How I Built an AI File Renamer with FastAPI + Electron (and Why Not Tauri)

I had a folder with 2,000 files named things like IMG_8842.jpg, document(3).pdf, and Screenshot 2026-08-02 145321.png. I knew exactly what was in each one — the day I saved them. Two weeks later, I had no idea.

Writing regex felt like overkill. Manual renaming was out of the question. So I built RenomeeAI: an AI-powered desktop app that reads what's inside a file — its text, metadata, even EXIF data — and generates a meaningful filename based on what you tell it you need.

Here's what I chose to build it with, and why.

Most file renaming tools work on existing filenames. They let you find-and-replace, add prefixes, or reformat dates — but only from information already in the name.

The real problem is different: the useful information is inside the file.

Traditional batch renaming tools can't help here. You'd need to open each file, read it, extract the relevant info, and rename it manually. That's exactly the slow, repetitive work AI is good at replacing.

My first instinct was to build a web app. It's faster to ship, easier to share, and works on any device.

But three things pushed me toward a desktop app:

Privacy. Users are renaming real files — invoices, legal documents, personal photos. Uploading them to a server, even temporarily, felt like a non-starter. A local app processes everything on-device.

File system access. The browser's File System Access API has real limitations: no recursive folder traversal, no write access outside user-selected directories, no background processing. Electron gives you full fs access with no restrictions.

Native UX. Drag-and-drop from the file explorer, right-click context menus, system tray, file associations — these make a file utility feel like a real tool, not a workaround.

Electron handles the UI, file picker dialogs, and local system integration. It communicates with the backend over a local HTTP connection on a fixed port — simple, debuggable, and decoupled.

FastAPI handles everything AI-related: OCR, EXIF extraction, LLM calls, and the actual file rename operations. Python was the natural choice here — the AI/ML ecosystem lives in Python, and keeping all the smart logic in one place makes it easier to reason about.

The two layers are intentionally independent. You could swap the Electron frontend for a CLI or a web UI without touching the backend.

I considered Flask and a Node.js backend. Here's why I landed on FastAPI:

The async support matters more than it might seem. LLM API calls can take 2–10 seconds. If you're processing a batch of 50 files, you want those calls running concurrently, not queued. FastAPI's async / await model handles this cleanly without extra libraries.

Pydantic models also made request validation effortless — when Electron sends a rename request with file paths and user intent, FastAPI validates and parses it automatically.

This is the question I get most often, so let me be direct.

Tauri is excellent for apps where Rust can do the heavy lifting. For an AI tool with a Python backend, the story gets complicated:

I considered keeping everything in Python with PyQt, but ruled it out quickly:

Node.js doesn't have mature, production-ready libraries for local OCR and LLM integration that match Python's ecosystem. The moment you need pytesseract, pdf2image, or direct Volcengine SDK calls, Python becomes the obvious choice.

This is the part that makes the app actually useful — and it's simpler conceptually than it sounds.

When a user selects files to rename, each file arrives at the AI layer with three types of raw data:

On its own, this data is noisy and unstructured. The user's natural language input is the missing piece. When someone says "rename these invoices by supplier name and date" or "keep only the paper title and year", they're providing the selection criteria that transforms raw data into a specific filename.

The AI's job is to bridge these two sides: take heterogeneous file data on one end, take a plain-language intent on the other, and produce a clean, targeted filename. No regex. No template syntax. Just a description of what you want.

The AI doesn't rename files — it converts messy, heterogeneous data into the filename the user already had in mind but couldn't express as a rule.

This is the fundamental shift from traditional renaming tools. Instead of asking "what rule should I apply?", the user asks "what do I want these files to be called?" — and the answer is just a sentence.

RenomeeAI is available for macOS and Windows. If you're dealing with folders full of screenshots, downloaded PDFs, or unorganized photos, it's worth a look.

👉 renomeeai.com (replace with real URL)

The next major feature on the roadmap is Google Drive integration — renaming files directly in the cloud without downloading them first. After that, deeper EXIF-based workflows for photographers.

If you've built something similar — or have strong opinions about Electron vs. Tauri for Python-heavy apps — I'd love to hear your take in the comments.