Zapier

Connect TensorX AI to 6,000+ apps with Zapier, the world's most popular automation platform.

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Overview

Zapier connects apps and automates workflows without code. Using TensorX's OpenAI-compatible API with Zapier's webhooks, you can add AI to any Zap.

What You Can Build

• 📧 Smart Email Responses - Auto-draft replies based on content
• 📋 Form Processing - Analyze and route form submissions
• 🏷️ Content Tagging - Auto-categorize documents and records
• 📊 Report Summaries - Generate insights from data
• 🛒 E-commerce Automation - Product content, review analysis
• 💬 Multi-Channel Bots - Respond across Slack, Discord, email

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Prerequisites

• Zapier account (Free tier works, Webhooks require paid plan)
• TensorX API key from app.tensorx.ai

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Method 1: Webhooks by Zapier (Recommended)

The most flexible way to call TensorX from Zapier.

Step 1: Create a New Zap

1. Click Create Zap
2. Choose your trigger (e.g., New Email in Gmail)
3. Add an action step

Step 2: Add Webhooks Action

1. Search for Webhooks by Zapier
2. Select POST as the action event
3. Configure the webhook:

URL:

https://api.tensorx.ai/v1/chat/completions

Payload Type: json

Data:

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

Headers:

Key	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Step 3: Use Dynamic Content

Replace the user message with data from your trigger:

messages: [{"role": "user", "content": "Summarize this email: {{body}}"}]

Step 4: Extract the Response

Add a Formatter by Zapier action:

• Action: Text → Split Text
• Input: {{webhooks__choices__0__message__content}}
• Separator: (leave empty for full text)

Or use Code by Zapier for more control:

const response = JSON.parse(inputData.webhookResponse);
return { aiMessage: response.choices[0].message.content };

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Method 2: OpenAI App (With Custom Base URL)

If Zapier's OpenAI integration supports custom endpoints:

Setup

1. Add OpenAI as an action
2. In connection settings, look for Advanced or Custom Base URL
3. Enter: https://api.tensorx.ai/v1
4. Use your TensorX API key

Note: This method depends on Zapier's OpenAI integration supporting custom URLs. If not available, use the Webhooks method above.

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Method 3: Code by Zapier

For complex logic, use JavaScript or Python:

JavaScript Example

const response = await fetch('https://api.tensorx.ai/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${inputData.apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'deepseek/deepseek-chat-v3.1',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: inputData.prompt }
    ],
    max_tokens: 1000
  })
});

const data = await response.json();
return { response: data.choices[0].message.content };

Python Example

import requests

response = requests.post(
    'https://api.tensorx.ai/v1/chat/completions',
    headers={
        'Authorization': f'Bearer {input_data["api_key"]}',
        'Content-Type': 'application/json'
    },
    json={
        'model': 'deepseek/deepseek-chat-v3.1',
        'messages': [
            {'role': 'user', 'content': input_data['prompt']}
        ],
        'max_tokens': 1000
    }
)

result = response.json()
return {'response': result['choices'][0]['message']['content']}

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Example Zaps

1. Email Auto-Classifier

Automatically label and route incoming emails:

Gmail (New Email)
    ↓
Webhooks (POST to TensorX)
    ↓
Paths (by AI classification)
    ├── Support → Zendesk
    ├── Sales → Pipedrive
    └── Other → Slack notification

Prompt:

Classify this email into exactly one category: SUPPORT, SALES, BILLING, SPAM, or OTHER.

From: {{from_email}}
Subject: {{subject}}
Body: {{body}}

Respond with only the category name.

2. Form Response Analyzer

Analyze feedback forms and extract insights:

Typeform (New Response)
    ↓
Webhooks (POST to TensorX)
    ↓
Google Sheets (Add Row)
    ↓
Slack (Send if negative sentiment)

Prompt:

Analyze this feedback and provide:
1. Sentiment (Positive/Neutral/Negative)
2. Key topics mentioned (comma-separated)
3. One-line summary

Feedback: {{answers}}

3. Content Generator

Generate social posts from blog articles:

RSS (New Item)
    ↓
Webhooks (POST to TensorX)
    ↓
Buffer (Create Post)

Prompt:

Create a Twitter post promoting this article. Keep it under 280 characters. Include a hook and call-to-action.

Title: {{title}}
Summary: {{description}}

4. Lead Scoring

Score new leads based on their data:

CRM (New Lead)
    ↓
Webhooks (POST to TensorX)
    ↓
Formatter (Extract score)
    ↓
CRM (Update Lead)

Prompt:

Score this lead from 1-100 based on:
- Company size (larger = higher)
- Job title seniority
- Industry fit

Company: {{company}}
Title: {{job_title}}
Industry: {{industry}}
Employees: {{company_size}}

Respond with ONLY the numeric score.

5. Meeting Notes Processor

Transform raw notes into structured summaries:

Calendar (Event End)
    ↓
Webhooks (POST to TensorX)
    ↓
Notion (Create Page)
    ↓
Slack (Share summary)

Prompt:

Convert these meeting notes into a structured summary:

# {{event_title}}
Date: {{event_date}}

## Attendees
[List attendees]

## Key Points
[Bullet points of main discussion items]

## Action Items
[Tasks with owners if mentioned]

## Decisions Made
[Any decisions reached]

Notes: {{notes}}

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Working with Responses

Access Response Fields

After the webhook, use Zapier's built-in parsing:

What You Need	Zapier Field Path
AI Response	choices__0__message__content
Model	model
Total Tokens	usage__total_tokens

Clean Up Output

Use Formatter by Zapier:

1. Trim Whitespace - Remove extra spaces
2. Replace - Clean unwanted characters
3. Split Text - Extract specific parts

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Recommended Models

Task	Model	Why
Classification	deepseek/deepseek-chat-v3.1	Fast, accurate
Summarization	meta-llama/llama-3.3-70b-instruct	Good compression
Analysis	deepseek/deepseek-r1-0528	Deep reasoning
Content Creation	deepseek/deepseek-chat-v3.1	Creative, coherent
Code Tasks	z-ai/glm-5.1	Excellent for coding

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Error Handling

Handle API Errors

Add a Paths step after your webhook:

• Path A: Webhook successful (status = 200)
• Path B: Error occurred → Send alert

Common Issues

Error	Cause	Fix
401	Invalid API key	Check key in webhook headers
400	Malformed JSON	Validate your JSON structure
429	Rate limited	Add delay between steps
Timeout	Long response	Increase webhook timeout

Retry Logic

Use Zapier's built-in retry:

1. Click the webhook step
2. Go to Advanced
3. Enable Retry on failure

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Best Practices

1. Store API Key Securely

• Use Zapier's App Connections when possible
• Never expose keys in Zap names or descriptions

2. Optimize for Speed

• Use concise prompts
• Set appropriate max_tokens
• Choose faster models for simple tasks

3. Handle Long Text

For content exceeding limits:

Formatter (Truncate) → Webhook → Response

4. Test Thoroughly

• Use Zapier's Test feature
• Check edge cases (empty fields, special characters)

5. Monitor Usage

Add a logging step to track:

• Token consumption
• Response times
• Error rates

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Pricing Notes

• Zapier: Webhooks require a paid plan
• TensorX: Pay per token used

Optimize costs by:

• Caching repeated requests
• Using smaller models for simple tasks
• Batching related operations

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See Also

• API Reference - Complete API documentation
• Models - Available models
• Zapier Help - Official Zapier documentation