Model settings

The agent.yaml sets which model runs the agent, the budget a run may spend, and whether the model exposes its reasoning. The Basic agent example shows these together. The full set of configuration fields is in the configuration reference.

Model

llm.model selects the model and is required. It accepts any model identifier the Anthropic API accepts:

llm:
  model: claude-sonnet-5

Larger models reason better on complex, long-horizon tasks; smaller models like Haiku are faster and cheaper for narrow ones. When the agent exposes ten or more tools it relies on the model’s server-side tool search, which recent models support and older ones (Claude Opus 4.1 and earlier and local models) do not. Set llm.no_tool_search to send every tool directly on an endpoint that does not implement tool search; when a large tool set cannot use it the run warns that all tools are being sent directly. The configuration reference lists the known models and their trade-offs.

Budget

llm.budget limits the agent loop so it cannot run without end:

llm:
  budget:
    max_tokens: 500000
    max_iterations: 50
    call_timeout: 120s

Setting	Description
`max_tokens`	tokens a whole conversation may process, default 500000
`max_iterations`	agent loop iterations one turn may take, default 50
`call_timeout`	per-call timeout as a duration string, default `120s`

The two caps have different scopes. max_iterations applies to a single turn, and every turn of a conversation gets the same allowance. max_tokens applies to the whole conversation, so every turn draws on one allowance. Start a new conversation to get a fresh one.

When a turn reaches the iteration cap, it stops, says so, and you can carry on with another prompt. When a conversation reaches its token cap, it is finished: the next prompt is refused before it runs. Start a new conversation, or raise llm.budget.max_tokens on the machine running the agent.

max_tokens counts tokens, not money. It adds up the uncached input, the output, and both prompt-cache tiers. A cache read counts the same as an uncached input token here even though it costs a fraction as much, so two conversations with the same token count can cost very different amounts. Set this value against your own usage rather than treating it as a spending limit.

Thinking

Extended thinking lets the model expose its reasoning before it answers. Some providers call this reasoning rather than thinking; it is the same setting.

llm:
  thinking:
    enabled: true

Reasoning is never displayed unless asked for. --thinking (or THINKING=1) shows it, on fisk run and on fisk session show --transcript.

thinking=N on the run summary and in the TUI status bar reports the tokens spent reasoning, shown whether or not reasoning is displayed. It is part of the output half of tokens=in/out, not extra.

Note

Older models that predate adaptive thinking, such as Sonnet 4.5 and Haiku 4.5, reject the parameter. Both explicit states send one, so for those models remove the thinking block rather than setting enabled: false. The same applies to an endpoint behind ANTHROPIC_BASE_URL whose proxy does not implement it.

Terminal UI

These harness settings govern the full-screen UI for an agent, independent of the per-run --no-tui flag:

harness:
  no_tui: true
  no_bell: true

no_tui is a persistent off switch: the agent always uses the line-by-line output, even on an interactive terminal, and the command line cannot turn the UI back on. Use --no-tui instead for a one-off run.
no_bell silences the terminal bell. By default the full-screen UI rings the bell each time a run blocks on an approval gate or an ask_human_* prompt, so a waiting run is noticed even when unattended.

Both are negative switches and have no effect in the line UI.