AI Curator

The complete dataset preparation workflow for LLM fine-tuning from raw text to fine-tuned model.
Collect. Curate. Export. Train.

Designed exclusively for text-based LLMs — not for vision or audio models.

Whether you're a professional building production-grade training datasets or a beginner taking your first steps into LLM fine-tuning, it adapts to your workflow.

Main Features:

• Privacy First — Local SQLite database. No cloud, no tracking, no telemetry.
• No Code — Point, click, curate. No programming required.
• Zero Configuration — Install and run. Start collecting in minutes.
• EdukaAI Starter Pack included - Start training in 5 minutes with 75 premium samples

Three ways to work:

• Intuitive Web UI — Point, click, curate. Perfect for beginners.
• Powerful CLI — Automate, script, and integrate. Built for professionals.
• Live Capture API — Real-time data streaming from any tool, IDE, or script. Our most powerful feature.

Why AI Curator?

For Professionals: Unlimited Versatility

Capture, curate, and export data from virtually any source:

• Live streaming — Real-time capture from IDEs, chat interfaces, customer support systems, and internal tools
• Log processors — Transform application logs, error reports, and user interactions into training pairs
• Batch imports — Ingest massive datasets from Hugging Face, Kaggle, JSON files, CSV exports, or custom pipelines
• API integration — HTTP endpoints for any script, tool, or automated workflow to push data
• Manual refinement — Human-in-the-loop quality control for mission-critical datasets

For Beginners: Zero-Code Learning

Start fine-tuning in 5 minutes with no configuration:

• Pre-loaded starter pack — 75 premium samples ready to train immediately (included!)
• Point-and-click curation — Web UI requires zero technical knowledge
• One-click export — Export to training formats without writing any code
• Visual feedback — See your dataset quality, progress, and readiness at a glance

Real-world use cases:

• Developers — Turn IDE interactions and code reviews into coding assistants
• Support Teams — Build customer service bots from real ticket resolutions
• Researchers — Clean and prepare domain-specific datasets for publication
• Product Teams — Transform user feedback and documentation into helpful AI companions
• Absolute Beginners — Fine-tune your first LLM in 5 minutes with the pre-loaded starter pack, then use it as a hands-on learning experience to understand how AI training works from zero knowledge

Export ready for: Apple MLX or any Alpaca-compatible trainer.

Quick Start

Homebrew (macOS & Linux) ✅ Recommended

The fastest way to install AI Curator with automatic updates:

brew tap elgap/tap
brew install ai-curator

The curator command will be available globally. Simply run:

curator

Install Script (macOS & Linux)

Universal installer that works on all platforms and architectures:

curl -fsSL https://raw.githubusercontent.com/elgap/ai-curator/main/install.sh | bash

This will:

1. Check and install Node.js 20+ if needed
2. Download AI Curator to your current directory
3. Install all dependencies (with correct native binaries for your platform)
4. Build the application
5. Add curator command to your PATH
6. Import the EdukaAI Starter Pack (75 samples ready to use)

After installation, simply run:

curator

Or if you prefer:

./curator

🎁 Built-In Datasets

AI Curator creates two purpose-built datasets on first run — your Live Capture Inbox and EdukaAI Starter Pack container. The starter pack with 75 premium samples is imported automatically during installation.

📡 Live Capture Inbox (ID: 1)

Your personal data collection inbox

• Captures conversations, code snippets, and any text you want to train on
• Perfect for building custom datasets from your daily workflow
• Disabled by default — enable in Settings → Live Capture when ready
• Goal: 500 samples for your Personal AI Assistant
• Category: captured

🎓 EdukaAI Starter Pack (ID: 2) [ACTIVE]

Start training in 5 minutes with 75 premium samples

The starter pack is automatically imported during installation. If you need to import it manually:

curator import EdukaAISP/default-dataset.json --dataset 2 --status approved

A curated collection of immersive football training data from the Kingston United vs. Newport County thriller:

• Player roleplays — Be Chen Wei, Diego Rodriguez, or Marco Esposito
• Tactical analysis — Deep match breakdowns and formation analysis
• Fan perspectives — Emotional reactions from both home and away supporters
• Commentary transcripts — Professional match narration
• Alternate history — "What if" scenarios exploring different outcomes

Export for training:

# Export for Apple Silicon training (MLX format)
curator export --dataset 2 --format mlx --output train.jsonl

# Or export for Unsloth (faster, memory-efficient)
curator export --dataset 2 --format unsloth --output train.jsonl

Perfect for: Your first LLM fine-tuning experience — see results in ~5 minutes!

The Three Workflows

1. Web UI — Visual Curation for Everyone

Perfect for: Beginners, visual learners, manual quality control

A clean, intuitive interface that requires zero technical knowledge:

• Import datasets via drag-and-drop (JSON, JSONL, CSV)
• Review samples in a clear card-based layout
• Approve or reject with one click
• Edit and refine instructions and outputs
• Track progress with visual dashboards
• Export with one click to your preferred format

No code. No configuration. Just point, click, and curate.

2. CLI — Power Tools for Professionals

Perfect for: Automation, scripting, bulk operations, CI/CD integration

When you need to move fast and work at scale:

# Import 10,000 samples in one command
curator import massive-dataset.jsonl --dataset 1 --workers 8

# Export EdukaAI Starter Pack for immediate training
curator export --dataset 2 --format mlx --output train.jsonl

# Export only approved high-quality samples from any dataset
curator export --dataset 3 --filter "status=approved AND quality>=4" --format mlx

# Search and download from Hugging Face
curator search "python programming"
curator download hf:openai/summarize_from_feedback --dataset 3

# Split for training with stratification
curator export --split "0.8,0.1,0.1" --seed 42 --format jsonl

7 export formats: Alpaca, ShareGPT, JSONL, CSV, MLX (Apple Silicon), Unsloth, TRL
Query language: Filter by status, quality, category, or full-text search
Smart splitting: Train/test/val with stratification to maintain category balance

3. Live Capture API — Real-Time Data Streaming

Perfect for: Integration, continuous collection, capturing from live tools

This is AI Curator's most powerful feature. Stream training data in real-time from any source via simple HTTP POST:

# From your IDE, script, or any tool:
curl -X POST http://localhost:3333/api/capture \
  -H "Content-Type: application/json" \
  -d '{
    "source": "my-ide",
    "records": [{
      "instruction": "Explain this error",
      "output": "The error occurs because...",
      "systemPrompt": "You are a coding assistant",
      "category": "coding",
      "qualityRating": 5
    }]
  }'

Why this matters:

• Capture conversations as they happen — no manual copy-pasting
• Integrate with any tool that can make HTTP requests
• Your product's actual interactions become training data
• Real-time or batched — your choice

Common integrations:

• IDE extensions (capture code explanations)
• Chat interfaces (save valuable AI conversations)
• Support systems (turn tickets into Q&A samples)
• Log processors (extract error-resolution pairs)
• Custom scripts (automated data generation)

Complete Data Workflow

Step 1: Collect (Multiple Ways)

Live Capture — Real-time streaming from any tool via API
File Import — Upload existing datasets (JSON, JSONL, CSV)
Manual Entry — Create human-crafted examples in the UI
External Sources — Import from Hugging Face & Kaggle via CLI

Step 2: Curate (Under Your Supervision)

Review Workflow:

• Draft — Captured or imported samples waiting for review
• In Review — Being evaluated for quality
• Approved — High-quality samples ready for training
• Rejected — Low-quality or irrelevant samples excluded

Quality Controls:

• Rate samples 1-5 stars for quality
• Categorize by domain (coding, writing, Q&A, etc.)
• Tag for organization
• Detect and flag duplicates
• Edit and refine content

Step 3: Export (Ready for Training)

Export to 7 formats optimized for different training pipelines:

| Format | Best For | Notes | | ---------- | ------------------- | -------------------------------- | | Alpaca | General fine-tuning | Standard instruction format | | MLX | Apple Silicon | Optimized for MLX-LM on M1/M2/M3 | | JSONL | Pipelines | Line-delimited for streaming |

Export Options:

• Filter by status (approved only, or include drafts)
• Filter by quality (minimum star rating)
• Filter by category or tags
• Train/test/validation splits
• Stratified splitting (maintains category proportions)
• With or without metadata

Live Capture API in Detail

The Universal Capture API turns any tool into a training data source. Send data from:

Development Tools:

// VS Code extension or IDE plugin
fetch("http://localhost:3333/api/capture", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    source: "vscode-extension",
    records: [
      {
        instruction: "Explain this function",
        output: explanation,
        context: { code, language },
        category: "coding",
      },
    ],
  }),
});

Log Processing:

# Extract error-resolutions from logs
tail -f /var/log/app.log | grep "ERROR" | while read line; do
  curl -X POST localhost:3333/api/capture \
    -d "{\"source\":\"logs\",\"records\":[...]}"
done

Chat Interfaces:

# Python script capturing from any chat
import requests

requests.post('http://localhost:3333/api/capture', json={
    'source': 'chat-interface',
    'records': [{
        'instruction': user_message,
        'output': assistant_response,
        'qualityRating': 4 if user_thumbs_up else 3
    }]
})

Endpoint: POST /api/capture

CLI Reference

For power users who prefer the command line:

| Command | Description | | -------------------------- | ------------------------------------------------ | | curator | Start server with web UI (http://localhost:3333) | | curator import <file> | Import local file (JSON, JSONL, CSV) | | curator export [options] | Export dataset to training format | | curator search <query> | Search Hugging Face and Kaggle | | curator download <id> | Download and auto-import from external source | | curator clear | Clear dataset (with confirmation) | | curator reset | Reset database to fresh state |

Example Workflows:

# Full pipeline: Search → Download → Curate → Export
curator search "medical qa" --limit 5
curator download hf:medalpaca/medical_meadow_small --dataset 1
curator export --dataset 1 --format mlx --output medical-training.jsonl --filter "quality>=4"

# Incremental collection with live capture + periodic export
# (in one terminal) curator  # Start server
# (in another) ./my-capture-script.sh  # Stream data
# (later) curator export --dataset 1 --split "0.8,0.1,0.1" --format jsonl

Environment Variables:

| Variable | Default | Description | | --------------------- | ---------- | ----------------------- | | AI_CURATOR_PORT | 3333 | Server port | | AI_CURATOR_DATA_DIR | ~/.curator | Where data is stored | | HUGGINGFACE_TOKEN | - | For private HF datasets |

Core Features

Dataset Management

• Create multiple datasets for different projects
• Set goals and track progress visually
• Organize by purpose: coding, writing, Q&A, roleplay, custom

Training Sample Management

• Core fields: Instruction, Input, Output, System Prompt
• Metadata: Category, Difficulty, Quality (1-5), Tags
• Workflow: Draft → In Review → Approved/Rejected
• Bulk operations: Multi-select approve, categorize, delete

Quality Control

• Draft-first capture — Review before including in training
• Quality ratings — Star system for sample ranking
• Human-in-the-loop — You approve every sample

Import Formats

AI Curator accepts standard dataset formats:

• JSON — Array of objects or JSON Lines
• JSONL — Line-delimited JSON (streaming-friendly)
• CSV — Comma-separated values with headers

Smart Auto-Detection: Format is automatically detected from file extension and content structure. Supports both standard Alpaca format and custom field mappings.

Privacy & Security

100% Local-First:

• SQLite database stored on your machine (~/.curator/)
• No cloud services or external APIs (except optional HF/Kaggle downloads)
• No tracking, analytics, or telemetry
• No account required, no data leaves your machine

You Own Everything:

• Your data is your data
• Export anytime to any format
• MIT licensed — full transparency

Development

Tech Stack

• Frontend: Vue 3 + Nuxt 4 + Tailwind CSS + Nuxt UI
• Backend: Nuxt 4 API routes + Drizzle ORM
• Database: SQLite (better-sqlite3)
• CLI: Node.js with esbuild bundling

Build from Source

git clone https://github.com/elgap/ai-curator.git
cd ai-curator
npm install
npm run dev

Commands

npm run db:reset      # Reset database
npm run test          # Run tests
npm run build         # Production build
npm run bundle:cli    # Bundle CLI for distribution
npm run lint          # Run ESLint

Project Structure

ai-curator/
├── app/              # Nuxt frontend (Vue components, pages)
├── server/           # Backend API routes, CLI commands
├── bin/              # CLI entry point (development)
├── dist/             # Bundled CLI (production)
├── scripts/          # Build and utility scripts
└── docs/             # Documentation

Contributing

We welcome contributions. Contributing guide will be published soon.

License

MIT License — see LICENSE

AI Curator and EdukaAI Studio are part of the EdukaAI project by ElGap — making AI & fine-tuning accessible through open-source, privacy-first, no-code tools.

We follow the #rapidMvpMindset, shipping fast and iterating regularly. Expect frequent updates.