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name: inference-providers description: "Operate the TaskingAI inference microservice provider/model catalog, credential validation, chat completion, text embedding, rerank, provider icons, custom/local providers, proxy safety, and provider troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TaskingAI Inference Providers

Use this sub-skill when a task is about TaskingAI's inference microservice: provider and model schema discovery, provider credentials, credential verification, chat completion, text embedding, rerank, provider icons, OpenAI-compatible/custom hosts, local model providers, proxy/URL blacklist behavior, or provider-specific failure diagnosis.

Load this first

1. For provider/model selection, credential fields, wildcard schemas, local/custom-host choices, and catalog counts, read references/provider-catalog.md.
2. For HTTP route contracts, request/response bodies, validation order, streaming, fallbacks, proxy/custom-header behavior, and icon routes, read references/api-reference.md.
3. For source-backed native-test behavior, credential-sensitive checks, and safe skip criteria, read references/native-testing.md.
4. For symptoms, likely causes, and recovery steps, especially PROVIDER_ERROR versus REQUEST_VALIDATION_ERROR, read references/troubleshooting.md.
5. To inspect a user-supplied TaskingAI source tree without network calls or credentials, use scripts/inspect_inference_catalog.py.

Source-backed facts to preserve

• The verified inference catalog snapshot contains 35 provider directories.
• Model schema types are exactly chat_completion, text_embedding, rerank, and wildcard.
• Provider and model metadata are loaded from bundled YAML resources into runtime caches before route handlers use them.
• Static import probes passed in Python 3.10 for APIRouter, Provider, and DebugChatCompletionModel.
• Credential verification can be schema-only at provider level or can call a default chat, embedding, or rerank model depending on provider metadata.

Quick routing

• Backend object lifecycle, model-instance APIs exposed by the main backend, backend proxy endpoints, authentication, and database/API object semantics belong to ../backend-api/.
• Deployment variables, container startup, service URLs, image tags, port wiring, TASKINGAI_INFERENCE_URL, global outbound proxy setup, and storage configuration belong to ../deployment-configuration/.
• Plugin tool bundles, plugin vision/tool schemas, plugin execution, and plugin image generation belong to ../plugin-bundles/.
• Stay here for provider catalog semantics, inference route payloads, credentials, custom_host, Ollama/LM Studio/LocalAI, provider icons, and provider-specific troubleshooting.

Operating workflow

1. Classify the requested model capability. Use chat_completion, text_embedding, rerank, or wildcard; do not assume a wildcard schema has enough information without provider_model_id, model_type, and any required properties.
2. Choose the provider path. Use provider selection guidance for hosted providers, hosted OpenAI-compatible routers, custom_host, and local OpenAI-compatible servers.
3. Validate credentials before invocation. Compare the request to the provider credentials_schema; pass either plaintext credentials or encrypted_credentials, never both. Use credential validation semantics when the user needs encrypted credentials for later reuse.
4. Build the inference payload. Use chat completion, text embedding, or rerank contracts. Check model properties and configs before provider calls.
5. Handle proxy and URL safety deliberately. Request-level proxy values must be HTTPS and are rejected if they contain a configured provider URL blacklist entry. custom_host endpoint URLs are checked against the same blacklist before a provider call.
6. Diagnose by error class. Treat schema/model/property/proxy failures as request validation first. Treat upstream HTTP/API/auth/quota failures as provider failures. Use troubleshooting before retrying with new credentials.

Hard usability cases covered here

• Choose between custom_host, Ollama/LM Studio/LocalAI, OpenRouter, and a named hosted provider for an OpenAI-compatible user task while preserving the difference between actual provider model IDs and TaskingAI model schema IDs.
• Diagnose PROVIDER_ERROR versus REQUEST_VALIDATION_ERROR during credential validation by separating local schema/model/proxy checks from upstream provider responses.