A CLI tool that uses AI to automatically categorize, rename, and organize files based on their content.
AIFiles is a command-line interface (CLI) tool that uses artificial intelligence to automatically organize and manage files. It analyzes file contents—including documents, images, music, and videos—to extract metadata, generate descriptive titles and categories, and move files into a structured folder system based on user-defined templates. It solves the problem of manual file sorting by applying AI-driven context understanding.
Developers, power users, and anyone with large or disorganized file collections who wants to automate file management using AI, especially those concerned with privacy who prefer to use local LLMs.
Developers choose AIFiles for its deep customization, support for both cloud and local AI models, real-time file watching, and interactive TUI for managing organized files—all through a single, extensible CLI tool.
A CLI that organize and manage your files using AI
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Supports both cloud AI services (OpenAI, Grok, DeepSeek) and local LLMs (Ollama, LM Studio), giving users control over privacy and costs, as highlighted in the LLM_PROVIDER configuration options.
Offers over 100 field placeholders and customizable prompts in config files, allowing precise tuning of AI extraction and folder naming, evidenced by the fields.json and ORGANIZATION_PROMPT_TEMPLATE settings.
Includes a watch daemon that auto-organizes new files in specified directories, reducing manual effort, as shown in the aifiles watch command with auto-organization features.
Features a TUI for browsing, searching, editing metadata, and reverting file versions, providing a rich interface beyond basic CLI, described in the aifiles filemanager section.
Requires installing optional system dependencies (Pandoc, ExifTool, etc.) and manual configuration of multiple files, which can be time-consuming and error-prone, as noted in the System Dependencies and Configuration sections.
Relies on AI models that may incur API costs for cloud providers or demand significant resources for local LLMs, leading to potential latency or expenses, mentioned in the privacy warning and local setup instructions.
Lacks built-in cloud storage sync and has a roadmap for future features, which may not meet immediate needs for integrated workflows, as indicated in the Roadmap section.