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2.5 Connect an AI model

Moss needs a working model to answer questions and operate devices. Most new users can rely on the default after sign-in; manual setup is mainly for corporate model endpoints or running a small model locally.

Three paths​

SituationPath
Fresh install, just want it workingAfter sign-in, use the built-in recommended models
Team already has a model service or keysSettings → AI engine
Want a fast local small modelAI capabilities → Local LLM: install and pull a model

Default models​

After sign-in you can usually go straight to the workbench—no keys required first.

If the composer shows “fast/reasoning model not configured” or “model unreachable,” use AI model settings or Local model from the hint to jump to the right page.

Bring your own model service​

If your team provided an endpoint, open Settings → AI engine and set it as the reasoning or fast model.

Config center · AI engine: configure default models for reasoning mode and fast mode

ModeRole
ReasoningMain chat, complex tasks, planning, troubleshooting
FastShort Q&A, summarizing results, light file browsing

Common fields:

FieldWhat to enter
Display nameFriendly label for this configuration
ProviderMatch your team’s platform
Model nameName from your team or provider
Base URLAPI base from your team; leave empty if using the platform default
API keyKey from your team or provider (enter in the settings page)

If unsure, share the provider’s field guide or a redacted screenshot (hide the key) with Moss to map fields. Do not paste API keys into chat.

Local Ollama​

Local LLM lets you:

Local LLM page: install and start Ollama, download a model, and set it as Moss fast mode

  1. Install or detect local Ollama.
  2. Start the local model server.
  3. Enter a model name to pull.
  4. Run a one-shot chat test.
  5. One-click set as Moss fast model.

Local models suit quick answers, summaries, and light Q&A. They run on your PC; board-side OpenClaw needs its own agent settings on the device.

Next steps​

When models work, continue to 2.6 First conversation.

Deeper docs: