Profiling

Measure agent performance — timing, API calls, streaming, and memory

Profile agent runs with bounded buffers that stay safe in long-lived production workloads.

from praisonaiagents import Agent
from praisonai.profiler import Profiler, profile

Profiler.enable()

@profile(category="agent")
def run_task(prompt: str):
    agent = Agent(name="assistant", instructions="Be helpful")
    return agent.start(prompt)

result = run_task("Summarise quarterly sales")
print(Profiler.get_statistics())

The user runs a profiled task; timing and memory stats accumulate in a bounded buffer.

How It Works

Quick Start

1. Simple Usage

   Enable profiling and wrap an agent turn:

   from praisonaiagents import Agent
   from praisonai.profiler import Profiler, profile
   
   Profiler.enable()
   
   @profile(category="agent")
   def run_task(prompt: str):
       agent = Agent(name="assistant", instructions="Be helpful")
       return agent.start(prompt)
   
   run_task("Summarise quarterly sales")
   Profiler.report()

2. With Configuration

   Time blocks and export a report:

   from praisonaiagents import Agent
   from praisonai.profiler import Profiler
   
   Profiler.enable()
   
   with Profiler.block("agent_execution"):
       agent = Agent(name="assistant", instructions="Be helpful")
       agent.start("Analyse market trends")
   
   Profiler.export_to_file("profile_report.json", format="json")
   stats = Profiler.get_statistics()
   print(f"P95: {stats['p95']:.2f}ms")

How It Works

Profiling is off by default — decorators and context managers are no-ops until you call Profiler.enable() or set PRAISONAI_PROFILE=1.

Each buffer uses a bounded deque — when full, oldest records drop. See Performance Profiling for per-agent isolation via set_profiler().

Configuration Options

Best Practices

Related