Orobix Active Learning vs Potato

Orobix Active Learning has no write-up on Specifiction yet; its figures are above.

Potato is an open-source platform for annotating text, audio, images, video, and qualitative data. Tasks are configured in YAML, and its maker says an annotation interface can be created without programming. The homepage lists 61 annotation types, including classification, spans, bounding boxes, polygons, and qualitative coding. Potato also supports annotating multi-agent teams on an interaction graph and evaluating computer-use, voice, and video agents. AI assistance can suggest labels, highlight keywords, check quality and consistency, pre-annotate material, and generate explanations. Listed integrations include OpenAI, Anthropic Claude, Google Gemini, Ollama, HuggingFace, OpenRouter, vLLM, YOLO, and LangChain. Imports cover text, image, audio, video, and PDF or HTML documents; exports include JSON, JSONL, CSV, CoNLL, Hugging Face, COCO, and YOLO. Potato runs locally or on users’ own servers and supports offline use. The free self-hosted plan includes all features and has no usage limits. It is licensed under GNU GPL version 3 or later; installation with pip requires Python 3.7 or later.