Truly universal online file converter.

Many online file conversion tools are boring and insecure. They only allow conversion between two formats in the same medium (images to images, videos to videos, etc.), and they require that you upload your files to some server.

This is not just terrible for privacy, it's also incredibly lame. What if you really need to convert an AVI video to a PDF document? Try to find an online tool for that, I dare you.

Convert.to.it aims to be a tool that "just works". You're almost guaranteed to get an output - perhaps not always the one you expected, but it'll try its best to not leave you hanging.

For a semi-technical overview of this tool, check out the video: https://youtu.be/btUbcsTbVA8

1. Go to convert.to.it
2. Click the big blue box to add your file (or just drag it on to the window).
3. An input format should have been automatically selected. If it wasn't, yikes! Try searching for it, or if it's really not there, see the "Issues" section below.
4. Select an output format from the second list. If you're on desktop, that's the one on the right side. If you're on mobile, it'll be somewhere lower down.
5. Click Convert!
6. Hopefully, after a bit (or a lot) of thinking, the program will spit out the file you wanted. If not, see the "Issues" section below.

Ever since the YouTube video released, we've been getting spammed with issues suggesting the addition of all kinds of niche file formats. To keep things organized, I've decided to specify what counts as a valid issue and what doesn't.

There are thousands of file formats out there. It can take hours to add support for just one. The math is simple - we can't possibly support every single file. As such, simply listing your favorite file formats is not helpful. We already know that there are formats we don't support, we don't need tickets to tell us that.

When suggesting a file format, you must at minimum:

• Make sure that there isn't already an issue about the same thing, and that we don't already support the format.
• Explain what you expect the conversion to be like (what medium is it converting to/from). It's important to note here that simply parsing the underlying data is not sufficient. Imagine if we only treated SVG images as raw XML data and didn't support converting them to raster images - that would defeat the point. In other words, try to avoid crude "binary waterfalls".
• Provide links to existing browser-based solutions if possible, or at the very least a reference for implementing the format, and make sure the license is compatible with GPL-2.0.

If this seems like a lot, please remember - a developer will have to do 100x more work to actually implement the format. Doing a bit of research not only saves them precious time, it also weeds out "unserious" proposals that would only bloat our to-do list.

If you're submitting a bug report, you only need to do step 1 - check if the problem isn't already reported by someone else. Bug reports are generally quite important otherwise.

Though please note, "converting X to Y doesn't work" is not a bug report. However, "converting X to Y works but not how I expected" likely is a bug report.

1. Clone this repository WITH SUBMODULES. You can use git clone --recursive https://github.com/p2r3/convert for that. Omitting submodules will leave you missing a few dependencies.
2. Install Bun.
3. Run bun install to install dependencies.
4. Run bunx vite to start the development server.

The following steps are optional, but recommended for performance:

When you first open the page, it'll take a while to generate the list of supported formats for each tool. If you open the console, you'll see it complaining a bunch about missing caches.

After this is done (indicated by a Built initial format list message in the console), use printSupportedFormatCache() to get a JSON string with the cache data. You can then save this string to cache.json to skip that loading screen on startup.

If you run into issues where your changes seem to not be applying, try disabling this cache.

Docker compose files live in the docker/ directory, so run compose with -f from the repository root:

docker compose -f docker/docker-compose.yml up -d

Alternatively download the docker-compose.yml separately and start it by executing docker compose up -d in the same directory.

This runs the container on http://localhost:8080/convert/.

Use the override file to build the image locally:

docker compose -f docker/docker-compose.yml -f docker/docker-compose.override.yml up --build -d

The first Docker build is expected to be slow because Chromium and related system packages are installed in the build stage (needed for puppeteer in buildCache.js). Later builds are usually much faster due to Docker layer caching.

The best way to contribute is by adding support for new file formats (duh). If you don't have a format to add but are eager to help, take a look at our issues. There are plenty of suggestions there.

Here's how adding a format works works:

Each "tool" used for conversion has to be normalized to a standard form - effectively a "wrapper" that abstracts away the internal processes. These wrappers are available in src/handlers.

Below is a super barebones handler that does absolutely nothing. You can use this as a starting point for adding a new format:

// file: dummy.ts

import type { FileData, FileFormat, FormatHandler } from "../FormatHandler.ts";
import CommonFormats, { Category } from "src/CommonFormats.ts";

class dummyHandler implements FormatHandler {

  public name: string = "dummy";
  public supportedFormats: FileFormat[] = [
    // Example PNG format, with both input and output disabled
    CommonFormats.PNG.builder("png")
      .markLossless()
      .allowFrom(false)
      .allowTo(false),

    // Alternatively, if you need a custom format, define it like so:
    {
      name: "CompuServe Graphics Interchange Format (GIF)",
      format: "gif",
      extension: "gif",
      mime: "image/gif",
      from: false,
      to: false,
      internal: "gif",
      category: [Category.IMAGE, CATEGORY.VIDEO], // See src/CommonFormats.ts for valid categories
      lossless: false
    },
  ];
  public ready: boolean = false;

  async init () {
    this.ready = true;
  }

  async doConvert (
    inputFiles: FileData[],
    inputFormat: FileFormat,
    outputFormat: FileFormat
  ): Promise<FileData[]> {
    const outputFiles: FileData[] = [];
    return outputFiles;
  }

}

export default dummyHandler;

For more details on how all of these components work, refer to the doc comments in src/FormatHandler.ts. You can also take a look at existing handlers to get a more practical example.

There are a few additional things that I want to point out in particular:

• Pay attention to the naming system. If your tool is called dummy, then the class should be called dummyHandler, and the file should be called dummy.ts.
• The handler is responsible for setting the output file's name. This is done to allow for flexibility in rare cases where the full file name matters. Of course, in most cases, you'll only have to swap the file extension.
• The handler is also responsible for ensuring that any byte buffers that enter or exit the handler do not get mutated. If necessary, clone the buffer by wrapping it in new Uint8Array().
• When handling MIME types, run them through normalizeMimeType first. One file can have multiple valid MIME types, which isn't great when you're trying to match them algorithmically.
• When implementing/suggesting a new file format, please treat the file as the media that it represents, not the data that it contains. For example, if you were making an SVG handler, you should treat the file as an image, not as XML. In other words, avoid simple "binary waterfalls", as they're not semantically meaningful.

This project currently uses two levels of tests:

• Broad project-level tests live directly in test/ (for example graph traversal and end-to-end conversion smoke tests).
• Optional handler-specific unit tests live in test/handlers/, using the file name pattern <handlerName>.test.ts. These are a good fit for handlers with meaningful parsing, serialization, or file-naming logic that is hard to exercise reliably through traversal alone.

Not every handler needs a dedicated unit test, but handlers with non-trivial custom internal logic may benefit from having one.

If your tool requires an external dependency (which it likely does), there are currently two well-established ways of going about this:

• If it's an npm package, just install it to the project like you normally would.
• If it's a Git repository, add it as a submodule to src/handlers.
• If neither of the above are available, then as a last resort, you may create a folder with the required assets under src/handlers/handlerName.

Please try to avoid CDNs (Content Delivery Networks). They're really cool on paper, but they don't work well with TypeScript, and each one introduces a tiny bit of instability. For a project that leans heavily on external dependencies, those bits of instability can add up fast.

• If you need to load a WebAssembly binary (or similar), add its path to vite.config.js and target it under /convert/wasm/. Do not link to node_modules.

If you intend to use an LLM, agent-enabled IDE, or other AI-driven tool for your contribution, please follow these guidelines:

• Clearly state that you've used an LLM, ideally in your pull request's description. Do not attempt to pass off an AI's work as your own. I'm far more likely to accept a pull request that openly admits to using AI than one that does but pretends it doesn't. Transparency helps the maintainer (me) know what to keep an eye out for (e.g. hallucinations), and helps you keep yourself in check.
• Do not overindulge. If your contribution is trivial or simple enough to be written by hand, please opt to write it by hand. This is especially true if it's your first contribution. You're much more likely to retain knowledge and understanding about architectural details if you've familiarized yourself with the process hands-on first.
• Keep the scope to things you could do by hand. LLMs are tools, and this is a community-driven project. Orchestrating an AI to write logic that you don't fully comprehend is not only reckless for a community project, it's also disrespectful towards human contributors who took the time to research their additions. In other words, there should never be a scenario where you need an LLM.
• Explain what you (and the LLM) are doing, in a way that makes it clear that you understand the changes you're making.

Not adhering to these rules will likely get your pull request closed.

I figure that there are people who'd prefer if I merged zero AI-written code, but I believe that's simply not feasible. Just from a code integrity perspective, it's much safer to be transparent about AI usage and define clear guidelines than to make it a taboo and risk people "sneaking in" unvetted AI code. Making things illegal doesn't stop everyone from doing those things - some will still do them, just in secret and with less oversight.