Annotate anything — text, agents, and beyond
Potato is the first annotation platform purpose-built for evaluating AI agents. Import traces from any framework, visualize agent actions interactively, and evaluate with research-grade annotation schemas — all via YAML configuration.
pip install potato-annotation# Create your config.yamlpotato start config.yaml# Open localhost:8000Everything you need for annotation
Powerful features designed for researchers and teams.
Agent Evaluation
Calibrate LLM judges against human labels, edit agent trajectories into SFT and DPO training data, triage the worst traces first, review coding and web agent traces, and watch live agents in real time — all via YAML.
30+ Annotation Types
Radio, multiselect, likert, slider, text, span, best-worst scaling, pairwise, number, multirate, video, image, audio, bounding box, polygon, event, and taxonomy annotation, plus qualitative coding (QDA Mode) with a living codebook, in-vivo codes, memos, and cases.
AI-Powered
LLM integration with OpenAI, Claude, Gemini for intelligent hints, keyword highlighting, label suggestions, MACE competence estimation, option highlighting, and diversity ordering.
Multimedia Support
Annotate audio with waveforms, images with bounding boxes and polygons, and video with playback controls.
Active Learning
5 query strategies — uncertainty sampling, diversity-based selection, BADGE, BALD, and hybrid ensemble — plus LLM cold-start for intelligent instance selection before any labels exist.
Zero Code Setup
Configure everything in YAML. No programming required to create sophisticated annotation interfaces.
Built for every domain
Text, images, audio, or video — Potato has you covered.
350+
Browse 350+ annotation designs
Ready-to-use configurations from the community.
Ready to start annotating?
Join researchers and teams worldwide using Potato.