Profiling

from praisonai.profiler import Profiler, profile

# Enable profiling
Profiler.enable()

# Profile a function
@profile
def my_agent_task():
    # Your agent code here
    pass

# Profile a block of code
with Profiler.block("agent_initialization"):
    agent = Agent(instructions="You are helpful")

# Get report
Profiler.report()

export PRAISONAI_PROFILE=1

from praisonai.profiler import profile, profile_async

@profile
def sync_function():
    """Automatically profiled when enabled."""
    return "result"

@profile_async
async def async_function():
    """Profile async functions."""
    return await some_async_call()

# With custom category
@profile(category="llm_call")
def call_llm():
    pass

from praisonai.profiler import Profiler

with Profiler.block("data_processing"):
    # Code to profile
    process_data()

with Profiler.block("model_inference", category="inference"):
    result = model.predict(data)

from praisonai.profiler import Profiler, profile_api

# Using decorator
@profile_api(endpoint="openai/chat/completions")
def call_openai():
    response = client.chat.completions.create(...)
    return response

# Using context manager
with Profiler.api_call("https://api.openai.com/v1/chat/completions", method="POST") as call:
    response = requests.post(url, json=data)
    call['status_code'] = response.status_code
    call['response_size'] = len(response.content)

# Get all API calls
api_calls = Profiler.get_api_calls()
for call in api_calls:
    print(f"{call.endpoint}: {call.duration_ms:.2f}ms")

from praisonai.profiler import Profiler, StreamingTracker

# Using context manager
with Profiler.streaming("chat_completion") as tracker:
    for chunk in stream:
        if tracker._first_token_time is None:
            tracker.first_token()  # Mark TTFT
        tracker.chunk()
        process(chunk)

# Manual tracking
tracker = StreamingTracker("my_stream")
tracker.start()

for i, chunk in enumerate(response_stream):
    if i == 0:
        tracker.first_token()
    tracker.chunk()
    
tracker.end(total_tokens=150)

# Get streaming records
streams = Profiler.get_streaming_records()
for s in streams:
    print(f"TTFT: {s.ttft_ms:.2f}ms, Total: {s.total_ms:.2f}ms, Chunks: {s.chunk_count}")

from praisonai.profiler import Profiler

# Profile memory for a block
with Profiler.memory("agent_creation"):
    agent = Agent(instructions="...", tools=[...])

# Get memory records
memories = Profiler.get_memory_records()
for m in memories:
    print(f"{m.name}: current={m.current_kb:.1f}KB, peak={m.peak_kb:.1f}KB")

# Take a snapshot
snapshot = Profiler.memory_snapshot()
print(f"Current: {snapshot['current_kb']:.1f}KB")
print(f"Peak: {snapshot['peak_kb']:.1f}KB")

from praisonai.profiler import Profiler

# Get overall statistics
stats = Profiler.get_statistics()
print(f"P50 (Median): {stats['p50']:.2f}ms")
print(f"P95: {stats['p95']:.2f}ms")
print(f"P99: {stats['p99']:.2f}ms")
print(f"Mean: {stats['mean']:.2f}ms")
print(f"Std Dev: {stats['std_dev']:.2f}ms")

# Get statistics for specific category
api_stats = Profiler.get_statistics(category="api")
llm_stats = Profiler.get_statistics(category="llm_call")

from praisonai.profiler import Profiler, profile_detailed

# Using decorator
@profile_detailed
def heavy_computation():
    return sum(i * i for i in range(100000))

# Using context manager
with Profiler.cprofile("agent_run") as stats:
    result = agent.run()

# Get cProfile stats
cprofile_data = Profiler.get_cprofile_stats()
for entry in cprofile_data:
    print(f"Operation: {entry['name']}")
    print(entry['stats'])

from praisonai.profiler import profile_lines

@profile_lines
def detailed_function():
    a = expensive_operation_1()  # Line timing
    b = expensive_operation_2()  # Line timing
    return a + b

# Get line profile data
line_data = Profiler.get_line_profile_data()

from praisonai.profiler import Profiler

# Print to console
Profiler.report()

# Get as string
report_text = Profiler.report(output="string")

# Export as JSON string
json_report = Profiler.export_json()

# Save to file
Profiler.export_to_file("profile_report.json", format="json")

# Export as HTML string
html_report = Profiler.export_html()

# Save to file
Profiler.export_to_file("profile_report.html", format="html")

# Export flamegraph as SVG
Profiler.export_flamegraph("profile.svg")

from praisonai.profiler import profile_imports, time_import

# Profile imports in a block
with profile_imports() as profiler:
    import pandas
    import numpy
    from praisonaiagents import Agent

# Get slowest imports
slowest = profiler.get_slowest(n=5)
for imp in slowest:
    print(f"{imp.module}: {imp.duration_ms:.2f}ms")

# Quick single import timing
duration = time_import("torch")
print(f"torch import: {duration:.2f}ms")

from praisonai.profiler import Profiler, profile

Profiler.disable()  # Profiling off

@profile
def fast_function():
    return 1 + 1

# No overhead - decorator is a no-op when disabled
for _ in range(1000000):
    fast_function()  # Full speed

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

# Enable profiling
Profiler.enable()

@profile_api(endpoint="openai/chat")
def create_completion(prompt):
    return client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )

@profile(category="agent")
def run_agent(task):
    with Profiler.memory("agent_init"):
        agent = Agent(instructions="You are helpful")
    
    with Profiler.block("agent_execution"):
        result = agent.chat(task)
    
    return result

# Run with profiling
result = run_agent("Explain quantum computing")

# Get comprehensive report
print("\n=== Profiling Report ===")
Profiler.report()

# Get statistics
stats = Profiler.get_statistics()
print(f"\nP95 Latency: {stats['p95']:.2f}ms")

# Export detailed report
Profiler.export_to_file("agent_profile.html", format="html")
Profiler.export_flamegraph("agent_flamegraph.svg")

# Cleanup
Profiler.disable()
Profiler.clear()