      "icon_url": "https://cdn.example.com/icons/openai.svg",
      "favicon_url": "https://cdn.example.com/favicons/openai.png",
      "created_at": "2024-01-10T12:00:00Z",
      "updated_at": "2or-10T12:00:00Z"
    },
    {
      "id": "e5f6g7h8-3456-7890-abcd-d92b77a9f0d1",
      "name": "Anthropic",
      "slug": "anthropic",
      "provider_type": "anthropic",
      "icon_url": "https://cdn.example.com/icons/anthropic.svg",
      "created_at": "2024-01-11T08:30:00Z",
      "updated_at": "2024-01-11T08:30:00Z"
    }
  ]
}

2. List Models for a Specific Provider

Fetch all models available from a given LLM provider using its provider_id.

Endpoint:
GET /api/v1/admin/llm-providers/{provider_id}/models

Example Request:

bash
curl -X GET "https://platform.example.com/api/v1/admin/llm-providers/a1b2c3d4-1234-5678-90ab-bf1a88f8c7e3/models" \
  -H "X-API-Key: your-secret-api-key"

Example Response (200 OK):

json
{
  "data": [
    {
      "id": "m001",
      "provider_id": "a1b2c3d4-1234-5678-90ab-bf1a88f8c7e3",
      "model_name": "GPT-4 Turbo",
      "provider_model_id": "gpt-4-turbo-2024-04-09",
      "status": "active",
      "capabilities_json": {
        "vision": true,
        "function_calling": true,
        "max_tokens": 128000
      },
      "provider_slug": "openai",
      "provider_icon_url": "https://cdn.example.com/icons/openai.svg",
      "created_at": "2024-02-01T10:00:00Z",
      "updated_at": "2024-02-01T10:00:00Z"
    },
    {
      "id": "m002",
      "provider_id": "a1b2c3d4-1234-5678-90ab-bf1a88f8c7e3",
      "model_name": "GPT-3.5 Turbo",
      "provider_model_id": "gpt-3.5-turbo-1106",
      "status": "active",
      "capabilities_json": {
        "vision": false,
        "function_calling": true,
        "max_tokens": 16385
      },
      "created_at": "2023-11-06T09:00:00Z",
      "updated_at": "2023-11-06T09:00:00Z"
    }
  ]
}

Common Workflow

A typical administrative workflow might involve:

  1. Discover Available Providers
    Call GET /v1/admin/llm-providers to get a list of all integrated LLM providers.

  2. Inspect Models of a Provider
    Use the id from a provider (e.g., OpenAI) to list all its models:

    bash
    GET /v1/admin/llm-providers/a1b2c3d4-.../models
  3. Evaluate Model Capabilities
    Use the capabilities_json field to determine which models support features like vision, function calling, or long contexts.

This flow helps in making decisions about which models to expose to end users or use in backend pipelines.


See Authentication & errors for request authentication and the standard error responses.

Use these endpoints to maintain up-to-date knowledge of your AI infrastructure and support dynamic model routing and feature flagging across your applications.

See also