aipymemtimeprofiler 0.4.0

AI-enabled time and memory profiler for Python applications

Zero-Hassle AI Enabled Python Profiler for Time & Memory

AI enabled lightweight time and memory profiler for Python, zero-configuration python profiler that captures function-level performance metrics along with analysis of function implementation with AI Agent with no code changes. Ideal for:

• Python scripts
• Flask projects
• Real-time debugging of performance bottlenecks

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Features

Zero Code Changes

Just run your Python script — no decorators, annotations, or modifications needed.

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

Each function includes:

• Function name
• Source file and line number
• Time (ms)
• Memory usage (KB)
• Return object size
• Arguments
• Memory growth & potential leaks

Works with Any Project Structure

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

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Setup Instructions

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

2. Optional: Suppress Console Output

By default, profiler prints a table to the console. To disable:

export CONSOLE_DISPLAY=False

Environment Setup

This will install the requirements in your env

If you don't have a virtual environment, just install the dependencies:

profile_code

This will create an env and install requirements

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

This will:

• Read the env vars
• Launch your script
• Record memory + execution stats
• Save detailed JSON report

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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
• Jupyter Notebook integration

Pull requests are welcome!