cascadeflow.init()

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

Signature

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

Parameters

Parameter	Type	Default	Description
`mode`	`"off" \| "observe" \| "enforce"`	`"off"`	Harness mode
`config`	`HarnessConfig \| None`	`None`	Full configuration (overrides mode)
`verbose`	`bool`	`False`	Print decisions to stderr

Returns

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

Usage

Minimal

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

With config

from cascadeflow import HarnessConfig

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

Environment-driven

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

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

Python

TypeScript

Signature

Parameters

Returns

Usage

Minimal

With config

Environment-driven

Notes

​Signature

​Parameters

​Returns

​Usage

​Minimal

​With config

​Environment-driven

​Notes

Signature

Parameters

Returns

Usage

Minimal

With config

Environment-driven

Notes