Datasets:
Update README: Add OpenAI format documentation
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README.md
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license: mit
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task_categories:
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language:
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tags:
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- n8n
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- workflow
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- automation
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- no-code
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- low-code
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- axolotl
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- fine-tuning
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size_categories:
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---
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# n8n Workflow Templates Dataset
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A curated collection of n8n workflow automation templates formatted for LLM fine-tuning.
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## Dataset Description
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This dataset contains **2,737** n8n workflow templates from the official n8n template library, formatted in
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### System Prompt
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## Dataset Structure
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```json
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{
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"instruction": "You are an expert n8n workflow generation assistant...",
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"input": "Create a workflow that retrieves Google Analytics data...",
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"output": "{
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}
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```
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## Loading the Dataset
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```python
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from datasets import load_dataset
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```
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```yaml
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datasets:
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- path: mbakgun/n8nbuilder-n8n-workflows-dataset
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type: alpaca
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```
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## Use Cases
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## Statistics
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| Metric
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| Total Workflows | 2,737
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## License
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## Acknowledgments
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This dataset is currently maintained by
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### Data Sources & Attribution
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If you are a template creator and have concerns about your template being included, please
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## Citation
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---
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license: mit
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tags:
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- n8n
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- workflow
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- automation
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- no-code
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- low-code
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- axolotl
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- fine-tuning
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- text-generation
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task_categories:
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- text-generation
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language:
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- en
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size_categories:
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- 1K<n<10K
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# n8n Workflow Templates Dataset
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A curated collection of n8n workflow automation templates formatted for LLM fine-tuning in multiple formats.
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## Dataset Description
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This dataset contains **2,737** n8n workflow templates from the official n8n template library, formatted in multiple formats for fine-tuning LLMs to generate n8n workflows.
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### Available Formats
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1. **Alpaca Format** (`train` split)
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- Traditional instruction-input-output format
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- Compatible with Axolotl and other Alpaca-based training frameworks
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2. **OpenAI Messages Format** (`train_openai` split)
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- Chat-based format with role-based messages
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- Compatible with OpenAI fine-tuning API and similar chat-based training
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### Data Fields
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#### Alpaca Format
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| Field | Description |
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| ----------- | ----------------------------------------- |
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| instruction | System prompt for n8n workflow generation |
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| input | User's workflow requirements/description |
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| output | Complete n8n workflow JSON |
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#### OpenAI Messages Format
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| Field | Description |
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| -------- | ----------------------------------------- |
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| messages | Array of message objects with role and content |
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Each message object contains:
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- `role`: "user" or "assistant"
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- `content`: The message content (instruction or workflow JSON)
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### System Prompt
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## Dataset Structure
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### Alpaca Format Example
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```json
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{
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"instruction": "You are an expert n8n workflow generation assistant...",
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"input": "Create a workflow that retrieves Google Analytics data...",
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"output": "{"id": "...", "nodes": [...], "connections": {...}}"
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}
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```
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### OpenAI Messages Format Example
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```json
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{
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"messages": [
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{
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"role": "user",
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"content": "You are an expert n8n workflow generation assistant...\n\nCreate a workflow that retrieves Google Analytics data..."
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},
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{
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"role": "assistant",
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"content": "{"id": "...", "nodes": [...], "connections": {...}}"
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}
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]
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}
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```
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## Loading the Dataset
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### Using Hugging Face Datasets
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```python
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from datasets import load_dataset
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# Load Alpaca format
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alpaca_dataset = load_dataset("mbakgun/n8nbuilder-n8n-workflows-dataset", split="train")
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# Load OpenAI format
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openai_dataset = load_dataset("mbakgun/n8nbuilder-n8n-workflows-dataset", split="train_openai")
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# Load both
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full_dataset = load_dataset("mbakgun/n8nbuilder-n8n-workflows-dataset")
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```
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### Using Hugging Face Hub
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```python
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from huggingface_hub import hf_hub_download
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import json
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# Download Alpaca format
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alpaca_file = hf_hub_download(
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repo_id="mbakgun/n8nbuilder-n8n-workflows-dataset",
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filename="train.jsonl",
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repo_type="dataset"
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)
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# Download OpenAI format
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openai_file = hf_hub_download(
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repo_id="mbakgun/n8nbuilder-n8n-workflows-dataset",
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filename="train_openai.jsonl",
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repo_type="dataset"
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)
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```
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## Fine-tuning
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### With Axolotl (Alpaca Format)
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```yaml
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datasets:
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- path: mbakgun/n8nbuilder-n8n-workflows-dataset
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type: alpaca
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split: train
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```
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### With OpenAI Fine-tuning API (OpenAI Format)
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```python
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from openai import OpenAI
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client = OpenAI()
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# Prepare data from Hugging Face
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dataset = load_dataset("mbakgun/n8nbuilder-n8n-workflows-dataset", split="train_openai")
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# Convert to OpenAI format and upload
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training_file = client.files.create(
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file=open("training_data.jsonl", "rb"),
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purpose="fine-tune"
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)
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```
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### With Other Frameworks
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Both formats can be easily converted to other training formats as needed.
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## Use Cases
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* Fine-tuning LLMs to generate n8n workflows from natural language
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* Training models to understand workflow automation patterns
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* Building AI assistants for no-code/low-code automation
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* Research on code generation and workflow automation
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## Statistics
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| Metric | Value |
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| --------------- | -------------- |
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| Total Workflows | 2,737 |
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| Formats | 2 (Alpaca, OpenAI Messages) |
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| Splits | train (Alpaca), train_openai (OpenAI) |
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## License
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## Acknowledgments
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This dataset is currently maintained by n8nbuilder.dev — an AI-powered n8n workflow generation tool.
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### Data Sources & Attribution
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* **Template Source**: All workflow templates in this dataset are sourced from n8n's public template gallery. Template creators retain all rights to their workflows.
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* **Indexing**: Templates were indexed using n8n-mcp by @czlonkowski.
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* **n8n**: n8n is the workflow automation platform that powers these templates.
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If you are a template creator and have concerns about your template being included, please open an issue.
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## Citation
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