Integrations
Connect Potato with AI models, crowdsourcing platforms, and export to your favorite ML frameworks.
AI & LLM Integration
Supercharge annotation with AI assistance
OpenAI
GPT-4, GPT-3.5 for intelligent hints, auto-suggestions, and keyword highlighting.
View documentation →Anthropic Claude
Claude 3 models for nuanced annotation assistance and quality checking.
View documentation →Google Gemini
Gemini Pro for multimodal annotation support across text and images.
View documentation →Local LLMs
Coming SoonOllama and local model support for privacy-sensitive deployments.
View documentation →AI-Powered Features
- Intelligent label suggestions
- Automatic keyword highlighting
- Quality checking assistance
- Pre-annotation for review
- Explanation generation
- Consistency checking
Workforce Options
Use your own team or scale with crowdsourcing
Your Own Team
Recommended for Sensitive DataRun Potato locally or on your own servers with your in-house annotators. Perfect for sensitive data that can't be shared externally, IRB-approved studies, or when you already have a trained annotation team.
Benefits
Or scale with crowdsourcing platforms
Prolific
Academic-friendly crowdsourcing with quality participants. Full integration with completion URLs and participant tracking.
Features
Amazon MTurk
Scale to thousands of annotators with Mechanical Turk integration. Supports qualifications and approval workflows.
Features
Supported Data Formats
Import data in any common format
Text
.txt, .json, .jsonl
Images
.jpg, .png, .gif, .webp
Audio
.mp3, .wav, .ogg, .m4a
Video
.mp4, .webm, .mov
Documents
.pdf, .html
Export Formats
Export annotations to popular ML formats
General
- JSON
Native Potato format with full annotation data
- JSONL
Line-delimited JSON for streaming and large datasets
- CSV
Tabular export for spreadsheet analysis
NLP
- CoNLL
Standard format for NER and sequence labeling
- Hugging Face
Direct export to HF Datasets format
- spaCy
Training data format for spaCy models
Computer Vision
- COCO
MS COCO format for object detection
- YOLO
YOLO format for real-time detection
- Pascal VOC
XML format for image classification
Python API & CLI
Programmatic access for automation
Command Line
# Start annotation server potato start config.yaml # Export annotations potato export --format coco # Validate configuration potato validate config.yaml
Python API
from potato import Potato
# Load project
project = Potato("config.yaml")
# Get annotations
annotations = project.get_annotations()
# Export to DataFrame
df = project.to_dataframe()Ready to Get Started?
Install Potato and start integrating with your favorite tools in minutes.