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Integrations

Connect Potato with AI models, crowdsourcing platforms, and export to your favorite ML frameworks.

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AI & LLM Integration

Supercharge annotation with AI assistance

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OpenAI

GPT-4, GPT-3.5 for intelligent hints, auto-suggestions, and keyword highlighting.

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Anthropic Claude

Claude 3 models for nuanced annotation assistance and quality checking.

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✨

Google Gemini

Gemini Pro for multimodal annotation support across text and images.

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Local LLMs (Ollama)

Run AI-assisted annotation with local LLMs using Ollama. Keep your data completely private.

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HuggingFace

Access open-source models via HuggingFace Inference API for flexible AI assistance.

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OpenRouter

Access multiple AI providers through a single API with OpenRouter integration.

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vLLM

Self-hosted high-performance inference with vLLM for maximum control and speed.

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YOLO

Visual object detection with YOLO for image and video annotation tasks.

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LangChain

Automatic trace ingestion from LangChain agents via callback handler. Capture full agent runs as annotation-ready traces.

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OpenAI Vision

GPT-4o and GPT-4 Vision for multimodal annotation assistance on images and screenshots.

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Anthropic Vision

Claude 3 Vision models for image and screenshot annotation assistance.

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AI-Powered Features

  • Intelligent label suggestions
  • Automatic keyword highlighting
  • Quality checking assistance
  • Pre-annotation for review
  • Explanation generation
  • Consistency checking
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Workforce Options

Use your own team or scale with crowdsourcing

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Your Own Team

Recommended for Sensitive Data

Run 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

Data never leaves your serversNo per-annotator costsFull control over accessWorks offline
View local deployment guide →

Or scale with crowdsourcing platforms

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Prolific

Academic-friendly crowdsourcing with quality participants. Full integration with completion URLs and participant tracking.

Features

Completion URL handlingParticipant ID trackingAttention checksQuality filters
View documentation →
☁️

Amazon MTurk

Scale to thousands of annotators with Mechanical Turk integration. Supports qualifications and approval workflows.

Features

HIT managementQualification testsApproval workflowsBonus payments
View documentation →
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Supported Data Formats

Import data in any common format

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Text

.txt, .json, .jsonl

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Images

.jpg, .png, .gif, .webp

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Audio

.mp3, .wav, .ogg, .m4a

🎬

Video

.mp4, .webm, .mov

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Documents

.pdf, .html

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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

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Agent Trace Formats

Import agent traces from 13 frameworks for annotation. Convert via CLI or ingest in real-time via webhook.

Agent Frameworks

  • LangChain / LangSmith

    Hierarchical runs, tool calls

  • Langfuse

    Observation spans, scores

  • OpenAI

    Function calling, assistants

  • Anthropic Claude

    Tool use, thinking blocks

  • MCP

    Model Context Protocol sessions

  • OpenTelemetry

    Distributed span hierarchy

  • ATIF

    Standard interchange format

Web Agents

  • WebArena

    Screenshots, element targeting

  • Raw Browser

    HAR + screenshots

Coding Agents

  • Claude Code

    Anthropic Messages API with tool_use

  • Aider

    Markdown chat with edit blocks

  • SWE-Agent

    Thought/action/observation trajectories

General

  • ReAct

    Generic thought/action/observation

  • Multi-Agent

    CrewAI, AutoGen, LangGraph

Agent Training Exports

Export agent annotations directly to training pipeline formats

PRMProcess reward model training format
DPO / RLHFPreference pairs for alignment training
SWE-benchCompatible evaluation results
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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()
View Data Formats

Ready to Get Started?

Install Potato and start integrating with your favorite tools in minutes.

Quick Start GuideBrowse Documentation