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The LLM Repository holds the models an app can use. Each entry, an LLM config, stores the provider, the model, the credentials and default settings. Agents and workflows refer to an entry by its id, never to a provider key directly. Keeping models in one place means:
  • Keys stay out of YAML. Agent and workflow definitions can be shared, versioned and committed to git without leaking secrets. Keys are stored securely and never returned in API responses.
  • Models are swapped in one place. Point the config at a new deployment or model and every agent that uses it follows.
  • Rate limits and costs are easier to reason about, because you know which agents share a model.

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Supported providers

Each config also has a Model Type: LLM for chat models, or Embedding for models that turn text into vectors (used by knowledge bases).

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Adding a model in the console

Go to LLM Repository → Add LLM:
The Add LLM to Repository dialog
Click Add Model. The new entry’s Id is shown in the list; that is what agents use.

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Using a model from an agent or workflow

In any agent definition, inline or saved, point the model block at the config:
provider: rootflo means “use a model from this app’s LLM Repository”. The agent picks up the provider, model, key and base URL from the config at run time.
Prefer provider: rootflo over writing a provider and model name directly in the YAML (provider: openai, name: gpt-4o). Direct provider blocks work, but they need the key from the server’s environment and bypass the repository.
Model parameters on the agent (for example settings.temperature: 0) override the config’s defaults for that agent.

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Trying another model without editing the agent

The agent’s chat panel in the console has a model picker under the conversation. Pick another config to test the same agent against a different model. Nothing is saved.

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Calling a model directly

An application sometimes needs a plain model call outside an agent. The app can proxy that call so the key stays on the server:
The request body is passed through unchanged to the model’s base URL with <provider path> appended, using the config’s key. For an Azure OpenAI deployment, for example:

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API

Deleting a config breaks every agent and workflow that refers to it by id. Check before deleting, or update the config in place instead.