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fluffbuzz infer is the canonical headless surface for provider-backed inference workflows. It intentionally exposes capability families, not raw gateway RPC names and not raw agent tool ids.

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Turn infer into a skill

Copy and paste this to an agent:
A good infer-based skill should:
  • map common user intents to the correct infer subcommand
  • include a few canonical infer examples for the workflows it covers
  • prefer fluffbuzz infer ... in examples and suggestions
  • avoid re-documenting the entire infer surface inside the skill body
Typical infer-focused skill coverage:
  • fluffbuzz infer model run
  • fluffbuzz infer image generate
  • fluffbuzz infer audio transcribe
  • fluffbuzz infer tts convert
  • fluffbuzz infer web search
  • fluffbuzz infer embedding create

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Why use infer

fluffbuzz infer provides one consistent CLI for provider-backed inference tasks inside FluffBuzz. Benefits:
  • Use the providers and models already configured in FluffBuzz instead of wiring up one-off wrappers for each backend.
  • Keep model, image, audio transcription, TTS, video, web, and embedding workflows under one command tree.
  • Use a stable --json output shape for scripts, automation, and agent-driven workflows.
  • Prefer a first-party FluffBuzz surface when the task is fundamentally “run inference.”
  • Use the normal local path without requiring the gateway for most infer commands.

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Command tree

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Common tasks

This table maps common inference tasks to the corresponding infer command.

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Behavior

  • fluffbuzz infer ... is the primary CLI surface for these workflows.
  • Use --json when the output will be consumed by another command or script.
  • Use --provider or --model provider/model when a specific backend is required.
  • For image describe, audio transcribe, and video describe, --model must use the form <provider/model>.
  • For image describe, an explicit --model runs that provider/model directly. The model must be image-capable in the model catalog or provider config. codex/<model> runs a bounded Codex app-server image-understanding turn; openai-codex/<model> uses the OpenAI Codex OAuth provider path.
  • Stateless execution commands default to local.
  • Gateway-managed state commands default to gateway.
  • The normal local path does not require the gateway to be running.

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Model

Use model for provider-backed text inference and model/provider inspection.
Notes:
  • model run reuses the agent runtime so provider/model overrides behave like normal agent execution.
  • model auth login, model auth logout, and model auth status manage saved provider auth state.

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Image

Use image for generation, edit, and description.
Notes:
  • Use image edit when starting from existing input files.
  • For image describe, --model must be an image-capable <provider/model>.
  • For local Ollama vision models, pull the model first and set OLLAMA_API_KEY to any placeholder value, for example ollama-local. See Ollama.

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Audio

Use audio for file transcription.
Notes:
  • audio transcribe is for file transcription, not realtime session management.
  • --model must be <provider/model>.

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TTS

Use tts for speech synthesis and TTS provider state.
Notes:
  • tts status defaults to gateway because it reflects gateway-managed TTS state.
  • Use tts providers, tts voices, and tts set-provider to inspect and configure TTS behavior.

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Video

Use video for generation and description.
Notes:
  • --model must be <provider/model> for video describe.

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Web

Use web for search and fetch workflows.
Notes:
  • Use web providers to inspect available, configured, and selected providers.

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Embedding

Use embedding for vector creation and embedding provider inspection.

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JSON output

Infer commands normalize JSON output under a shared envelope:
Top-level fields are stable:
  • ok
  • capability
  • transport
  • provider
  • model
  • attempts
  • outputs
  • error

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Common pitfalls

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Notes

  • fluffbuzz capability ... is an alias for fluffbuzz infer ....