datasets/training/README.md

N8N Training Dataset Collection

High-quality curated subset of n8n workflow examples optimized for LLM training.

Overview

Total Examples: 28,337
Purpose: Train LLMs for autonomous n8n workflow creation and error troubleshooting

Dataset Files

01_conversational_sft.jsonl

Examples: 9,979
Source: eclaude/n8n-workflows-sft (HuggingFace)
Format: Supervised Fine-Tuning (SFT)

Specialty: Conversational workflow generation

Structure:

{
  "instruction": "Create a workflow that sends Slack notifications when...",
  "response": "{n8n workflow JSON}"
}

Use For:

• Training conversational AI assistants
• Natural language → workflow conversion
• Chat-based workflow generation

---

02_reasoning_with_thinking.jsonl

Examples: 5,361
Source: ruh-ai/n8n-workflow-dataset (HuggingFace)
Format: JSONL with thinking chains

Specialty: Reasoning and debugging (UNIQUE - only dataset with thinking chains!)

Structure:

{
  "prompt": "Build a workflow to process CSV files...",
  "thinking": "Step 1: We need a trigger... Step 2: Parse CSV... Step 3: Loop through rows...",
  "json": "{n8n workflow JSON}"
}

Use For:

• Teaching logical reasoning
• Error troubleshooting
• Workflow debugging
• Explaining design decisions

---

03_latest_features.jsonl

Examples: 2,737
Source: mbakgun/n8nbuilder-n8n-workflows-dataset (HuggingFace)
Updated: December 26, 2024

Specialty: Most recent n8n features and nodes

Use For:

• Current n8n API patterns
• Latest node versions
• Modern integrations
• Avoiding deprecated patterns

---

04_advanced_workflows.json

Examples: 10,260
Source: Original validated collection
Format: JSONL (despite .json extension)

Specialty: Complex multi-node workflows

Use For:

• Advanced integration patterns
• Sophisticated business logic
• Production workflow examples
• Complex data transformations

---

Training Recommendations

Quick Start

import json
from pathlib import Path

def load_training_data():
    """Load all training datasets."""
    training_files = [
        '01_conversational_sft.jsonl',
        '02_reasoning_with_thinking.jsonl',
        '03_latest_features.jsonl',
        '04_advanced_workflows.json',
    ]
    
    examples = []
    for filename in training_files:
        with open(filename, 'r', encoding='utf-8') as f:
            first_char = f.read(1)
            f.seek(0)
            
            if first_char == '[':
                examples.extend(json.load(f))  # JSON array
            else:
                examples.extend(json.loads(line) for line in f if line.strip())
    
    return examples

data = load_training_data()
print(f"Loaded {len(data):,} training examples")

Train/Val/Test Split

from sklearn.model_selection import train_test_split

train, temp = train_test_split(data, test_size=0.2, random_state=42)
val, test = train_test_split(temp, test_size=0.5, random_state=42)

print(f"Train: {len(train):,}")  # 22,670
print(f"Val:   {len(val):,}")    # 2,834
print(f"Test:  {len(test):,}")   # 2,833

Why This Subset?

Quality over Quantity:

• ✅ Curated best examples from each category
• ✅ Complementary strengths (conversation, reasoning, latest, advanced)
• ✅ 3x faster training than full 81K dataset
• ✅ 3x lower compute cost
• ✅ No duplicates

Unique Capabilities:

• Conversational: Natural language understanding (01)
• Reasoning: Step-by-step logic and debugging (02)
• Current: Latest n8n features (03)
• Advanced: Complex patterns (04)

Alternative: Full Dataset

For maximum coverage, see ../n8n_master.jsonl (81,649 unique workflows).

Use the full dataset if:

• Quality subset shows coverage gaps
• Production deployment needs
• Comprehensive service knowledge required

File Formats

All files use JSONL (JSON Lines) format:

• One JSON object per line
• Easy to stream
• Memory efficient
• Industry standard

Note: 04_advanced_workflows.json is JSONL format despite the .json extension.

Dataset Statistics

Dataset	Examples	Size	Specialty
01_conversational_sft	9,979	~125 MB	Conversations
02_reasoning_with_thinking	5,361	~91 MB	Debugging
03_latest_features	2,737	~47 MB	Current
04_advanced_workflows	10,260	~13 MB	Advanced
Total	28,337	~276 MB	Complete

Related Documentation

• Main README - Repository overview
• Datasets README - Full dataset collection info
• Dataset Analysis - Detailed analysis

License

These datasets are aggregated from various sources. Please check individual source licenses:

• eclaude datasets: Check HuggingFace repository
• ruh-ai dataset: Check HuggingFace repository
• mbakgun dataset: Check HuggingFace repository
• Original datasets: Part of n8n-docs repository