projectlens 0.1.2

A tool for collecting and analyzing Python project files for LLM optimization

ProjectLens 🔍

A powerful tool for collecting and analyzing coding project files for review and optimization with Large Language Models (LLMs) like ChatGPT, Claude, DeepSeek, and other AI assistants.

Overview

ProjectLens scans your codebase and creates a comprehensive text export that you can feed directly to AI models. It intelligently filters files based on extensions, respects common ignore patterns, and creates a well-formatted output that maximizes the context available to LLMs.

Features

• 🔍 Recursively collect project files with specified extensions and smart filtering
• 🌲 Generate detailed project tree structure
• 📝 Create well-formatted output optimized for LLM analysis
• 🚀 Easy to use as both CLI tool and Python API
• 🛠️ Zero external dependencies

Installation

pip install projectlens

For development installation:

git clone https://github.com/cmcouto-silva/projectlens
cd projectlens
uv sync  # or pip install -e .

Quick Start

CLI Usage

# Basic usage - scan Python, Markdown and TOML files
projectlens /path/to/project -x py md toml

# Include specific files regardless of extension
projectlens /path/to/project -x py -i Dockerfile Makefile

# Exclude specific directories or patterns
projectlens /path/to/project -x py md -e tests *cache*

# Specify output file
projectlens /path/to/project -x py -o project_snapshot.txt

# Get detailed logs
projectlens /path/to/project -x py md -e data tests --verbose

Python API Usage

from projectlens import ProjectLens

# Initialize with file extensions to include
lens = ProjectLens(
    extensions=["py", "md", "toml"],
    include=["Dockerfile"],          # Additional files to include
    exclude=["tests", "*cache*"],    # Patterns to exclude
    max_file_size=500                # Maximum file size in KB
)

# Export the project
lens.export_project(
    folder_path="/path/to/project",
    output="project_snapshot.txt"    # Optional, auto-generated if not specified
)

Documentation

For detailed documentation, visit our documentation site.

Use Cases

• LLM Code Review: Generate a complete snapshot of your project for AI review
• Documentation Generation: Have AI analyze your project structure to suggest documentation
• Knowledge Transfer: Help new team members understand project structure
• Legacy Code Understanding: Analyze unfamiliar codebases quickly

License

This project is licensed under the MIT License - see the LICENSE file for details.

Credits

Created and maintained by Cainã Max Couto da Silva.

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