AI-powered wizards

The extension includes AI-powered wizards that use your existing LLM providers to generate configurations and tasks automatically. This reduces manual setup to a minimum.

Setup wizard

The setup wizard guides first-time configuration in five steps:

Connect — enter your provider endpoint and API key.
Verify — test the connection.
Models — fetch and select models from the provider catalogue.
Configure — generate and select configuration proposals for the chosen models.
Review & Save — review the provider, selected models and configurations, then save their records.

Five-step setup wizard with progress indicator showing Connect, Verify, Models, Configure, and Save steps — The setup wizard walks through provider creation, connection testing, model selection, configuration proposals, and reviewing and saving the records in five steps.

The setup wizard is the first entry in AI > Setup. While no provider is configured, the AI > Overview page opens with a notice and a button to it. On TYPO3 v14 the AI > Setup overview shows the same notice; TYPO3 v13 has no such overview and opens the last used entry of AI > Setup, or the setup wizard on the first visit.

Configuration wizard

The configuration wizard generates a complete LLM configuration using AI. Instead of filling in each field manually, describe your use case in plain language and the wizard generates everything.

Navigate to AI > Setup > Configurations.
Click Create with AI.
Describe your use case (e.g., "summarize blog posts in three sentences").
The wizard generates: identifier, name, system prompt, temperature, and all other parameters.
Review and click Save.

Configuration wizard form with a plain-language description field and generated configuration preview — The configuration wizard generates all fields from a natural-language description.

Task wizard

The task wizard creates a complete task setup — a task and a dedicated configuration — in one step.

Navigate to AI > Authoring > Tasks.
Click Create with AI.
Describe the task (e.g., "extract the five most important keywords from an article").
The wizard generates: a task with prompt template, a configuration with system prompt and parameters, and a model recommendation.
Review and click Save.

Task wizard form with description field and generated task preview — The task wizard generates a complete task and configuration from a description.

Model discovery

On the model edit form, use the Fetch Models button to query the provider API. This auto-populates available models with their capabilities, context length, and pricing metadata.

What the capability checkboxes are seeded with

Discovery can use provider-reported capabilities, a bundled model catalogue, family assumptions and operator edits. The provenance matters: a seeded checkbox is not a successful test of that capability. See Managing models for how provenance is shown. The following table summarizes the sources used by discovery:

Provider	Discovery source
Mistral	chat, tools, vision (per-model `capabilities`)
OpenRouter	chat, tools, vision (`supported_parameters`, `architecture.input_modalities`)
Ollama	chat, tools, vision, embeddings (`/api/show`, Ollama 0.6 and newer)
Gemini	chat, streaming, embeddings (`supportedGenerationMethods`); vision and tools come from the built-in table for known model ids
Anthropic	chat, vision, tools, streaming for every model the listing returns — it returns Claude chat models only, and they all have them
OpenAI	from the built-in table, keyed by model id; an id outside it is seeded from its prefix (`dall-e-`, `tts-`, `whisper-`) and otherwise chat alone
Groq	chat only — the listing carries no capability field at all

Where the API reports nothing, the record is seeded with the narrowest true statement rather than a guess. Check the capability checkboxes after discovery and tick what the model actually does: the field is yours to edit, and configurations that select models by criteria match against it. Fetch Models in the model form keeps those edits where discovery cannot say what a model lacks: for a Groq model, an Ollama model without a reported list, and an OpenAI or Gemini chat model outside the built-in table, it adds what it knows and leaves the other ticks of a record of the same kind alone.

Note

Models discovered by an earlier version carry capabilities that were partly guessed from the model name. An upgrade cannot tell an operator's deliberate edit from a stale seed, so it does not overwrite either. Running Fetch Models again replaces a guessed tick only where discovery states the list: the built-in tables, Mistral, OpenRouter, and Ollama models whose capabilities the host reported. For the models named above, including Ollama models it did not look up or could not read, untick a stale capability by hand.

Recommended workflow

For a fresh installation:

Run the Setup wizard from the dashboard to create your first provider, select models and save the generated configurations.
Use the Configuration wizard to create additional use-case configurations (one per use case in your extensions).
Use the Task wizard to create reusable prompt templates for editors.
Share configuration identifiers with your extension developers — they reference them in code via $configRepository->findByIdentifier('...').

For ongoing maintenance:

Add providers when you need additional AI services or separate prod/dev keys.
Fetch models periodically to pick up new models from providers.
Edit configurations to tune prompts and parameters — changes take effect immediately without code deployment.