Nishan30/n8n-workflow-generator

const workflow = new Workflow('Webhook to Slack');
const webhook = workflow.add('n8n-nodes-base.webhook', {{
  path: '/data',
  method: 'POST'
}});
const slack = workflow.add('n8n-nodes-base.slack', {{
  channel: '#general',
  text: '={{{{ $json.message }}}}'
}});
webhook.to(slack);

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load fine-tuned adapter
model = PeftModel.from_pretrained(base_model, "Nishan30/n8n-workflow-generator")
tokenizer = AutoTokenizer.from_pretrained("Nishan30/n8n-workflow-generator")

# System prompt
system_prompt = """You are an expert n8n workflow generator. Given a user's request, you generate clean, functional TypeScript code using the @n8n-generator/core DSL.

Your output should:
- Only contain the code, no explanations
- Use the Workflow class from @n8n-generator/core
- Use workflow.add() to create nodes
- Use .to() or workflow.connect() for connections
- Be ready to compile directly to n8n JSON"""

# Generate
user_request = "Create a webhook that sends data to Slack"
messages = [
    {{"role": "system", "content": system_prompt}},
    {{"role": "user", "content": user_request}}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.3,
    do_sample=True,
    top_p=0.9,
    repetition_penalty=1.1
)

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="Nishan30/n8n-workflow-generator",
    device_map="auto"
)

prompt = "Create a scheduled workflow that fetches data daily and sends to Slack"
result = generator(prompt, max_new_tokens=512, temperature=0.3)
print(result[0]['generated_text'])

User: "Create a webhook that posts to Slack"
Model: [Generates complete TypeScript DSL code]

User: "Daily workflow that fetches API data and stores in database"
Model: [Generates schedule trigger + HTTP request + database storage]

User: "Contact form that validates and sends email"
Model: [Generates form trigger + validation + email sending]

User: "Route high-priority items to #urgent, others to #general"
Model: [Generates webhook + if condition + dual Slack outputs]

custom_prompt = """You are a workflow expert. Generate n8n workflows with:
- Error handling for all HTTP requests
- Descriptive node names
- Production-ready configurations
"""

requests = [
    "webhook to slack",
    "daily email report",
    "form to database"
]

for req in requests:
    workflow = generate_workflow(req)
    print(workflow)

import json
from n8n_generator import compile_to_json

# Generate DSL
dsl_code = model.generate(prompt)

# Compile to n8n JSON
workflow_json = compile_to_json(dsl_code)

# Import to n8n
# POST to http://your-n8n-instance/api/v1/workflows