N8N Integration¶

Connect N8N automation workflows to Amazon Bedrock models through stdapi.ai's OpenAI-compatible interface. Use any existing OpenAI template from the N8N marketplace without modification—simply point it to your stdapi.ai instance.

About N8N¶

🔗 Links: Website | GitHub | Documentation | Community

N8N is a workflow automation platform:

• 45,000+ GitHub stars - Open-source workflow automation tool
• 400+ integrations - Connect with services and APIs
• Visual workflow builder - No-code interface with customization
• Self-hosted or cloud - Deploy anywhere
• AI-native - Built-in nodes for OpenAI and other AI providers

Why N8N + stdapi.ai?¶

Key Benefits:

• Use familiar OpenAI nodes and templates
• Access Amazon Bedrock AI models
• AWS pricing and infrastructure
• Keep data in your AWS environment
• Mix different models in the same workflow

Prerequisites¶

Quick Start Guide¶

Step 1: Set Up Your Credentials¶

The foundation of any N8N integration is configuring your API credentials. This one-time setup unlocks all AI capabilities.

Credential Name: stdapi.ai (or any name you prefer)
API Key:         YOUR_STDAPI_KEY
Base URL:        https://YOUR_SERVER_URL/v1

AI Capabilities: Step-by-Step¶

Now that your credentials are configured, let's explore each AI capability you can use in your N8N workflows.

💬 Chat Completions — Conversational AI¶

Build intelligent conversational workflows with state-of-the-art language models.

Available Models:

All Amazon Bedrock chat models are accessible through N8N once you configure the stdapi.ai credential. This includes:

• Anthropic Claude — All Claude model variants (Sonnet, Haiku, Opus)
• Amazon Nova — All Nova family models (Pro, Lite, Micro)
• Meta Llama — Llama 3 models (if enabled in your AWS region)
• Mistral AI — Mistral and Mixtral models (if enabled in your AWS region)
• And more — Any other Bedrock models available in your configured AWS region

Example Model IDs:

• anthropic.claude-sonnet-4-5-20250929-v1:0 — Superior reasoning and code
• amazon.nova-pro-v1:0 — Advanced reasoning
• amazon.nova-lite-v1:0 — Balanced performance
• amazon.nova-micro-v1:0 — Fast, cost-effective responses

💡 Real-World Examples:

• Customer Support: Automate responses to common customer inquiries with context-aware AI that understands your knowledge base
• Content Generation: Generate blog posts, product descriptions, email campaigns, and social media content at scale
• Data Analysis: Extract insights from text data, classify documents, perform sentiment analysis on feedback
• Code Assistant: Generate, review, and debug code as part of your development workflows

🔊 Text to Speech — Give Your Workflows a Voice¶

Convert text into natural-sounding audio with Amazon Polly's lifelike voices.

💡 Real-World Examples:

• Notifications: Send audio alerts and notifications via phone, smart speakers, or mobile apps
• Content Creation: Convert blog posts to podcasts, generate audio versions of articles automatically
• Accessibility: Make your content accessible by providing audio alternatives to text
• IVR Systems: Generate dynamic voice responses for interactive voice response systems

🎤 Speech to Text — Transcribe Audio Effortlessly¶

Transform audio recordings into accurate text transcriptions powered by Amazon Transcribe.

💡 Real-World Examples:

• Meeting Notes: Automatically transcribe meetings, calls, and interviews for easy review and sharing
• Customer Service: Convert voicemails and support calls to text for analysis and ticketing systems
• Media Production: Generate subtitles and captions for videos, podcasts, and webinars
• Voice Commands: Build voice-controlled automation workflows and smart assistants

🔍 Embeddings — Semantic Search & Intelligence¶

Generate vector embeddings to power semantic search, recommendations, and RAG (Retrieval-Augmented Generation) systems.

Available Embedding Models:

The example uses amazon.titan-embed-text-v2:0, but you can choose from multiple embedding models available in Amazon Bedrock:

• Amazon Titan Embed Text v2 — amazon.titan-embed-text-v2:0 (recommended, optimized for RAG, 8192 dimensions)
• Amazon Titan Embed Text v1 — amazon.titan-embed-text-v1 (legacy, 1536 dimensions)
• Cohere Embed — Cohere embedding models (if enabled in your AWS region)
• Other providers — Any Bedrock embedding model

Choose your embedding model based on:

• Dimension size — Higher dimensions (like Titan v2's 8192) capture more nuance but use more storage
• Performance — Some models are faster than others
• Language support — Ensure your chosen model supports your documents' languages

💡 Real-World Examples:

• Semantic Search: Find documents based on meaning rather than exact keyword matches—understand intent, not just words
• Document Similarity: Compare and cluster documents by semantic similarity for content organization
• Knowledge Bases: Build RAG systems that retrieve relevant context for AI-powered Q&A
• Recommendations: Create recommendation engines based on content similarity and user preferences

🎨 Image Generation — Visual Content Creation¶

Create stunning images from text descriptions using Amazon Bedrock's image generation models.

Available Image Models:

While the example below uses amazon.nova-canvas-v1:0, you can use any image generation model available in Amazon Bedrock:

• Amazon Nova Canvas — amazon.nova-canvas-v1:0 (recommended, fast and high-quality)
• Stability AI — Stable Diffusion models (if enabled in your AWS region)
• Other providers — Any Bedrock image generation model

Simply specify your preferred model ID in the Model parameter.

💡 Real-World Examples:

• Marketing Assets: Generate hero images, banners, and visual content for campaigns automatically
• Product Mockups: Create product visualizations and concept art for rapid prototyping
• Social Media: Produce eye-catching graphics for posts, stories, and advertisements at scale
• Blog Illustrations: Generate custom illustrations that match your article content perfectly

🔄 Using Existing OpenAI Templates¶

The best part? Any N8N workflow or template from the marketplace designed for OpenAI works with stdapi.ai—often with zero modifications needed.

Migration in 4 Simple Steps¶

📋 Model Conversion Reference¶

Use this quick reference to map OpenAI models to Amazon Bedrock equivalents. These are example suggestions—many more models are available depending on your AWS region.

🎯 Popular Template Categories¶

stdapi.ai is compatible with N8N templates across these categories:

🚀 Advanced Workflows¶

Multi-Model Orchestration¶

One of the most powerful features is combining multiple models in a single workflow. Each node can use a different model optimized for its specific task.

1. Webhook Trigger → Receives article topic
2. Chat Node (amazon.nova-micro-v1:0) → Generate outline quickly
3. Chat Node (anthropic.claude-sonnet-4-5-20250929-v1:0) → Write detailed article
4. Embeddings Node (amazon.titan-embed-text-v2:0) → Create searchable vectors
5. Image Node (amazon.nova-canvas-v1:0) → Generate hero image
6. TTS Node (amazon.polly-neural) → Create audio version

Self-Hosted N8N Configuration¶

For self-hosted installations, streamline your setup with environment variables:

version: '3.8'
services:
  n8n:
    image: n8nio/n8n
    environment:
      # stdapi.ai defaults
      - N8N_OPENAI_BASE_URL=https://YOUR_SERVER_URL/v1
      - N8N_OPENAI_API_KEY=${STDAPI_KEY}
      # Other N8N settings
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=${ADMIN_PASSWORD}
    ports:
      - "5678:5678"
    volumes:
      - n8n_data:/home/node/.n8n

Robust Error Handling¶

Build production-ready workflows with proper error handling and resilience.

Primary: anthropic.claude-sonnet-4-5-20250929-v1:0
Fallback: amazon.nova-pro-v1:0
Final Fallback: amazon.nova-lite-v1:0

📊 Monitoring & Optimization¶

Performance Best Practices¶

Cost Management¶

🎓 Next Steps & Resources¶

What You Can Build Now¶

With your stdapi.ai + N8N integration complete, you're ready to:

• ✨ Import Templates: Browse the N8N marketplace for OpenAI templates and adapt them instantly
• 🔨 Build Custom Workflows: Combine multiple AI capabilities for unique automation solutions
• 🔗 Chain Complex Logic: Create sophisticated multi-step AI pipelines with conditional branches
• 📈 Scale Production: Deploy high-volume workflows with confidence using enterprise-grade infrastructure
• 🌍 Go Global: Leverage multiple AWS regions for optimal performance worldwide

Helpful Resources¶

Get Support¶

⚠️ Important Considerations¶