Fetch scriptures dynamically from get Bible API
Overview
The Get Bible Query Workflow is a modular and self-standing workflow designed to retrieve scriptures dynamically based on structured input. It serves as an intermediary layer that extracts references, queries the GetBible API, and returns scriptures in a standardized JSON format.
This workflow is fully prepared for integration—simply call it from another workflow with the required JSON input, and it will return the requested scripture data.
Who Is This For?
This workflow is ideal for developers, Bible study apps, research tools, and dynamic scripture-based projects that need seamless access to scriptural content without direct API interaction.
✅ Use Cases:
- Bible Study Apps → Embed scripture retrieval functionality.
- Research & Theology Tools → Fetch structured verse data.
- Dynamic Content Generation → Integrate real-time scripture references.
- Sermon Preparation → Automate scripture lookups.
How It Works
- Trigger Workflow → This workflow is designed to be called from another workflow with a structured JSON input.
- Receive Input → Accepts a JSON object containing references, translation, and API version.
- Extract References → Parses single verses, comma-separated lists, and ranged passages.
- Query API → Sends structured requests to the GetBible API.
- Format Response → Returns structured JSON output, maintaining API response consistency.
JSON Input Structure
- References → Should include the book name, chapter, and verse(s).
- Multiple Verses → Separated by commas (e.g.,
John 3:16,18). - Verse Ranges → Defined with a dash (e.g.,
John 3:16-18). - Translation → Choose from the supported translations.
- API Version → Currently supports
v2.
Example JSON Input
{
"references": [
"1 John 3:16",
"Jn 3:16",
"James 3:16",
"Rom 3:16"
],
"translation": "kjv",
"version": "v2"
}
Example API Response
{
"result": {
"kjv_62_3": {
"translation": "King James Version",
"abbreviation": "kjv",
"book_name": "1 John",
"chapter": 3,
"ref": ["1 John 3:16"],
"verses": [
{
"chapter": 3,
"verse": 16,
"name": "1 John 3:16",
"text": "Hereby perceive we the love of God, because he laid down his life for us: and we ought to lay down our lives for the brethren."
}
]
}
}
}
💡 Fully structured and formatted response – ready for seamless integration.
Integration and Usage
The GetBible Query Workflow is designed for immediate use. Simply call it from another workflow and pass the appropriate JSON object as input, and it will return the requested scripture passages.
✔️ No additional configuration is required. ✔️ Designed for fast, reliable, and structured scripture retrieval. ✔️ Fully compatible with GetBible API responses.
Why Use This Workflow?
✔️ Fast & Reliable → Direct API integration for efficient queries. ✔️ Flexible Queries → Supports single, multi-verse, and ranged requests. ✔️ Agent-Compatible → Easily integrates into automated workflows. ✔️ No Code Needed → Just configure the JSON input and run the workflow.
Next Steps
🔗 API Support 📖 API Documentation 💬 Need help? Join the community for support! 🚀
Fetch Scriptures Dynamically from Get Bible API
This n8n workflow demonstrates how to dynamically fetch scripture passages from the Get Bible API. It's designed to be triggered by another workflow, allowing for flexible integration into various automation scenarios where specific Bible verses are needed.
What it does
This workflow simplifies the process of retrieving scripture data. Here's a step-by-step breakdown:
- Listens for an external trigger: The workflow starts when it's explicitly called by another n8n workflow. It expects to receive input data that specifies the desired scripture reference.
- Prepares the API request: It takes the input scripture reference and dynamically constructs the URL for the Get Bible API.
- Makes an HTTP Request: It sends a GET request to the Get Bible API with the constructed URL to fetch the scripture data.
- Processes the API response: It receives the JSON response from the Get Bible API and extracts the relevant scripture text.
- Formats the output: It formats the extracted scripture text into a clean, usable output for subsequent nodes in a calling workflow.
Prerequisites/Requirements
- n8n Instance: You need a running n8n instance to import and execute this workflow.
- Get Bible API: This workflow relies on the Get Bible API. While no explicit API key is configured in the provided JSON, ensure you are aware of any rate limits or authentication requirements if using it in a production environment.
Setup/Usage
-
Import the workflow:
- Copy the provided JSON code.
- In your n8n instance, click "New" in the workflows section.
- Click the "Import from JSON" button and paste the copied JSON.
- Click "Import".
-
Activate the workflow: After importing, ensure the workflow is activated by toggling the "Active" switch in the top right corner.
-
Trigger from another workflow: This workflow is designed to be called by an "Execute Workflow" node in another n8n workflow. When calling it, pass the scripture reference (e.g., "John 3:16") as input data to the "When Executed by Another Workflow" trigger node.
Example of input data for the "Execute Workflow" node:
{ "scriptureReference": "John 3:16" } -
Review the output: The output of this workflow will be the fetched scripture text, ready to be used by subsequent nodes in your calling workflow (e.g., sending to Slack, saving to a database, etc.).
Related Templates
Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax
Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions
AI-powered code review with linting, red-marked corrections in Google Sheets & Slack
Advanced Code Review Automation (AI + Lint + Slack) Who’s it for For software engineers, QA teams, and tech leads who want to automate intelligent code reviews with both AI-driven suggestions and rule-based linting — all managed in Google Sheets with instant Slack summaries. How it works This workflow performs a two-layer review system: Lint Check: Runs a lightweight static analysis to find common issues (e.g., use of var, console.log, unbalanced braces). AI Review: Sends valid code to Gemini AI, which provides human-like review feedback with severity classification (Critical, Major, Minor) and visual highlights (red/orange tags). Formatter: Combines lint and AI results, calculating an overall score (0–10). Aggregator: Summarizes results for quick comparison. Google Sheets Writer: Appends results to your review log. Slack Notification: Posts a concise summary (e.g., number of issues and average score) to your team’s channel. How to set up Connect Google Sheets and Slack credentials in n8n. Replace placeholders (<YOURSPREADSHEETID>, <YOURSHEETGIDORNAME>, <YOURSLACKCHANNEL_ID>). Adjust the AI review prompt or lint rules as needed. Activate the workflow — reviews will start automatically whenever new code is added to the sheet. Requirements Google Sheets and Slack integrations enabled A configured AI node (Gemini, OpenAI, or compatible) Proper permissions to write to your target Google Sheet How to customize Add more linting rules (naming conventions, spacing, forbidden APIs) Extend the AI prompt for project-specific guidelines Customize the Slack message formatting Export analytics to a dashboard (e.g., Notion or Data Studio) Why it’s valuable This workflow brings realistic, team-oriented AI-assisted code review to n8n — combining the speed of automated linting with the nuance of human-style feedback. It saves time, improves code quality, and keeps your team’s review history transparent and centralized.
Auto-reply & create Linear tickets from Gmail with GPT-5, gotoHuman & human review
This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.