// List configured AI providers and models in Adaline. Use when discovering available LLM providers, filtering models, or checking provider configuration.
| name | adaline-providers |
| description | List configured AI providers and models in Adaline. Use when discovering available LLM providers, filtering models, or checking provider configuration. |
Providers are configured LLM connections in an Adaline workspace. Models are provider-specific model records available to prompts and deployments.
Key terms:
openai, anthropic, or googlemodel name and enabled flagSet ADALINE_API_KEY. Base URL: https://api.adaline.ai/v2.
| Symptom | First Fix |
|---|---|
| Provider name missing | Read provider, not legacy name |
| Model name missing | Read model, not legacy name |
| Need provider models inline | Call GET /providers/{providerId}?includeModels=true |
| Model unavailable | Filter to enabled: true |
curl "https://api.adaline.ai/v2/providers" \
-H "Authorization: Bearer $ADALINE_API_KEY"
curl "https://api.adaline.ai/v2/providers/provider_abc123?includeModels=true" \
-H "Authorization: Bearer $ADALINE_API_KEY"
curl "https://api.adaline.ai/v2/models?providerId=provider_abc123" \
-H "Authorization: Bearer $ADALINE_API_KEY"
const providers = await adaline.providers.list();
const provider = await adaline.providers.get({ providerId, includeModels: true });
const models = await adaline.models.list({ providerId });
providers = await adaline.providers.list()
provider = await adaline.providers.get(provider_id=provider_id, include_models=True)
models = await adaline.models.list(provider_id=provider_id)
providerId when building prompt/model selectors.modelSettings only as capability metadata; pass actual runtime values in prompt/deployment config settings.See references/api.md for schemas and examples.
Create and manage evaluation datasets in Adaline. Use when building test cases, adding dataset columns/rows, importing data, or triggering dynamic columns.
Fetch deployed prompt snapshots from Adaline at runtime. Use when integrating prompt deployments, environment-based latest lookups, prompt caching, or pinned deployment IDs.
Run and manage evaluations in Adaline to test prompt quality at scale. Use when creating evaluation runs, polling status, analyzing results, or cancelling runs.
Create and manage evaluators in Adaline to score prompt outputs. Use when setting up LLM-as-a-judge, JavaScript, text-matcher, cost, latency, or response-length evaluators.
High-level guide for integrating your AI application with Adaline. Use when starting a new Adaline integration, choosing between API/SDK approaches, or planning which Adaline features to adopt.
Send traces and spans to Adaline for AI agent observability. Use when instrumenting LLM calls, tools, retrieval, embeddings, guardrails, or custom operations.