Decision Traces

import cascadeflow

cascadeflow.init(mode="observe")

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

    # Full decision trace
    for record in session.trace():
        print(f"Step {record['step']}: {record['action']} — {record['reason']}")
        print(f"  Model: {record['model']}, Cost: ${record['cost_total']:.4f}")
        print(f"  Budget: {record['budget_state']}, Applied: {record['applied']}")

Step 1: allow — budget ok, model compliant
  Model: gpt-4o-mini, Cost: $0.0003
  Budget: ok, Applied: false
Step 2: allow — budget ok, model compliant
  Model: gpt-4o-mini, Cost: $0.0007
  Budget: ok, Applied: false
Step 3: switch_model — budget pressure, routing to cheaper model
  Model: gpt-4o, Cost: $0.0032
  Budget: warning, Applied: false

from cascadeflow import get_harness_callback_manager, set_harness_callback_manager

cb_manager = get_harness_callback_manager()

# Traces are emitted through the callback system
# Use framework-specific integrations for structured access

summary = session.summary()
# {
#     "cost_total": 0.0032,
#     "steps": 3,
#     "tool_calls": 1,
#     "latency_total_ms": 1250.0,
#     "energy_used": 45.2,
#     "budget_remaining": 0.4968,
# }