n8n

Build powerful AI workflows with TensorX and n8n, the open-source automation platform.

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Overview

n8n is a popular workflow automation tool that supports custom AI integrations. With TensorX's OpenAI-compatible API, you can use any of our models in your n8n workflows.

What You Can Build

• 🤖 AI Agents - Autonomous agents with tool calling
• 📧 Email Assistants - Auto-respond, classify, summarize emails
• 📊 Data Processing - Analyze and transform data with AI
• 💬 Chatbots - Deploy conversational AI on any channel
• 📝 Content Pipelines - Generate, edit, translate content
• 🔍 RAG Systems - Build knowledge-based Q&A workflows

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Prerequisites

• n8n instance (Cloud or self-hosted)
• TensorX API key from app.tensorx.ai

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Method 1: OpenAI Chat Model Node (Recommended)

The easiest way to use TensorX with n8n's AI features like AI Agents and LangChain nodes.

Step 1: Create OpenAI Credentials

1. In n8n, go to Credentials → Add Credential
2. Search for OpenAI API
3. Configure the credential:

Field	Value
API Key	Your TensorX API key
Base URL	https://api.tensorx.ai/v1
Organization ID	Leave empty

1. Click Save

Step 2: Use in AI Agent Node

1. Add an AI Agent node to your workflow
2. Connect an OpenAI Chat Model sub-node
3. Select your TensorX credential
4. Configure the model:

Setting	Recommended Value
Model	deepseek/deepseek-chat-v3.1
Temperature	0.7 (or as needed)
Max Tokens	4096

Step 3: Test Your Setup

Create a simple test workflow:

Manual Trigger → AI Agent → Output

Example prompt: "Explain quantum computing in simple terms"

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Method 2: HTTP Request Node (Advanced)

For full control over API calls or using features not exposed in the OpenAI node.

Chat Completion Request

Add an HTTP Request node with these settings:

Request Configuration:

Field	Value
Method	POST
URL	https://api.tensorx.ai/v1/chat/completions
Authentication	Header Auth

Headers:

Header	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Body (JSON):

{
  "model": "deepseek/deepseek-chat-v3.1",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user", 
      "content": "{{ $json.userMessage }}"
    }
  ],
  "max_tokens": 1000,
  "temperature": 0.7
}

Extract the Response

Add a Code node after the HTTP Request:

const response = $input.first().json;
const aiMessage = response.choices[0].message.content;

return [{
  json: {
    response: aiMessage,
    model: response.model,
    tokens: response.usage
  }
}];

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Method 3: Custom n8n Credential (Pro Tip)

Create a reusable HTTP credential for TensorX:

1. Go to Credentials → Add Credential
2. Select Header Auth
3. Configure:

Field	Value
Name	tensorx-api
Name	Authorization
Value	Bearer YOUR_API_KEY

Now use this credential in any HTTP Request node targeting https://api.tensorx.ai.

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Example Workflows

1. Email Classifier Agent

Automatically classify incoming emails and route them:

Email Trigger (IMAP)
    ↓
AI Agent (TensorX)
    ↓
Switch (by classification)
    ↓
├── Support → Create Ticket
├── Sales → Forward to Sales
└── Spam → Archive

System Prompt:

You are an email classifier. Analyze the email and respond with exactly one of:
- SUPPORT (customer service issues)
- SALES (product inquiries, pricing)
- SPAM (promotional, unwanted)
- OTHER (everything else)

Email content: {{ $json.text }}

2. Content Generator Pipeline

Generate blog posts from topics:

Webhook
    ↓
AI Agent (Generate Outline)
    ↓
Loop Over Items
    ↓
AI Agent (Write Section)
    ↓
Merge
    ↓
Google Docs (Create)

3. RAG-Powered Support Bot

Build a knowledge-based chatbot:

Chat Trigger
    ↓
Pinecone (Search)
    ↓
AI Agent (with context)
    ↓
Respond to Chat

System Prompt:

You are a support assistant. Answer questions using ONLY the provided context.
If the answer isn't in the context, say "I don't have information about that."

Context:
{{ $json.searchResults }}

Question: {{ $json.userQuestion }}

4. Slack AI Assistant

Slack Trigger (message)
    ↓
AI Agent (TensorX)
    ↓
Slack (reply)

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Recommended Models

Use Case	Model	Notes
General AI Agent	deepseek/deepseek-chat-v3.1	Best balance of capability and cost
Complex Reasoning	deepseek/deepseek-r1-0528	For multi-step analysis
Code Generation	z-ai/glm-5.1	Optimized for coding tasks
Long Documents	meta-llama/llama-4-maverick	Long document processing
Tool Calling	deepseek/deepseek-chat-v3.1	Strong function calling support

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Troubleshooting

"Invalid API Key" Error

• Verify your API key is correct
• Ensure you're using the full key (starts with tn_ or similar)
• Check that Base URL is exactly https://api.tensorx.ai/v1

Model Not Found

• Use the full model identifier (e.g., deepseek/deepseek-chat-v3.1)
• Check available models for exact names

Timeout Errors

• Increase timeout in HTTP Request node options
• Consider using streaming for long responses
• For reasoning models, set timeout to 120+ seconds

Rate Limits

If you hit rate limits:

1. Add a Wait node between API calls
2. Use the Split in Batches node for bulk processing
3. Implement retry logic with the Error Trigger

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Best Practices

1. Use System Prompts - Always include clear instructions
2. Set Temperature Appropriately - Lower (0.3) for factual, higher (0.8) for creative
3. Limit Max Tokens - Prevent unexpectedly long responses
4. Cache Responses - Store results to avoid redundant API calls
5. Handle Errors Gracefully - Use Error Trigger for fallbacks
6. Secure Credentials - Never expose API keys in workflow data

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See Also

• API Reference - Complete API documentation
• Models - Available models and capabilities
• n8n AI Documentation - Official n8n AI guides