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Activate the harness globally. All subsequent LLM calls (OpenAI, Anthropic) are automatically tracked.

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Signature

def init(
    mode: HarnessMode = "off",
    *,
    config: Optional[HarnessConfig] = None,
    verbose: bool = False,
) -> HarnessInitReport

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Parameters

ParameterTypeDefaultDescription
mode"off" | "observe" | "enforce""off"Harness mode
configHarnessConfig | NoneNoneFull configuration (overrides mode)
verboseboolFalsePrint decisions to stderr

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Returns

HarnessInitReport — confirmation of harness activation with mode and configuration summary.

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Usage

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Minimal

import cascadeflow
cascadeflow.init(mode="observe")

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With config

from cascadeflow import HarnessConfig

config = HarnessConfig(
    mode="enforce",
    budget=1.00,
    compliance="gdpr",
    verbose=True,
)
cascadeflow.init(config=config)

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Environment-driven

import os
cascadeflow.init(mode=os.getenv("CASCADEFLOW_MODE", "observe"))

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Notes

  • Call init() once at application startup, before any LLM calls
  • Calling init() again replaces the previous configuration
  • Use cascadeflow.reset() to deactivate the harness
  • init(mode="off") is equivalent to not calling init() at all