aipymemtimeprofiler 1.0.7

AI-enabled time and memory profiler for Python applications

Zero-Hassle AI Enabled Python Profiler for Time, Memory, return object size, cpu memory

AIPyMemTimeProfiler is a zero-configuration, AI-assisted Python profiler that automatically captures function-level memory and time usage — without any code changes.

Powered by LLMs (like Ollama), it not only measures performance but also analyzes your functions for inefficiencies and memory leaks.

Key Features

Zero code modifications — No decorators or wrappers needed

AI-powered analysis of selected functions

Function-level metrics for time, memory, arguments, return object size

Smart project detection — Profiles only your code, not external libraries

Structured JSON output for automation or visualization

Optional profiling of third-party libraries

Supports Flask apps, CLI scripts, nested folder hierarchies

Visualize Performance with Interactive Graphs

AIPyMemTimeProfiler includes a built-in Streamlit dashboard to visualize profiling data in a flamegraph-style format.

What You Get

• Multi-metric overview (time, memory, cpu time, return object size)

• Horizontal flame-style charts

• Interactive tooltips for every function

• No extra config — just run and view

Installation

pip install aipymemtimeprofiler

Quick Start

1. Set Environment Variables

export PROFILER_FILE_PATH="/absolute/path/to/your_script.py"
export PROFILER_DIR_PATH="/absolute/path/to/your/project/root"

If you just want to try, you can test it with sample_project.

export PROFILER_FILE_PATH="$(pwd)/sample_project/inside/app.py": The Python file to be profiled
export PROFILER_DIR_PATH="$(pwd)/sample_project/inside": Root of your project for accurate filtering

CLI entry point

profile_code

Launch the Graph UI

profile_graph

2. If you want to verify 3rd party libraries

export INCLUDE_LIBRARIES=True

LLM Environment Setup

Download Ollama from Ollama

ollama run <yout_model>

If you don't know which model to use.

ollama run deepseek-r1:1.5b: It is preferable as it is light weight.

Set your model env variable.

export AGENT_NAME="<your_model>"
export AGENTIC_PROFILER=True

TL;DR

pip install aipymemtimeprofiler export PROFILER_FILE_PATH=./app.py export PROFILER_DIR_PATH=./ profile_code

You’ll be prompted to select a function to analyze. Example:

Index	Function	File Path
0	calculate_data	/project/app/compute.py
1	main	/project/app/main.py
N	Skip Analysis	-

Time & Memory Metrics

Automatically captures:

• Max execution time (CPU)
• Peak memory usage
• RSS memory growth
• Return object size
• Arguments passed

Project-Aware Analysis

Only your code is profiled.
System libraries and external modules are ignored using intelligent project-path detection.

Profiler Metrics

The following table describes the metrics collected by the profiler:

Metric	Description	Key
Function Name	The name of the function being profiled.	function
File Path	The absolute path to the file where the function is defined.	file
Line Number	The line number where the function starts in the file.	line
Execution Time	Maximum time taken by each function in milliseconds (ms).	max_time_ms
CPU Time	Time spent by the CPU on this function (ms).	cpu_time_ms
Peak Memory Usage	Maximum memory usage during function execution in kilobytes (KB).	max_mem
RSS Memory Growth	Growth in Resident Set Size (RSS) memory in kilobytes (KB), helps spot memory leaks.	mem_growth_rss_kb
Arguments	The arguments passed to the function being profiled.	args
Possible Memory Leak	Indicates if a potential memory leak is detected (if any).	possible_memory_leak
Notes	Any additional notes related to the profiling data.	note
Returned Object Size	The size of the returned object in bytes.	return_obj

Option to select a function for analysis, which is analysed by the Ollama model installed and configured. This is the table providing the options for analysis.

Available Functions for Analysis

Index	Function Name	File Path
0	function_one	/path/to/file_one.py
1	function_two	/path/to/file_two.py
2	function_three	/path/to/file_three.py
3	function_four	/path/to/file_four.py
4	function_five	/path/to/file_five.py
...	...	...
N	function_n	/path/to/file_n.py
N+1	Skip Analysis	-

Structured JSON Reports

{ "function": "calculate_total", "file": "/app/logic.py", "line": 23, "cpu_time_ms": 15.3, "max_mem": 1024, "mem_growth_rss_kb": 300, "args": ["x", "y"], "return_obj": 124, "possible_memory_leak": false, "note": "" }

Works with Any Project Structure

Handles nested folder hierarchies easily — just point to your project root and go.

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Supported Use Cases

• Pure Python projects
• Flask APIs and apps
• Any directory layout

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Want More?

• Console table toggle
• HTML report output

Pull requests are welcome!