Choosing an Open-Source Annotation Tool in 2026
How to pick an open-source data annotation tool, the questions that narrow the choice quickest, and where Potato fits among Label Studio, Prodigy, Doccano, brat, and Argilla.
There is no single best annotation tool, and any article that tells you otherwise is selling something. The right choice comes down to what you're annotating, whether you need to evaluate LLMs and agents, your budget, and how much setup you can stomach. Here is how to narrow it down.
The questions that narrow it down
What are you annotating? For text-only NER or classification, simple tools like Doccano or brat do the job with little ceremony. For a mix of text, images, audio, and video, you need something broader, which is where Potato and Label Studio come in.
Do you need to evaluate agents or LLMs? Most tool comparisons skip this one. Evaluating an agent means reading its trace, judging steps and tool calls, and sometimes watching it run live. Most general annotation tools weren't built for that. Potato reads agent traces in many formats and has dedicated tools for trajectory evaluation, process reward labeling, and web and coding agent review.
What's your budget? Potato, Label Studio's core, Doccano, brat, and Argilla are free and open-source. Prodigy and some Label Studio tiers are paid.
How much setup can you tolerate? Potato is configured with a YAML file and needs no code. Prodigy is code-first. The others sit in between.
What ecosystem are you in? Prodigy pairs tightly with spaCy. Argilla lives in the Hugging Face stack. Potato exports to CoNLL, Hugging Face, and COCO/YOLO, so it slots into most pipelines.
Where Potato fits
Potato came out of academic NLP and was built for the whole research workflow: many task types, agreement metrics and quality control in the box, crowdsourcing integrations, and a large set of agent-evaluation tools. If your work spans several modalities or includes evaluating models and agents, it's worth a look.
If you mainly need one text task with a hosted commercial product, or you live entirely inside spaCy or Hugging Face, one of the others may suit you better.
Frequently asked questions
The questions below cover choosing a platform for GenAI evaluation and deciding between a commercial and an open-source tool.
How do I choose a GenAI annotation platform?
Start with whether the work includes evaluating model or agent output, because most general annotation tools were built to label data rather than to judge traces, steps, and tool calls. Then narrow by what you are annotating, your budget, how much setup you can tolerate, and the ecosystem you work in. Potato reads agent traces in many formats, has tools for trajectory evaluation, process reward labeling, and web and coding agent review, and is configured with a YAML file and no code.
Do you need a commercial annotation tool or will an open-source one do?
An open-source tool will do unless you want a hosted commercial product. Potato, Label Studio's core, Doccano, brat, and Argilla are free and open source, and Prodigy and some Label Studio tiers are paid. Potato includes agreement metrics, quality control, and crowdsourcing integrations with no paid tier.
Read more
The longer, side-by-side version is in the Open-Source Annotation Tools Compared guide, and the case for Potato specifically is on Why Potato. For a feature-level comparison from the source, see the comparison documentation.