MemEdit-G3tFun 0.1.2

High-performance Windows Memory Scanner/Editor

MemEdit

MemEdit is a high-performance Python library for Windows memory manipulation, built with a C++20 core and pybind11. It is designed for speed, efficiency, and ease of use, making it ideal for memory scanning and editing tasks.

Features

• C++20 Core: Leverages modern C++ features for maximum performance.
• Multi-threaded Scanning: Uses std::async and parallel processing to scan large memory blocks (GBs) in seconds.
• SIMD-Ready: Designed for efficient buffer comparisons.
• AOB Scanning: Supports "Array of Bytes" searching with wildcards (e.g., 48 8B ?? 05 ?? ?? ?? ??).
• Pythonic Interface: Clean and easy-to-use Python bindings.
• GIL Management: Releases the Global Interpreter Lock (GIL) during heavy scanning operations to prevent UI freezing.
• RAII Memory Management: Safely handles process handles using modern C++ practices.

Requirements

• Windows OS
• Python 3.7+
• C++20 compatible compiler (MSVC recommended)
• pybind11

Installation

To compile and install MemEdit, run the following command in the project root:

pip install .

Or for development:

python setup.py build_ext --inplace

Usage

Basic Scanning

import MemEdit

# Attach to a process by PID
pid = 1234
scanner = MemEdit.Scanner(pid)

if scanner.is_valid():
    # Perform a first scan for an int32 value
    results = scanner.first_scan_int32(100)
    print(f"Found {len(results)} addresses.")

    # Filter results with a next scan
    results = scanner.next_scan_int32(200)
    print(f"Filtered to {len(results)} addresses.")

    # Write to memory
    if results:
        scanner.write_int32(results[0], 9999)

AOB Scanning

# Scan for a pattern with wildcards
pattern = "48 8B ?? 05 ?? ?? ?? ??"
results = scanner.aob_scan(pattern)
for addr in results:
    print(f"Pattern found at: {hex(addr)}")

Performance Optimization

MemEdit optimizes performance by:

1. Filtering Regions: Only scans MEM_COMMIT and readable/writable regions (PAGE_READWRITE, etc.) using VirtualQueryEx.
2. Parallel Execution: Distributes scanning tasks across multiple threads.
3. Efficient I/O: Minimizes ReadProcessMemory calls by reading large chunks into buffers.

Error Handling

The library includes robust error handling for common issues:

• Access Denied: Returns an invalid scanner object if the process cannot be opened.
• Read/Write Failures: Throws Python exceptions or returns boolean status for memory operations.

License

MIT License