Budget Enforcement

import cascadeflow

cascadeflow.init(mode="enforce")

with cascadeflow.run(budget=0.50) as session:
    result = await agent.run("Research and summarize this topic")

    summary = session.summary()
    print(f"Cost: ${summary['cost_total']:.4f}")
    print(f"Budget remaining: ${summary['budget_remaining']:.4f}")
    print(f"Steps completed: {summary['steps']}")

with cascadeflow.run(budget=1.00, max_tool_calls=5) as session:
    result = await agent.run("Search and analyze this dataset")
    # Stops when either budget or tool call limit is hit

@cascadeflow.agent(budget=0.10)
async def triage_agent(query: str):
    """Cheap triage — $0.10 max."""
    return await llm.complete(query)

@cascadeflow.agent(budget=2.00)
async def research_agent(query: str):
    """Deep research — $2.00 max."""
    return await llm.complete(query)

from cascadeflow.telemetry import BudgetConfig, CostTracker, strict_budget_enforcement

tracker = CostTracker(
    budget_config=BudgetConfig(
        daily_limit=10.0,
        per_query_limit=0.50,
        alert_threshold=0.8,
    ),
    enforcement_callback=strict_budget_enforcement,
)

# Track costs manually
tracker.track(model="gpt-4o", cost=0.003)
print(f"Daily spend: ${tracker.daily_spend:.4f}")

with cascadeflow.run(budget=0.50) as session:
    result = await agent.run("Multi-step analysis")

    for record in session.trace():
        if record['action'] == 'stop':
            print(f"Stopped at step {record['step']}: {record['reason']}")
        else:
            print(f"Step {record['step']}: {record['action']} (${record['cost_total']:.4f})")