Context Observability & History

from praisonaiagents import ContextManager

manager = ContextManager(model="gpt-4o-mini")

# Process messages (generates events)
result = manager.process(messages=messages)

# Get optimization history
history = manager.get_history()
for event in history:
    print(f"{event['event_type']}: {event['tokens_saved']} saved")

@dataclass
class OptimizationEvent:
    timestamp: str              # ISO timestamp
    event_type: str             # Event type
    strategy: str               # Strategy used
    tokens_before: int          # Tokens before
    tokens_after: int           # Tokens after
    tokens_saved: int           # Tokens saved
    messages_affected: int      # Messages changed
    details: Dict[str, Any]     # Additional info

# Get all history
history = manager.get_history()

# Filter by event type
compactions = [e for e in history if e['event_type'] == 'auto_compact']

# Get total tokens saved
total_saved = sum(e['tokens_saved'] for e in history)

# Show optimization history
praisonai chat
> /context history

# Output:
# Time                     Event                Tokens       Saved
# ----------------------------------------------------------------------
# 2024-01-01T12:00:00      overflow_detected       45,000          -
# 2024-01-01T12:00:01      auto_compact           45,000     -15,000
# 2024-01-01T12:00:01      benefit_check          30,000          -

stats = manager.get_stats()

print(f"Model: {stats['model']}")
print(f"Utilization: {stats['utilization']*100:.1f}%")
print(f"History events: {stats['history_events']}")
print(f"Warnings: {stats['warnings']}")

# Show summary
> /context show

# Show token ledger
> /context stats

# Show budget allocation
> /context budget

config = ManagerConfig(
    monitor_enabled=True,
    monitor_format="json",
)

# JSON snapshots include:
# - optimization_history
# - estimation_metrics
# - warnings