Harness Overview

from cascadeflow import HarnessConfig

config = HarnessConfig(
    mode="enforce",                    # "off" | "observe" | "enforce"
    verbose=False,                     # Print decisions to stderr
    budget=0.50,                       # Max USD for the run (None = unlimited)
    max_tool_calls=10,                 # Max tool/function calls (None = unlimited)
    max_latency_ms=5000.0,             # Max wall-clock ms per call (None = unlimited)
    max_energy=100.0,                  # Max energy units (None = unlimited)
    kpi_targets={"quality": 0.9},      # Target values for KPI dimensions
    kpi_weights={                      # Relative importance of each dimension
        "quality": 0.6,
        "cost": 0.3,
        "latency": 0.1,
    },
    compliance="gdpr",                 # "gdpr" | "hipaa" | "pci" | "strict" | None
)

import type { HarnessConfig } from '@cascadeflow/core';

const config: HarnessConfig = {
  mode: 'enforce',
  verbose: false,
  budget: 0.50,
  maxToolCalls: 10,
  maxLatencyMs: 5000,
  maxEnergy: 100,
  kpiWeights: {
    quality: 0.6,
    cost: 0.3,
    latency: 0.1,
  },
  compliance: 'regulated',
};

import cascadeflow

# Global activation
cascadeflow.init(mode="observe")

# Scoped run with overrides
with cascadeflow.run(budget=0.50, max_tool_calls=10) as session:
    # agent code
    pass

# Decorated agent function
@cascadeflow.agent(budget=0.20, compliance="gdpr")
async def my_agent(query: str):
    pass

import { harnessAgent, init, run } from '@cascadeflow/core';

init({ mode: 'observe' });

await run({ budget: 0.50, maxToolCalls: 10 }, async (session) => {
  await agent.run('Analyze this data');
  console.log(session.summary());
});

const myAgent = harnessAgent({ budget: 0.20, compliance: 'regulated' })(
  async (query: string) => agent.run(query),
);