If other people will use what you build, Dify has the parts that matter — tuned knowledge bases, logs and evaluation, publishing, a shared workspace with roles. None of that helps when the person who needs the result is you, this afternoon, working from files already in a folder on your disk. That is the gap Orkas fills.
Dify is a platform for shipping AI features to other people. Orkas is a tool for getting your own work done.
| Capability | OrkasThis site | DifyLLM app platform |
|---|---|---|
| Tuning how documents get retrieved | Reads the folder you point at, as it is | Managed knowledge bases with chunking and retrieval settings |
| Getting better over the next three months | You adjust the brief and try again | Logs, annotation and evaluation built into the platform |
| Handing an AI feature to end users | It is the app you use yourself | Publish it as a web app or an API |
| A team working in one place with roles | Installed per machine, no shared workspace | A shared workspace with members and permissions |
| Time to the first useful result | Install, open, describe the outcome you want | Stand up the platform, then build the app inside it |
| What the output actually is | A file you can send — deck, video, site, report | Responses from the application you assembled |
| Where your material lives | In your own folders, untouched until asked | Imported into datasets the platform manages |
| What you are responsible for keeping alive | A desktop app you can close | The platform, its storage and its upgrades |
Read this as a question about who the output is for. Everything Dify wins at — tuned retrieval, evaluation loops, publishing, shared workspaces — is what you need when other people will use the thing you built. None of it helps when the person who needs the result is you, this afternoon.
You probably should not pick one — Use each tool for the job it is built for, and pass the resulting files into Orkas when you need a coordinated, reviewable deliverable.
Operating an AI product and doing your own work are separate problems that happen to use the same models.
Keep on the platform anything your users touch. That is what the logs and evaluation are for.
Take the internal work off it — the competitor sweep, the release notes, the deck for Thursday, the monthly report.
Open Orkas on the folder where those files already are. Nothing needs importing first.
Both are open source, so neither is more open than the other. They are different shapes: Dify is a platform you host and ship AI features from, Orkas is a desktop application you install and use. If your AI feature has end users, Orkas is not a replacement.
No. You point it at a folder and it reads what is there. Dify manages datasets you import, which is what makes its retrieval tunable — a real advantage for large archives, and unnecessary overhead for a working folder.
No. Orkas is a desktop application, not a service you host or expose. If the requirement is something end users open or something other software calls, Dify is the correct tool.
Usually Orkas, because internal work rarely needs an audience. A competitor sweep, the release notes, Thursday's deck, the monthly report — these want a file at the end, not an application that answers questions about them.
Free, MIT licensed, and it works on the files already sitting on your machine.
