Provider mock

kind = "mock" is an in-process scripted chat provider. It records every chat / chat-stream invocation (model, messages, tools, and the opaque params the brain passed) and returns scripted turns, so a brain’s provider traffic is fully deterministic and inspectable.

[providers.openai]
kind = "mock"
turns = [
  { content = "first reply" },
  { tool_call = { id = "call-1", name = "echo", arguments = '{"input":"hi"}' } },
  { reasoning = "let me think", content = "the answer", usage = { prompt_tokens = 12, completion_tokens = 5 } },
]
models = ["gpt-test"]

Keys

• turns — the scripted turns, popped one per chat. A turn is a table with any of:
  • content = "..." — text; the mock emits it and a terminal stop finish reason.
  • reasoning = "..." — reasoning/thinking text; emitted as its own delta before the content/tool-call delta.
  • tool_call = { id, name, arguments } — a tool call; the mock emits the call and a terminal tool_calls finish reason. arguments is the raw JSON string the model would have produced. Write it as a TOML string (arguments = '{"input":"hi"}') or, more readably, as the inline JSON value (arguments = { input = "hi" }); an inline value is stringified when the mock is built, so the guest always sees the wire string.
  • usage = { prompt_tokens = 12, completion_tokens = 5, total_tokens = 17 } — token counts attached to the turn’s terminal delta (every key optional).
  • Once the script is exhausted the last turn repeats for every further chat, so a looping brain keeps working without re-listing the script.
• models — the model names list-models returns. Defaults to ["mock-model"].

A bare kind = "mock" with no turns emits an empty stream, which is useful for tests that only assert the call itself. Because the recorded call includes params, an assertion can pin a brain’s per-call generation settings:

[assertions.alice]
events = [
  { kind = "call", op = "chat", detail = { params = { temperature = 0.2 } } },
]