Integrate Project into Your App

Overview

Once you’ve built and tested a Project in Triform, you’ll want to integrate it into your application. This tutorial covers API integration, authentication, and best practices. Time required: 20-30 minutes

Prerequisites

A deployed Triform Project
API key for authentication
Basic knowledge of HTTP requests

Step 1: Deploy your Project

Before integrating, deploy your Project:

Open your Project on the Canvas
Click Deploy in the Top Bar
Select the target environment (staging or production)
Configure the deployment:
- Endpoint name: Choose a memorable name (e.g., news-digest)
- HTTP method: POST (most common) or GET
- Authentication: API key required (recommended)
Click Deploy Now

Copy the generated endpoint URL:

https://api.triform.ai/v1/your-org/news-digest

Step 2: Generate an API key

Go to Profile → API Keys
Click Create New Key
Name it: Production Integration
Set permissions: Execute Projects
Copy the key (shown only once!)
Store securely (use environment variables)

Security note: Never commit API keys to version control. Use environment variables or secret managers.

Step 3: Make your first API call

Using cURL

curl -X POST https://api.triform.ai/v1/your-org/news-digest \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "sources": [
      "https://newsapi.org/v2/top-headlines?country=us",
      "https://api.example.com/tech-news"
    ]
  }'

Using Python

import requests
import os

API_KEY = os.environ.get('TRIFORM_API_KEY')
ENDPOINT = 'https://api.triform.ai/v1/your-org/news-digest'

headers = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

payload = {
    'sources': [
        'https://newsapi.org/v2/top-headlines?country=us',
        'https://api.example.com/tech-news'
    ]
}

response = requests.post(ENDPOINT, json=payload, headers=headers)

if response.status_code == 200:
    result = response.json()
    print('Success:', result)
else:
    print('Error:', response.status_code, response.text)

Using JavaScript (Node.js)

const axios = require('axios');

const API_KEY = process.env.TRIFORM_API_KEY;
const ENDPOINT = 'https://api.triform.ai/v1/your-org/news-digest';

const headers = {
  'Authorization': `Bearer ${API_KEY}`,
  'Content-Type': 'application/json'
};

const payload = {
  sources: [
    'https://newsapi.org/v2/top-headlines?country=us',
    'https://api.example.com/tech-news'
  ]
};

axios.post(ENDPOINT, payload, { headers })
  .then(response => {
    console.log('Success:', response.data);
  })
  .catch(error => {
    console.error('Error:', error.response?.status, error.response?.data);
  });

Using JavaScript (Browser/React)

const callTriformAPI = async () => {
  const API_KEY = process.env.REACT_APP_TRIFORM_API_KEY;
  const ENDPOINT = 'https://api.triform.ai/v1/your-org/news-digest';
  
  try {
    const response = await fetch(ENDPOINT, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        sources: [
          'https://newsapi.org/v2/top-headlines?country=us',
          'https://api.example.com/tech-news'
        ]
      })
    });
    
    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }
    
    const data = await response.json();
    console.log('Success:', data);
    return data;
  } catch (error) {
    console.error('Error:', error);
  }
};

Step 4: Handle responses

Triform API responses follow this structure:

Success response (200 OK)

{
  "execution_id": "exec_abc123",
  "status": "completed",
  "output": {
    "digest": "Top stories today include...",
    "article_count": 42,
    "topics": ["technology", "politics", "science"]
  },
  "execution_time_ms": 3420,
  "timestamp": "2025-10-01T10:30:00Z"
}

Error response (4xx/5xx)

{
  "error": {
    "code": "invalid_input",
    "message": "Field 'sources' is required",
    "details": {
      "field": "sources",
      "expected_type": "array"
    }
  },
  "request_id": "req_xyz789"
}

Handling in code

def call_triform_api(payload):
    response = requests.post(ENDPOINT, json=payload, headers=headers)
    
    if response.status_code == 200:
        data = response.json()
        return {
            'success': True,
            'data': data['output'],
            'execution_id': data['execution_id']
        }
    elif response.status_code == 400:
        error = response.json()['error']
        return {
            'success': False,
            'error': error['message'],
            'code': error['code']
        }
    elif response.status_code == 429:
        # Rate limit exceeded
        return {
            'success': False,
            'error': 'Rate limit exceeded. Try again later.'
        }
    elif response.status_code >= 500:
        # Server error
        return {
            'success': False,
            'error': 'Triform service temporarily unavailable'
        }
    else:
        return {
            'success': False,
            'error': f'Unexpected error: {response.status_code}'
        }

Step 5: Implement error handling

Retry logic with exponential backoff

import time
from typing import Optional

def call_with_retry(payload, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.post(
                ENDPOINT,
                json=payload,
                headers=headers,
                timeout=30
            )
            
            if response.status_code == 200:
                return response.json()
            elif response.status_code == 429:
                # Rate limited, wait and retry
                wait_time = 2 ** attempt  # 1s, 2s, 4s
                time.sleep(wait_time)
                continue
            elif response.status_code >= 500:
                # Server error, retry
                wait_time = 2 ** attempt
                time.sleep(wait_time)
                continue
            else:
                # Client error, don't retry
                raise Exception(f"API error: {response.text}")
                
        except requests.exceptions.Timeout:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise
        except requests.exceptions.RequestException as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)
                continue
            raise
    
    raise Exception(f"Failed after {max_retries} attempts")

Timeout handling

Always set timeouts to prevent hanging requests:

# Set reasonable timeout (connect timeout, read timeout)
response = requests.post(
    ENDPOINT,
    json=payload,
    headers=headers,
    timeout=(5, 30)  # 5s to connect, 30s to read
)

Step 6: Integrate into your application

Example: Web dashboard

# Flask app
from flask import Flask, render_template, request, jsonify

app = Flask(__name__)

@app.route('/dashboard')
def dashboard():
    return render_template('dashboard.html')

@app.route('/api/refresh-news', methods=['POST'])
def refresh_news():
    sources = request.json.get('sources', [])
    
    result = call_triform_api({
        'sources': sources
    })
    
    if result['success']:
        return jsonify({
            'digest': result['data']['digest'],
            'article_count': result['data']['article_count']
        })
    else:
        return jsonify({'error': result['error']}), 400

Example: Scheduled job

# Daily cron job
import schedule
import time

def fetch_daily_news():
    print("Fetching daily news digest...")
    
    payload = {
        'sources': [
            'https://newsapi.org/v2/top-headlines?country=us',
            'https://api.example.com/tech-news'
        ]
    }
    
    result = call_with_retry(payload)
    
    # Save to database or send email
    save_to_database(result['output']['digest'])
    send_email_digest(result['output']['digest'])
    
    print("Daily news digest completed")

# Schedule for 6 AM daily
schedule.every().day.at("06:00").do(fetch_daily_news)

while True:
    schedule.run_pending()
    time.sleep(60)

Example: Real-time processing

# Process incoming webhooks
from fastapi import FastAPI, BackgroundTasks

app = FastAPI()

@app.post('/webhook/new-content')
async def handle_new_content(data: dict, background_tasks: BackgroundTasks):
    # Trigger Triform processing in background
    background_tasks.add_task(process_with_triform, data)
    return {'status': 'processing'}

def process_with_triform(data):
    result = call_triform_api({
        'content': data['content'],
        'metadata': data['metadata']
    })
    
    # Store results
    store_results(result)

Step 7: Monitor and optimize

Track API usage

import logging

logger = logging.getLogger(__name__)

def call_triform_api_with_logging(payload):
    start_time = time.time()
    
    try:
        response = requests.post(ENDPOINT, json=payload, headers=headers)
        elapsed = time.time() - start_time
        
        logger.info(f"Triform API call completed in {elapsed:.2f}s, status: {response.status_code}")
        
        return response.json()
    except Exception as e:
        elapsed = time.time() - start_time
        logger.error(f"Triform API call failed after {elapsed:.2f}s: {str(e)}")
        raise

Caching results

from functools import lru_cache
import hashlib
import json

# Simple in-memory cache
cache = {}
CACHE_TTL = 3600  # 1 hour

def get_cache_key(payload):
    return hashlib.md5(json.dumps(payload, sort_keys=True).encode()).hexdigest()

def call_triform_cached(payload):
    cache_key = get_cache_key(payload)
    
    # Check cache
    if cache_key in cache:
        cached_data, timestamp = cache[cache_key]
        if time.time() - timestamp < CACHE_TTL:
            logger.info("Returning cached result")
            return cached_data
    
    # Call API
    result = call_triform_api(payload)
    
    # Store in cache
    cache[cache_key] = (result, time.time())
    
    return result

Best practices

Use environment variables — Never hardcode API keys

Implement retries — Network issues happen; retry with backoff

Set timeouts — Don’t let requests hang indefinitely

Log everything — Track success/failure for debugging

Cache when appropriate — Save API calls for expensive operations

Monitor quota — Track usage against your plan limits

Test error paths — Ensure your app handles API failures gracefully

Version your integration — Track which API version you’re using

Troubleshooting

Problem: 401 Unauthorized
Solution: Check API key is correct and has Execute permissions Problem: 429 Too Many Requests
Solution: Implement rate limiting, add delays between requests Problem: 500 Server Error
Solution: Retry with backoff, check Triform status page Problem: Slow responses
Solution: Optimize your Project, add caching, use async requests Problem: Unexpected output format
Solution: Verify Project schema matches your expectations, check docs

Next steps

Continue exploring the documentation to learn about deployments, API key management, monitoring, quotas, and security best practices.

Getting Started

Tutorials

Triton

Workspace

Core Concepts

Organizations

Quotas

Security

Changelog

Roadmap

Support

Appendix

Integrate Project into Your App

Overview

Prerequisites

Step 1: Deploy your Project

Step 2: Generate an API key

Step 3: Make your first API call

Using cURL

Using Python

Using JavaScript (Node.js)

Using JavaScript (Browser/React)

Step 4: Handle responses

Success response (200 OK)

Error response (4xx/5xx)

Handling in code

Step 5: Implement error handling

Retry logic with exponential backoff

Timeout handling

Step 6: Integrate into your application

Example: Web dashboard

Example: Scheduled job

Example: Real-time processing

Step 7: Monitor and optimize

Track API usage

Caching results

Best practices

Troubleshooting

Next steps

Getting Started

Tutorials

Triton

Workspace

Core Concepts

Organizations

Quotas

Security

Changelog

Roadmap

Support

Appendix

​Overview

​Prerequisites

​Step 1: Deploy your Project

​Step 2: Generate an API key

​Step 3: Make your first API call

​Using cURL

​Using Python

​Using JavaScript (Node.js)

​Using JavaScript (Browser/React)

​Step 4: Handle responses

​Success response (200 OK)

​Error response (4xx/5xx)

​Handling in code

​Step 5: Implement error handling

​Retry logic with exponential backoff

​Timeout handling

​Step 6: Integrate into your application

​Example: Web dashboard

​Example: Scheduled job

​Example: Real-time processing

​Step 7: Monitor and optimize

​Track API usage

​Caching results

​Best practices

​Troubleshooting

​Next steps

Overview

Prerequisites

Step 1: Deploy your Project

Step 2: Generate an API key

Step 3: Make your first API call

Using cURL

Using Python

Using JavaScript (Node.js)

Using JavaScript (Browser/React)

Step 4: Handle responses

Success response (200 OK)

Error response (4xx/5xx)

Handling in code

Step 5: Implement error handling

Retry logic with exponential backoff

Timeout handling

Step 6: Integrate into your application

Example: Web dashboard

Example: Scheduled job

Example: Real-time processing

Step 7: Monitor and optimize

Track API usage

Caching results

Best practices

Troubleshooting

Next steps