How Maya Built a Chatbot, Voice Agent and Phone Agent With Voiceflow, n8n and RAG

Maya was the kind of operations lead every fast-growing company relies on. She juggled support tickets, internal documentation, customer calls, and a calendar full of demos. Her team sold electronics online, and business was booming. Unfortunately, so were the repetitive questions.

“Where is my order?” “Can I book a call for tomorrow afternoon?” “What is your return policy for defective products?” These questions came through chat, email, and phone, at all hours. The information lived in different places: Google Drive docs, Google Calendar, a backend order tracking API, and a growing internal knowledge base. Every answer required context, and every context lived in a different tool.

Maya knew she needed automation, but not just any chatbot. She wanted a smart assistant that could talk, type, and even pick up the phone, all while pulling accurate answers from her company’s documents. That is when she discovered a workflow template built around Voiceflow, n8n, Google Calendar, Qdrant, OpenAI, and Twilio, using Retrieval-Augmented Generation (RAG) to keep responses grounded in real knowledge.

The Problem: Fragmented Knowledge, Frustrated Customers

On a Monday morning, Maya watched her support dashboard spike again. Customers were asking for order updates, trying to schedule appointments, and hunting for basic product info. Her team was drowning in repetitive work while complex tickets waited in the queue.

She had already tried a basic FAQ chatbot on the website, but it failed whenever the question required live data or referenced internal documents. It could not read Google Calendar, it did not know anything about their updated warranty policy on Google Drive, and it definitely could not check order status from the backend API.

What she needed was a single automation layer that could:

• Hold natural conversations in chat, voice, or phone
• Pull answers from a real knowledge base using RAG
• Create and read appointments from Google Calendar
• Check order status via an internal API

She wanted a system that separated concerns, used the best tools for each task, and still felt like one unified assistant to the customer.

The Discovery: A Modular Voiceflow + n8n + RAG Architecture

While researching automation tools, Maya came across an n8n workflow template titled “Voiceflow Chatbot, Voice & Phone Agent with RAG.” It promised exactly what she was imagining: a chat, voice, and phone agent that could answer knowledge questions, track orders, and manage appointments.

The architecture clicked for her immediately. Each tool did what it was best at:

• Voiceflow handled conversational design and deployment to chat widgets, voice, and phone channels (including Twilio numbers).
• n8n acted as the orchestration layer, running webhooks, integrating with APIs, and connecting LangChain-style LLM nodes.
• OpenAI provided LLM capabilities for intent parsing, response generation, and embeddings.
• Qdrant served as a vector database that powered Retrieval-Augmented Generation so answers could reference actual documents.
• Google Calendar managed appointments created directly from conversations.

Instead of trying to force one platform to do everything, this stack let each piece specialize. Voiceflow talked to customers, n8n coordinated the logic, Qdrant remembered the documents, and OpenAI turned context into fluent answers.

The Plan: One Conversation, Three Webhooks, Many Capabilities

Maya sketched the high-level flow on a whiteboard. A customer would interact with a Voiceflow experience, either on the website chat widget or via a phone number. Voiceflow would collect what the customer wanted and call an n8n webhook. From there, n8n would decide what to do.

The architecture boiled down to a few key steps:

1. The user talks or types into Voiceflow (chat, voice widget, or phone call).
2. Voiceflow sends the captured data to n8n through a specific webhook, depending on the intent:
   • n8n_order for order tracking
   • n8n_appointment for calendar bookings
   • n8n_rag for knowledge questions handled with RAG
3. n8n runs the correct branch of the workflow:
   • Create or read Google Calendar events
   • Call the backend order tracking API
   • Run RAG retrieval from Qdrant and call the LLM agent
4. n8n returns a plain response back to Voiceflow.
5. Voiceflow continues the conversation and replies to the user in chat or voice.

The template Maya found already implemented this pattern. All she had to do was adapt it to her company’s data and rules. The real work would start with the knowledge base.

Rising Action: Teaching the Agent the Company’s Knowledge

Step 1 – Creating a Qdrant Collection

Maya knew that RAG depends on a solid vector store. The template guided her to start with Qdrant. She set up a Qdrant collection that would store embeddings of her company’s documents: return policies, warranty terms, product guides, and onboarding manuals.

Using n8n’s HTTP Request nodes, she automated the collection setup. The workflow could create or refresh the collection as part of a setup routine, so she did not have to manage Qdrant manually. Whether hosted herself or through a managed Qdrant provider, the pattern stayed the same: a clean collection ready to receive vectors.

Step 2 – Ingesting Documents From Google Drive to Qdrant

Next, she turned her scattered Google Drive into a searchable memory. The template included a pipeline that:

• Used Google Drive nodes to list files in a folder and download them.
• Converted Google Docs into plain text so the LLM could read them.
• Split long documents into smaller chunks using a Text Splitter, based on tokens or characters, for better retrieval granularity.
• Called the OpenAI Embeddings node to convert each chunk into a vector.
• Inserted those vectors into the Qdrant Vector Store.

For the first time, her policies and product docs were not just static files. They became a living, searchable knowledge base that the agent could use in real time. This would be the backbone of RAG-powered answers later.

Parallel Challenge: Order Tracking Without Human Handoffs

While the knowledge base was coming together, support tickets kept piling up with one recurring theme: “Where is my order?” Maya wanted the agent to answer that without a human stepping in every time.

Step 3 – Wiring Order Tracking Into n8n

She created an n8n webhook named n8n_order. Voiceflow would call this webhook whenever a customer gave an order number or asked about order status. Inside n8n, the workflow received the order number, then called the company’s backend tracking API.

The workflow then transformed the raw API response into a human-friendly message, something like “Your order #12345 is on its way and expected to arrive on Friday.” That response was sent back to Voiceflow, which delivered it in natural language, whether via chat or voice.

For the customer, it felt like a helpful agent. For Maya, it meant dozens fewer tickets per day.

The Turning Point: Letting the Agent Book Real Appointments

One afternoon, a sales rep pinged Maya: “The bot is great for FAQ and orders, but can it book demos for us?” That question became the turning point. If the agent could schedule appointments directly into Google Calendar, it would close the loop between interest and action.

Step 4 – Appointment Handling With Google Calendar

Maya extended the template’s appointment flow. In Voiceflow, she created an appointment capture block that collected the user’s preferred date, time, and email. This block called the n8n_appointment webhook.

On the n8n side, the workflow used an LLM node to normalize natural language like “next Tuesday at 3 pm” into a Google Calendar compatible format. The template relied on a structured output parser to ensure that the LLM always returned valid fields for start and end.

The JSON that the Google Calendar node expected looked like this:

// Example JSON expected by Google Calendar node
{  "start": "2025-10-21T14:00:00Z",  "end": "2025-10-21T15:00:00Z",  "summary": "Event title with user@example.com",  "description": "Created via Voiceflow agent"
}  

The workflow automatically added an hour to the end time, built a meaningful summary that included the user’s email, and created the event in the correct calendar. When the event was confirmed, n8n sent a success message back to Voiceflow, which replied to the user with a friendly confirmation.

Suddenly, the assistant was not just answering questions. It was filling the team’s calendar with qualified appointments.

The Heart of the System: RAG-Powered Knowledge Answers

With order tracking and appointments running smoothly, Maya returned to the original vision: an agent that could answer nuanced questions grounded in real documentation.

Step 5 – Running the RAG Pipeline

Whenever a user asked a knowledge question, Voiceflow routed it to the n8n_rag webhook. From there, the RAG pipeline kicked in:

1. The workflow embedded the user’s question using the OpenAI Embeddings node.
2. It queried Qdrant for the top-k most relevant document chunks.
3. It combined those retrieved snippets with the original question and sent them to a conversational LLM agent node in n8n.
4. The agent used a carefully designed system prompt that defined the domain, tone, and fallback behavior.
5. The final answer, enriched with citations or references when appropriate, went back to Voiceflow.

A typical webhook payload from Voiceflow to n8n for a knowledge query looked like this:

{  "query": {  "Question": "How do I return a defective product?",  "Email": "user@example.com"  }
}  

Thanks to RAG, the assistant could now explain the exact return policy for defective products, referencing the same Google Docs the legal team maintained, without hallucinating or guessing.

Voiceflow as the Front Door to Every Capability

To tie the whole experience together, Maya configured three capture blocks in Voiceflow, each pointing to a different n8n webhook URL:

• n8n_order for order tracking flows
• n8n_appointment for calendar events
• n8n_rag for knowledge queries

Each block sent structured data to n8n, then parsed the response and continued the conversation accordingly. If the company wanted a phone agent, all they had to do was connect a Twilio number in Voiceflow’s project settings and enable the phone channel. The same workflows would power web chat, voice, and phone calls.

System Prompts: Teaching the Agent How to Behave

Maya quickly learned that the system message was as important as the data. The template shipped with an example system prompt tailored to an electronics store, which she adapted to her brand’s voice and policies.

She followed a few key guidelines:

• Always instruct the agent to cite or reference retrieved documents when giving RAG answers, if it makes sense.
• Require honesty when the knowledge base does not contain the answer, and tell the agent to offer next steps like contacting support or escalating to a human.
• Keep the tone friendly and professional, using minimal jargon unless the user explicitly asks for technical detail.

With a clear system prompt, the agent felt consistent and safe, even as it pulled information from a growing number of documents.

Testing, Tuning, and Trusting the Workflow

Before rolling out the agent to all customers, Maya spent a week testing every path.

Testing and Validation

She used the Voiceflow test console to simulate user conversations and the n8n manual trigger nodes to run workflows step by step. She checked that:

• Webhook payloads from Voiceflow were parsed correctly in n8n.
• Google Calendar events had the right timezone, start time, and one-hour duration.
• RAG retrieval returned relevant context, and the LLM referenced sources where needed.

Whenever something looked off, she adjusted chunk sizes, retrieval parameters, or the system prompt until the answers felt natural and reliable.

Keeping It Safe and Sustainable: Security, Monitoring, and Costs

Security

Maya knew that automation without security was a risk she could not take. She secured webhooks using shared secrets or tokens so only trusted calls could trigger workflows. API keys for OpenAI, Qdrant, Google, and Twilio were stored in n8n and Voiceflow secrets, never in plaintext inside nodes or scripts. Qdrant access was limited with network rules and API keys to protect the knowledge base.

Monitoring

To keep an eye on behavior, she logged webhook requests and responses, as well as LLM outputs, for debugging and auditing. She added alerts using n8n and external monitoring so that failed integrations or unusual error rates would trigger notifications before customers felt the impact.

Cost Awareness

Finally, she tracked costs across the stack:

• OpenAI usage for both LLM calls and embeddings
• Qdrant hosting fees
• Voiceflow paid plans, especially for phone channels
• Twilio phone number and call usage
• n8n hosting or infrastructure costs

To keep things efficient, she designed caching strategies where possible and limited unnecessary RAG retrievals. Only questions that truly required document context triggered the more expensive steps.

When Things Go Wrong: How Maya Troubleshoots

Even with a robust setup, edge cases appeared. Over time, Maya built a simple troubleshooting playbook.

• Incorrect calendar times: She checked timezone handling in the LLM parser and verified that Google Calendar credentials and settings matched the company’s default timezone.
• Weak or off-topic RAG answers: She experimented with a higher top-k value for retrieval, tried different embedding models, refined document chunking, and cleaned up source documents to remove irrelevant sections.
• Webhook failures: She validated network reachability, confirmed that Voiceflow function nodes used the correct HTTP method and headers, and reviewed n8n logs for error details.

Best Practices She Learned Along the Way

By the time the assistant was live, Maya had a short list of best practices she would recommend to any team:

• Use structured output parsing in n8n when you need strict schemas, especially for dates, start and end times, or numeric values.
• Keep system prompts concise but very specific, including instructions for citations and fallback paths.
• Design conversation flows with graceful error handling and clear escalation paths to human agents.

The Resolution: From Overwhelmed Support to a Unified AI Agent

Within a few weeks of launch, the impact was obvious. The new Voiceflow + n8n + RAG agent handled a large share of order tracking questions, booked appointments directly into Google Calendar, and answered detailed policy questions by referencing internal documents stored in Qdrant.

Convert CSV to JSON with an n8n Webhook (Step-by-step)

If you ever find yourself staring at a CSV file thinking, “I just want this as JSON already,” this guide is for you. In this walkthrough, you’ll learn how to build a flexible CSV-to-JSON webhook in n8n that can:

• Accept CSV as a file upload or raw text
• Handle different delimiters like commas and semicolons
• Return clean JSON that is ready for APIs, databases, or other workflows
• Gracefully handle errors and even ping you in Slack when something breaks

We’ll go through the full n8n workflow template step by step, plus talk about how to make it production-ready. Think of this as your starter kit for a CSV-to-JSON microservice.

What this n8n workflow actually does

At a high level, this workflow exposes a webhook endpoint that converts CSV to JSON on demand. A client sends CSV data to the webhook, and n8n responds with a structured JSON payload.

It supports three main input styles:

• A CSV file upload (binary data)
• Raw CSV text in the request body
• JSON that is already structured (for which the workflow can simply pass through or adapt)

Behind the scenes, the workflow:

1. Receives the request in a Webhook node
2. Uses a Switch node to figure out what kind of input it got
3. Extracts CSV content if a file was uploaded
4. Normalizes everything into a single CSV string field
5. Parses that CSV into JSON with a Code node
6. Validates and aggregates the result
7. Returns a JSON response, or a clear error message if something goes wrong
8. Optionally notifies a Slack channel when errors happen

Why use n8n for CSV to JSON conversion?

n8n is an open-source automation platform that is really good at gluing systems together. Instead of spinning up a custom service every time you need to transform CSV, you can let n8n handle it for you.

This CSV-to-JSON webhook template is especially handy when you:

• Need a quick API endpoint that accepts CSV and returns JSON
• Want to feed CSV data into other tools, APIs, or databases
• Have different clients sending data in different formats (file uploads vs raw text)
• Prefer to maintain your logic visually in a workflow instead of code-only services

In short, you get a lightweight data transformation pipeline without having to maintain a separate app or server.

Workflow architecture at a glance

Here is the main cast of nodes in this n8n template:

• Webhook (POST) – entry point that receives CSV or JSON
• Switch – routes the execution based on input type
• Extract From File – pulls CSV text out of uploaded files
• Set (Change Field) – maps incoming data into a single CSV field
• Code (Convert Raw Text To CSV) – parses CSV text into JSON
• If / Check – validates that parsing worked as expected
• Aggregate – combines parsed rows into a JSON array
• RespondToWebhook – sends success or error responses
• Slack (optional) – notifies your team when something fails

Let’s unpack how each part works, and how you can tweak it.

Step-by-step: building the CSV-to-JSON webhook

1. Webhook node: accepting the request

Start with a Webhook node configured to accept POST requests. In the node settings:

• Set the HTTP method to POST
• Set responseMode to responseNode so that a later node controls the final HTTP response
• Enable file uploads by configuring a binary property

The template uses the binary property name data. That means if a client uploads a file, it will be available under the binary field data for downstream nodes.

2. Switch node: routing by input type

Next comes a Switch node that decides how to handle the incoming request. It checks:

• Whether there is binary file data present
• The Content-Type header, usually via $json.headers['content-type']

Common branches you will want:

• File upload – if binary data is present, treat the input as a CSV file
• Raw CSV text – if Content-Type is text/plain
• JSON payload – if Content-Type is application/json

This routing step lets the same webhook endpoint handle multiple formats without forcing all clients to behave the same way.

3. Extract From File: reading CSV content from uploads

If the request contains a CSV file, it flows into an Extract From File node (or an equivalent Binary to Text conversion). This node reads the binary content from the data field and outputs it as text.

The important part is that, after this node, you have a plain string that contains the CSV data. That string can then be passed to the parser just like raw text from the request body.

4. Set node: normalizing raw CSV text

When CSV is sent directly as text, you want to standardize where that text lives in the item. A Set node is perfect for this.

For example, if the raw text is in $json.body, you can map it into a dedicated field like csv:

• Create a new field named csv
• Set its value to {{$json.body}} (or whatever property holds the CSV string)

By the time the data reaches the Code node, everything – whether it started as a file or raw text – is available as a single CSV string in a consistent field. That keeps the parsing logic much simpler.

5. Code node: converting CSV text into JSON

This is where the actual CSV-to-JSON magic happens. The Code node uses JavaScript to turn the CSV string into an array of JSON objects.

The template uses a straightforward parser that:

• Reads the CSV from the csv field
• Splits the file into lines
• Uses the first line as headers
• Splits each subsequent line into values and maps them to the headers
• Supports both comma and semicolon delimiters via a regular expression

Here is the exact code used in the workflow:

const csvData = $input.all()[0]?.json?.csv;

// Use a regex to split on either ',' or ';'
const lines = csvData.split("\n");
const headers = lines[0].split(/,|;/);

const jsonData = lines.slice(1).map((line) => {  const data = line.split(/,|;/);  let obj = {};  headers.forEach((header, i) => {  obj[header] = data[i];  });  return obj;
});

if (jsonData.length === 0) {  throw new Error("No data to process");
}

return jsonData;

A couple of important notes about this approach:

• It is intentionally simple and works well for many basic CSV files.
• It does not handle quoted fields that contain commas, semicolons, or newlines.
• It also does not fully cover edge cases like escaped quotes.

If you need full CSV compatibility, you can either:

• Use a proper CSV parsing library like csv-parse in a server-side context and call it from n8n, or
• Implement a more robust parser inside a Function or Code node using a zero-dependency CSV parser

6. Validation, aggregation, and building the response

Once the CSV has been parsed, the workflow performs a quick sanity check and then prepares the final JSON response.

Typical steps here include:

• An If or Check node that verifies the parser returned items and did not throw an error.
• Aggregate nodes that combine the individual items into a single JSON array, which is usually more convenient for clients to consume.
• A RespondToWebhook node that sends the aggregated JSON back to the caller.

Because the Webhook node is configured with responseMode = responseNode, this RespondToWebhook node is what ultimately controls the HTTP response that the client sees.

7. Error handling and Slack notifications

Things will break occasionally, especially when clients send malformed CSV. The workflow is wired so that error paths lead into a dedicated RespondToWebhook node that returns a standardized error payload, such as:

{  "status": "error",  "data": "There was a problem converting your CSV. Please refresh the page and try again."
}

This gives your clients a predictable format for handling failures.

If you want extra visibility, you can also plug in a Slack node on the error branch. That way, whenever a conversion fails, your team gets a message in Slack and can investigate.

How to test your CSV-to-JSON webhook

Once the workflow is active, you will want to verify that it behaves correctly for both file uploads and raw text. Here are two quick curl examples you can use from the command line.

Test with a CSV file upload

For sending a CSV file directly:

curl -X POST "https://yoururl.com/webhook/tool/csv-to-json" \  -H "Content-Type: text/csv" \  --data-binary @path/to/your/file.csv

Replace https://yoururl.com/webhook/tool/csv-to-json with your actual webhook URL and @path/to/your/file.csv with the path to your CSV file.

Test with raw CSV text in the body

To send CSV directly as plain text:

curl -X POST "https://yoururl.com/webhook/tool/csv-to-json" \  -H "Content-Type: text/plain" \  --data-binary $'album,year,US_peak_chart_pos\nElephant,2003,6\nIcky Thump,2007,2'

This sends a small CSV snippet inline. It is perfect for quick testing while you tweak the workflow.

Making it production-ready

Once you have the basic CSV-to-JSON conversion working, you will probably want to harden it a bit before exposing it to real users or external systems.

Improve CSV parsing robustness

The simple regex-based approach in the Code node is great for demos and straightforward files, but production data can be messy. Consider:

• Quoted fields that contain commas or semicolons inside them
• Fields with escaped quotes, such as "He said ""Hello"""
• Different newline styles like \r\n vs \n

For those cases, a dedicated CSV parser is a safer bet. You can:

• Use a library like csv-parse in a server-side environment and integrate it with n8n, or
• Embed a lightweight CSV parsing implementation in a Function node

Handle large files and performance

Very large CSV files can be challenging if you try to load everything into memory inside a single workflow run. To keep performance under control:

• Set sensible file size limits on uploads
• Consider streaming the CSV outside n8n and only sending processed chunks in
• Store large files in object storage and trigger a separate workflow to process them asynchronously

This kind of design helps you avoid timeouts and memory pressure when clients send massive datasets.

Security and access control

Since this workflow exposes a webhook endpoint, you will want to protect it, especially if it is on the public internet. Some good practices:

• Require authentication, for example with an API key, bearer token, or n8n’s built-in auth options
• Restrict and validate accepted Content-Type headers
• Sanitize all inputs and reject unexpected or malformed CSVs politely
• Validate that required headers or columns are present before processing

Content-Type based routing

Reliable Content-Type detection makes the Switch node more accurate. The workflow typically checks:

• $json.headers['content-type'] === 'text/plain' for raw CSV text
• $json.headers['content-type'] === 'application/json' for JSON bodies
• The presence of binary data to treat the request as a file upload

This keeps your routing logic predictable and easier to maintain.

What you can do after converting CSV to JSON

Once your CSV data is in JSON form, you can plug it into almost anything. Some popular follow-up steps in n8n include:

• Validating and normalizing fields such as dates, numbers, and enums
• Enriching rows with lookup calls to APIs or databases
• Inserting the data into a database table
• Sending the JSON to another API endpoint
• Streaming parsed objects into a message queue or data warehouse

The CSV-to-JSON webhook becomes a reusable building block that you can drop into larger automation workflows.

Troubleshooting: when things are not working

If the output is not what you expect, here are a few quick checks:

• Empty output? Inspect the raw CSV body in the Webhook node to confirm that headers and newlines are present.
• Parsing errors? Add console.log statements or throw descriptive errors in the Code node to see exactly where it fails.
• Weird delimiters or formats? Test with multiple CSV examples, including semicolon-delimited files, missing cells, or extra columns, and adjust the parser accordingly.

Pre-launch checklist

Before you put this CSV-to-JSON webhook into production, run through a quick checklist:

• Authentication and rate limiting are configured

Build an n8n AI Agent with LangChain & MCP

What if every repetitive question, every manual lookup, and every tiny calculation could be handled for you, in the background, by an AI Agent that you control? The “MCP Learning” n8n template is designed to be that turning point. It connects a chat trigger, an AI Agent powered by LangChain and OpenAI, and a set of reliable tools (Calculator, HTTP requests, and an MCP client) so you can move from doing tasks yourself to orchestrating them with automation.

This guide walks you through that journey. You will start with the problem of scattered, manual work, shift into what becomes possible when you adopt an automation mindset, then learn how this n8n template gives you a practical, ready-to-use pattern you can adapt and grow over time.

The problem: manual tasks stealing your focus

Most teams face the same bottlenecks. A customer asks a question, and you jump into a system to check an order. A colleague wants a quick calculation, and you reach for a spreadsheet. An internal request appears, and you manually trigger an API call or microservice. Each action seems small, but together they drain time and attention that could be spent on strategy, creativity, or growth.

AI and automation can help, but only if they are connected in a safe and structured way. You need something that can:

• Listen to real chat messages and act on them
• Use a powerful LLM like OpenAI to understand intent
• Call tools in a controlled, auditable way
• Keep side effects isolated and traceable

This is exactly where the “MCP Learning” n8n workflow template comes in. It is not just a demo, it is a reusable pattern that you can apply to customer support, internal operations, and any workflow that benefits from an AI agent with real tools at its disposal.

Mindset shift: from doing the work to designing the system

Adopting automation is not only about technology, it is about mindset. Instead of asking, “How do I respond to this request?” you start asking, “How can I design a system that responds for me, safely and reliably?”

With n8n, LangChain, and MCP, you are not building a one-off chatbot. You are building an AI Agent that:

• Understands natural language through an OpenAI Chat Model
• Plans actions using LangChain agent logic
• Calls well-defined tools like a Calculator, HTTP Request, or MCP Client
• Executes side effects in a separate, controlled flow using an MCP Server Trigger

This template encourages you to think like an architect of your own workflows. You define which tools are available, you set the rules, and you decide how far the agent can go. Every improvement you make compounds over time, freeing more hours and mental energy for the work that actually moves you or your business forward.

The n8n AI Agent template: your starting point

The “MCP Learning” template shows a modern way to orchestrate AI inside n8n. It connects:

• A chat trigger that starts the workflow when a message is received
• An AI Agent node powered by LangChain logic
• An OpenAI Chat Model for language understanding and generation
• Tools like a Calculator, HTTP Request, and MCP Client that the agent can call
• An MCP Server Trigger that handles those MCP tool calls on a separate flow

Once set up, you can send a message to your chat entry point, watch the agent interpret it, decide whether it needs to call a tool, and then respond with a clear, verifiable answer. It is a powerful pattern that you can adapt to fit your specific use cases.

How the workflow behaves: from message to meaningful action

To understand the transformation this template enables, it helps to walk through the full journey of a single message:

1. A user sends a chat message. This could be from a webhook or any supported platform integration.
2. The message lands in the When chat message received node, which triggers the workflow and passes the content onward.
3. The AI Agent node receives the message. Using LangChain logic and the configured OpenAI Chat Model, it interprets the user’s intent and plans its next steps.
4. If the agent decides it needs extra information or a precise operation, it calls one of the tools available to it, such as:
   • Calculator for deterministic numeric calculations
   • HTTP Request (Tool) to reach external APIs or microservices
   • MCP Client to interact with tools managed via MCP
5. Tools run in isolation. When the MCP Client is used, the request is handled by a separate flow that starts from the MCP Server Trigger. This trigger receives the tool call, executes the required action, and returns the result.
6. The AI Agent gathers the tool outputs, composes a final response, and sends it back to your chat platform or any downstream system you connect.

The result is a loop where your AI Agent not only talks, but acts. It can compute, fetch data, and perform side effects in a controlled, observable way.

Node-by-node: what powers this template

When chat message received

This node is the entry point. It listens for incoming chat events, either via webhook or a platform-specific trigger. Every user message that reaches this node becomes a potential automated interaction handled by your AI Agent.

AI Agent

The AI Agent node is the coordinator of the entire experience. It:

• Receives the incoming chat message
• Runs your LangChain agent logic for planning and tool selection
• Decides when to call tools and how to combine their outputs
• Generates a final response for the user

In n8n, this node integrates directly with LangChain, connecting the chat model and your tool set into one cohesive agent behavior.

OpenAI Chat Model

The OpenAI Chat Model node provides the language intelligence. In the template, it is configured with a modern model such as gpt-4.1-nano, but you can choose the model that fits your needs and budget.

Before you enable this node, make sure your OpenAI credentials are correctly configured in n8n under Credentials → OpenAI API Key. This step unlocks the LLM that powers understanding, reasoning, and natural language responses.

MCP Client & MCP Server Trigger

The MCP pair is what makes structured tool orchestration possible. They work together as a client-server pattern inside your automations:

• MCP Client appears to the AI Agent as a tool it can call. When chosen, the agent sends its request through this client.
• MCP Server Trigger runs in a separate n8n flow. It listens for incoming MCP tool calls, executes the desired action, and returns the result.

This separation lets you run tools in isolation, keep side effects under control, and centralize how tool calls are logged and monitored. It is a scalable pattern for safe, auditable AI-driven actions.

Calculator

The Calculator node is a simple but powerful example of a deterministic tool. Instead of asking the LLM to approximate a calculation, the agent calls this node to perform exact numeric operations. This keeps results auditable and reduces the risk of hallucinated numbers.

HTTP Request (Tool)

The HTTP Request node, configured as a tool, lets your agent talk to the outside world. It can:

• Send GET or POST requests
• Call internal microservices or external APIs
• Trigger background jobs or workflows in other systems

In the template, it serves as a hook for richer automations, like checking order statuses, updating records, or kicking off internal processes.

Getting started: bring the template into your n8n space

To turn this pattern into something you can test and customize, follow these steps in your own n8n instance:

1. Import the template JSON Go to Workflow → Import from JSON and paste or upload the template file.
2. Configure OpenAI credentials In Credentials, add or update your OpenAI API Key, then link it to the OpenAI Chat Model node.
3. Set up the chat trigger Configure the webhook URL or connect the trigger to your chat platform integration so that incoming messages can reach the workflow.
4. Align MCP settings Double check the MCP Server Trigger webhookId and the MCP Client sseEndpoint. Update them so they match your MCP deployment or local test instance.
5. Adjust HTTP Request parameters If you plan to call specific APIs, customize the HTTP Request node body, headers, and endpoints to match your services.
6. Activate and test Turn the workflow on, send a few sample messages, and watch how the agent calls tools and responds.

Within a few minutes, you will have a working AI Agent in n8n that you can observe, refine, and extend.

Best practices to keep your AI Agent safe and reliable

Security and credentials

Security is part of building mature automation. To keep your workflows safe:

• Never hard-code API keys or tokens in workflow JSON
• Use n8n Credentials to store secrets securely
• Restrict the HTTP Request tool to trusted endpoints
• Implement authentication and authorization where possible

Tool design

The more deterministic your tools are, the more predictable your agent becomes. Ideal tools include:

• Calculators and numeric operations
• Lookups against internal databases or APIs
• Authenticated HTTP calls with clear input and output formats

This approach minimizes hallucinations and makes debugging far easier.

Agent prompts and safety

Strong system prompts are your guardrails. Use them to define:

• The scope of what the agent should and should not do
• Which tools it may call and in what situations
• How it should refuse or escalate unsafe or unclear requests

For tools with side effects, consider adding a verification step. For example, you can require explicit confirmation from the user or a structured request format before the agent calls a tool that changes data or triggers external actions.

Observability and auditing

To grow your automation confidently, you need visibility into what the agent is doing. Combine:

• Logs of all tool calls and agent plans
• Model responses captured in n8n
• The MCP Server Trigger pattern, which centralizes tool execution logs
• n8n’s execution history for a full audit trail

This makes it much easier to troubleshoot, improve prompts, and demonstrate that your AI workflows are behaving as intended.

Real-world use cases: where this template can take you

Once you understand the pattern, you can apply it to many scenarios. A few common examples include:

• Customer support assistants that compute refunds, check order statuses, or call backend APIs for account information
• Automated data enrichment that uses HTTP tools to fetch external records and update your internal systems
• Internal automation for scheduling tasks, running computations, or handling form submissions without manual intervention

Each new use case builds on the same foundation: a chat trigger, an AI Agent, and a set of well-designed tools.

Troubleshooting common issues

Agent not responding

If the agent seems silent, check:

• That the chat trigger webhook or platform integration is correctly configured
• That your OpenAI credential is valid and linked to the Chat Model node
• That the AI Agent node is pointing to the correct model configuration

Tool calls failing

When tools fail, start with visibility:

• Inspect node execution logs in n8n for errors
• Verify that endpoints, authentication headers, and payloads in the HTTP Request node are correct
• For MCP-related issues, confirm that SSE and server endpoints are reachable and that webhookId values match between the MCP Client and MCP Server Trigger

Extending the template into your own automation system

This template is intentionally designed as a foundation. Once it is running, you can expand it step by step:

• Add new tools for database queries, calendar APIs, or internal microservices
• Extend the LangChain configuration with memory, multi-step planning, or custom tool logic
• Introduce persistent memory nodes to support longer, context-aware conversations
• Implement rate limiting and retry logic for external APIs
• Include validation and sanitization steps for user inputs before they reach critical tools

Each enhancement brings you closer to an automation ecosystem where your AI Agent becomes a trusted collaborator, not just a novelty.

Demo prompts to explore and experiment

Once the template is active, try messages like these to see the tools in action:

• “Calculate the monthly payment for a $20,000 loan at 5% over 60 months.” This should invoke the Calculator tool for a precise result.
• “Check status of order 12345 and notify me if it’s delayed.” This can trigger an HTTP Request to your order API and return a status.
• “Please schedule a follow-up email in three days if the client doesn’t respond.” This can be wired to an internal scheduling API or microservice.

Use these as a starting point, then adapt them to your own data, systems, and workflows.

From template to transformation

The “MCP Learning” n8n template is more than a sample workflow. It is a practical, extensible pattern for building safe, auditable AI-assisted automations with LangChain and OpenAI. By separating language understanding in the LLM and Agent from deterministic tool execution through Calculator, HTTP, and MCP, you gain both flexibility and control.

As you refine prompts, design better tools, and add new integrations, this pattern can grow with you. It can support customer-facing bots, internal automations, and hybrid human-AI workflows that save time, reduce errors, and free your team to focus on what matters most.

Take the next step

If you are ready to move from manual tasks to designed systems, now is the moment to experiment. Import the template into your n8n instance, connect your OpenAI credentials, and try the demo prompts. Watch how the agent behaves, then iterate.

Use this template as a stepping stone toward a more automated, focused workflow. With each improvement, you will build an AI-powered automation layer that supports your goals instead of distracting from them.

If you want help adapting the template to your

Turn Your Newsletters into Daily Twitter Threads (Without Losing Your Mind)

If you have ever copied chunks of your newsletter into Twitter, chopped them into 280-character bits, fixed broken links, rewrote the hook, then did it all again tomorrow, this guide is your new best friend.

Instead of manually turning long-form content into Twitter (ok, X) threads every day, you can let an n8n workflow and an LLM do the heavy lifting. You keep the editorial judgment and final say, the robots handle the repetitive stuff that slowly erodes your will to create.

Below is a practical, production-ready setup that combines:

• n8n automation
• An LLM via LangChain or a provider like Anthropic Claude Sonnet
• Structured output parsing so the thread format does not implode
• Slack publishing for human review before anything hits X

The result: reliable, daily Twitter threads from your newsletter with minimal manual effort and maximum control.

Why Turn Newsletters into Twitter Threads Anyway?

Your newsletter is already doing the hard work: curated links, original insights, commentary your audience actually cares about. Letting that live in inboxes only is like writing a book and then hiding it in a drawer.

Repurposing newsletters into Twitter threads helps you:

• Boost reach by surfacing long-form ideas into fast-moving social feeds.
• Bring new people in by driving traffic back to your newsletter and site.
• Reuse what you already have instead of inventing new social content from scratch every day.

In other words, you get more output from the same input, without more caffeine or extra interns.

What This n8n Workflow Actually Does

This workflow mimics the core automation pipeline used by modern newsletters to publish daily news threads. Here is the high-level flow:

1. Take in your newsletter content using a trigger that fits your stack.
2. Feed the model examples of how you like your threads written.
3. Build a precise prompt that tells the LLM exactly how to structure the thread.
4. Call the LLM using LangChain or a provider node such as Anthropic Claude Sonnet.
5. Parse the model output into clean, numbered tweets.
6. Send the draft to Slack for editorial review, edits, and scheduling.

So instead of wrestling with character limits and formatting, your team just reviews, tweaks, and approves.

Meet the n8n Nodes Behind the Magic

The sample n8n template uses a handful of nodes, each doing a very specific job so the whole pipeline stays predictable and maintainable.

• workflow_trigger – Entry point where your newsletter content arrives.
• set_examples – Stores thread examples and style guidance for the LLM.
• build_prompt – Combines newsletter text with your prompt template.
• write_twitter_thread – Calls the LLM via LangChain or a provider node like Anthropic Claude Sonnet.
• twitter_parser – Parses the LLM response into a structured, tweet-by-tweet format.
• set_result – Saves the final thread text into the workflow payload.
• share_twitter_thread – Posts the draft thread to Slack for your editors and schedulers.

Each node does one thing well, which makes debugging a lot easier when something inevitably goes off script.

Step-by-Step: Build the Newsletter-to-Twitter Workflow

Let us walk through the setup from start to finish. You can adapt the details to your tech stack, but the structure stays the same.

1. Choose How the Newsletter Enters n8n (Trigger & Input)

First, decide how the workflow gets your newsletter content. A few common options:

• Manual trigger so editors can paste the final copy directly.
• Webhook that receives HTML or text from your CMS once the newsletter is ready.
• Scheduled trigger that pulls the latest edition from storage like S3 or Google Drive.

Pick the one that matches how your newsletter is produced today, not your fantasy future stack.

2. Teach the LLM Your Thread Style (Provide Examples)

LLMs are powerful, but they are not mind readers. Your set_examples node is where you spoon-feed them your preferred style.

In this node, include:

• 2 to 3 examples of high-performing threads from your brand or niche.
• Short, numbered tweets that are informative and easy to skim.
• Guidance like:
  • “Use numbered tweets (1/ …)”
  • “Bold key terms”
  • “Keep paragraphs short”

Think of this as onboarding a very fast junior social media editor.

3. Build a Clear, Bossy Prompt

Next up, the build_prompt node. This is where you merge the newsletter text with a prompt template that tells the model exactly what to do.

Your prompt should instruct the LLM to:

• Select the top 4 to 8 news items from the newsletter.
• Write a strong hook tweet followed by a numbered thread (1/ – N/).
• Use line breaks, minimal emojis, and include @mentions for well-known businesses when referenced.
• Preserve URLs exactly as they appear in the newsletter.
• Finish with a CTA that links back to your newsletter, for example https://recap.aitools.inc.

The more explicit you are here, the fewer surprises you will get in the output.

4. Call the LLM with LangChain or Provider Nodes

Now it is time to let the model cook. In the write_twitter_thread node, you call your chosen LLM through LangChain or a provider-specific n8n node.

In the example template, Claude Sonnet 4 is used, but you can swap in your favorite model. A few key settings to configure:

• System role that defines the persona, for example:
  “expert AI tech communicator and social media editor.”
• Max tokens so the model does not produce a novel instead of a thread.
• Temperature between 0.0 and 0.4 for more deterministic, less chaotic output.
• Inline inclusion of your prompt examples to keep style consistent.

Set it up once, then let the node quietly do its job in the background every day.

5. Parse the Thread into Clean Tweets

LLM output is naturally free-form, which is charming until you try to schedule it programmatically. The twitter_parser node fixes this.

Use a structured output parser, such as:

• A JSON schema that defines the expected fields, or
• A regex-based node that extracts the numbered tweets

This lets you:

• Split the thread into individual tweets automatically.
• Catch stray formatting, unexpected characters, or markup that might break the thread.
• Keep the output predictable enough for review and scheduling tools.

The parsed result is stored by the set_result node so the rest of the workflow can use it cleanly.

6. Keep Humans in the Loop with Slack QA

Even the best automation should not have direct access to your social accounts without a human sanity check. That is where the share_twitter_thread node comes in.

This final node posts the full thread into an editorial channel in Slack (or similar) along with a permalink back to the original newsletter. Your editors can then:

• Verify that facts and quotes are accurate.
• Check that links and @mentions are correct and not hallucinated.
• Edit copy, tighten hooks, and give the final approve-to-publish signal.

You get the speed of automation without the “why did the bot tag the wrong account” headaches.

Prompt Engineering Tips for Reliable Twitter Threads

A few tweaks to your prompts can make the difference between a clean, on-brand thread and something that looks like it escaped from a prompt playground.

• Always ask for a header tweet with a clear hook and context, for example:
  “TODAY’S AI NEWS: …”
• Force URL fidelity by telling the model to copy external links exactly as provided.
• Limit tweet length with instructions such as:
  “No tweet > 250 characters”
  to keep things readable and leave room for quote tweets and comments.
• Use @mentions carefully. Specify that mentions should only be used for known accounts explicitly referenced in the newsletter to reduce hallucinations.
• Define formatting rules, such as:
  • Numbered tweets
  • Bold for emphasis
  • Markdown or plaintext depending on your publishing destination

Be specific, be repetitive, and do not assume the model will “just know” what you want.

Handling Edge Cases and Staying Safe in Production

Once this workflow is running daily, you will want it to be boringly reliable. That means handling the weird stuff up front.

• Hallucinated links or quotes
  Flag any external URLs that were not in the original newsletter so editors can double-check before publishing.
• Sensitive or private data
  Sanitize PII and anything confidential before sending content to the model.
• Rate limits and retries
  Implement exponential backoff or queueing for LLM API calls, especially if you are running multiple newsletters or high volume.

A little defensive engineering here saves a lot of late-night debugging later.

Testing the Workflow Before Full Rollout

Instead of flipping the automation switch for everything on day one, treat this like any other experiment.

• Run an A/B test:
  • Half of your threads created manually
  • Half generated by the n8n + LLM workflow
• Measure:
  • Engagement uplift on X
  • Click-through rate back to the newsletter
  • Error rate, for example how many manual edits per automated thread

Use the results to refine your prompts, examples, and parsing until the automated threads perform at least as well as your hand-crafted ones.

What This Looks Like with Real Content

Here is a short newsletter excerpt that this workflow can turn into a full thread:

“AI models from Google and OpenAI achieved gold medal performance at the International Mathematical Olympiad, solving five of six problems with human-readable proofs. A separate incident highlighted risks: an AI coding assistant deleted production data, underscoring the need for guardrails.”

From this, the LLM should produce a 6 to 8 tweet thread that includes:

• A hook tweet summarizing “today in AI” style news.
• Bullets or tweets detailing the IMO achievement and what it means.
• A cautionary note about AI agents and production data risks.
• A closing CTA inviting readers to subscribe to the full newsletter.

All of that without you manually trimming sentences to fit the character limit.

What Success Looks Like When This Is Live

Once the pipeline is humming along, you should start to see:

• Faster social distribution for each new newsletter edition.
• More referral traffic from X back to your newsletter signup page.
• Less manual grunt work for your social team, while still keeping editorial control via Slack review.

You get the consistency of automation plus the polish of human editors.

Quick Setup Checklist: From Zero to Automated Threads

Ready to try this with your own newsletter? Here is a simple path to get started:

1. Clone or build the n8n workflow and connect your LLM credentials (Anthropic, OpenAI, or similar).
2. Create 2 to 3 example threads that match your brand voice and add them to the set_examples node.
3. Configure the parser node with a simple JSON schema such as:
   {"twitter_thread": "string"}
   to extract the full thread from the model output.
4. Route the final output to Slack in an editorial channel where your social team can review, edit, and schedule.

Next Steps and Where to Get the Template

If you are ready to stop hand-crafting every thread and want a daily stream of high-impact Twitter content from your newsletter, this workflow is a solid starting point.

Try it with your next edition, monitor engagement, and iterate. For more workflows, prompts, and automation templates, subscribe at https://recap.aitools.inc and request the n8n template so you can get up and running quickly.

n8n GraphQL Projects Template Explained

This article provides an in-depth explanation of an n8n workflow template that integrates with an AppSync GraphQL Projects API. The workflow is designed to query project data, support AppSync-style pagination with nextToken, merge multiple input sources, and adapt its behavior based on incoming parameters. If you operate n8n in a production or near-production environment and need a robust pattern for querying GraphQL endpoints, this guide breaks down the complete node structure, data flow, and recommended improvements.

Use Case and Core Capabilities

The primary objective of this template is to automate the retrieval of project records from a GraphQL API in a controlled and reusable way. It is optimized for scenarios where workflows must:

• Accept multiple input sources, such as a triggering workflow and a temporary session file
• Combine those inputs into a single, coherent request payload
• Handle AppSync pagination using nextToken
• Apply conditional logic and post-processing based on response data

The result is a pattern that can be dropped into broader automation chains to reliably fetch and normalize project data for downstream processing, reporting, or synchronization tasks.

High-Level Workflow Architecture

At a conceptual level, the workflow executes the following sequence:

1. Receives parameters from a parent workflow through an Execute Workflow Trigger
2. Reads a JSON session or authentication file from disk
3. Parses the file content to extract the authentication token and related metadata
4. Deletes the temporary file for security and housekeeping
5. Merges all input sources into a single item
6. Executes a ListProjects GraphQL query against the AppSync endpoint
7. Evaluates the presence of nextToken to determine if additional pages exist
8. Prepares variables for subsequent paginated calls when required
9. Aggregates all pages into a unified result set using a Code node

Trigger and Input Handling

Execute Workflow Trigger – Entry Point

The workflow is not intended to run in isolation. It is initiated by another n8n workflow through the Execute Workflow Trigger node. This node typically receives parameters that influence the GraphQL query, such as:

• fileName – the name of the session or token file to read
• status – a filter for project status
• name – a filter for project name or partial match

These fields are later mapped into GraphQL variables and filters, which allows the calling workflow to dynamically scope which projects are retrieved.

Reading and Parsing Session Data

Read/Write Files from Disk

The next step is to load session-related information from disk. The Read/Write Files node reads a JSON file that contains session data, such as an auth_token. The file path is not hardcoded. Instead, it uses the fileName provided by the trigger or a fallback default.

This pattern is useful for short-lived credentials or tokens that are generated by an upstream process and persisted only for the duration of a single workflow execution.

Extract from File

Once the file has been read, the Extract from File step parses the JSON content and exposes individual fields as structured data within n8n. For example, the auth_token is extracted and made available to subsequent nodes so that the GraphQL request can set a dynamic Authorization header.

Secure Cleanup of Temporary Files

Execute Command

To avoid leaving sensitive material on disk, the workflow executes a shell command using the Execute Command node:

rm -rf {{ $json.fileName }}

This deletes the temporary session file after it has been read and parsed. While this is effective for cleanup, it must be used with caution. Filenames should always be validated or sanitized to mitigate the risk of command injection or accidental deletion outside the intended directory.

Unifying Inputs Before the GraphQL Call

Merge Node – Combining Multiple Sources

The workflow then uses an n8n Merge node to consolidate data from three different branches:

• The initial trigger input (status, name, fileName, and any other parameters)
• The parsed session data from the JSON file (for example auth_token)
• The output of the command execution (if any relevant metadata is returned)

The Merge node is configured in combineByPosition mode, which aligns items from each input by index and merges their fields into a single item. This unified item is then used as the source of truth for the GraphQL node, ensuring all necessary variables and headers are available in one place.

GraphQL Integration With AppSync

ListProjects (GraphQL) Node

The core integration point is the ListProjects GraphQL node, which calls an AppSync endpoint with a ListProjects query. Key characteristics of this node include:

• The query requests a comprehensive set of nested fields, such as project squads, client details, tags, and meetings.
• GraphQL variables are constructed dynamically. If status or name are present in the merged input, they are included in the query filter. If they are omitted, the query runs without those constraints.
• The Authorization header is populated using the auth_token extracted from the JSON session file. n8n expressions are used to inject this token into the node configuration at runtime.
• The limit parameter is set to 999 to retrieve a large batch of projects per page. AppSync still returns a nextToken when more results are available, so subsequent pages can be fetched.

Pagination Logic and Control Flow

Conditional Routing Based on nextToken

If1 Node – Pagination Check

After each GraphQL request, the workflow needs to determine whether additional pages exist. This is handled by an If node that inspects the nextToken field in the GraphQL response.

• If nextToken is empty or null, the workflow follows the true branch. This indicates that the current response already contains the final page of results.
• If nextToken is present, the workflow follows the false branch, which prepares a new request cycle for the next page.

Edit Fields (Set Node) for Subsequent Pages

When pagination needs to continue, the Edit Fields (Set) node on the false branch constructs the parameters required for the next GraphQL call. It explicitly sets:

• nextToken – taken from the current GraphQL response
• auth_token – preserved from the initial session data
• status and name – forwarded from the original input to maintain consistent filters across pages

These fields are fed back into the ListProjects node, allowing the workflow to request the next page of results using the same filter criteria and authentication context.

Aggregation of Paginated Results

Code Node – Pagination Collector

The final step in the pagination process is to consolidate all pages into a single coherent output. A Code node is used to:

• Iterate over the outputs of both branches of the If node across multiple executions
• Collect each project item from every GraphQL response
• Stop once no nextToken is present on any branch, indicating that all pages have been retrieved

The Code node effectively implements a do/while-style loop over the paginated responses. It assumes that n8n is configured to provide branch outputs as separate streams, which can be indexed by page index. The final output of this node is a unified array of project objects that can be consumed by downstream workflows.

How AppSync Pagination Is Implemented

AppSync uses a nextToken mechanism for pagination. The template encapsulates this pattern as follows:

• Each GraphQL request includes a nextToken variable when one is available from the previous response.
• The If node evaluates the presence of nextToken after each call.
• The Code node walks through the results of each iteration, aggregating items and checking for termination conditions.

This architecture gives you clear separation between the responsibilities of querying, branching, and aggregation, which is beneficial when you need to debug or extend the workflow.

Security, Reliability, and Best Practices

Security Considerations

• Avoid storing long-lived secrets on disk. Temporary files should be used only for short-lived session data and must be deleted promptly, as this template does using the Execute Command node.
• Always sanitize and validate filenames before passing them to shell commands. This reduces the risk of command injection and unintended file operations.
• Limit the exposure of sensitive tokens in logs, error messages, and node output where possible.

Reliability and Rate Limiting

• GraphQL or AppSync endpoints may enforce rate limits. Even with a high limit value such as 999, you may need to introduce delays or backoff strategies to avoid throttling.
• Use the built-in retry configuration on the GraphQL or HTTP Request node to automatically handle transient errors such as 5xx or 429 responses.
• For critical automations, consider adding global error handling via an Error Trigger workflow to capture and respond to failures consistently.

Recommended Enhancements and Customization Options

Retry and Backoff Strategies

For production workloads, you should wrap the GraphQL call with a resilient error handling pattern. Options include:

• Configuring node-level retries with exponential backoff for network or rate limit errors
• Using an Error Trigger to route failed executions to a remediation workflow

Refining the Pagination Pattern

If you prefer a different approach to pagination, you can:

• Implement an explicit loop using the n8n “Execute Workflow” pattern where each iteration calls the same workflow until no nextToken is returned.
• Use a webhook or function-based iterative loop that performs repeated HTTP requests from a single Code node.
• Extract the pagination logic into a dedicated Function/Code node that is responsible solely for iterating until nextToken is absent.

More Secure Credential Management

While the template demonstrates reading an auth_token from disk, a more secure and maintainable approach is to use n8n Credentials or an OAuth2 integration if your GraphQL backend supports it. This provides:

• Centralized management of tokens and secrets
• Reduced reliance on temporary files
• Better alignment with security and compliance requirements

Optimizing Response Size and Performance

The reference query requests many nested fields. In performance-sensitive environments, you should:

• Request only the fields that are strictly required by downstream systems
• Reduce payload size to lower bandwidth usage and improve response times
• Consider lowering the limit if your endpoint is sensitive to large result sets

Troubleshooting and Diagnostics

• Empty results: Verify that the Authorization header is correctly set and that the token is valid and not expired.
• Infinite or unexpected pagination: Temporarily log or inspect nextToken values to ensure the API is returning valid tokens and that the Code node logic is correctly detecting termination.
• File deletion issues: If the Execute Command node fails, check file paths, permissions, and the working directory used by the node.
• General debugging: Use n8n’s Execution view to inspect node outputs step by step. This is particularly useful for catching misconfigured expressions, missing fields, or incorrect merge behavior.

When This Template Is a Good Fit

Consider using this n8n GraphQL Projects template when you need to:

• Retrieve large lists of projects from an AppSync or similar GraphQL endpoint
• Combine parameters from a triggering workflow with session data stored in a temporary file
• Handle GraphQL pagination and return a single, merged collection of project records

Pre-Production Checklist

1. Move authentication tokens and secrets into n8n Credentials or an external secret manager instead of disk-based session files.
2. Validate and sanitize any filenames passed into the Execute Command node, and confirm that file paths are correct.
3. Review and adjust the GraphQL query, including requested fields and limit, to meet your performance and data requirements.
4. Implement an appropriate error handling and retry strategy to address rate limiting and transient network issues.

Conclusion

This n8n GraphQL Projects template provides a robust foundation for querying AppSync-based project data with support for pagination, multi-source inputs, and secure cleanup of temporary artifacts. By refining credential management, tightening security controls around file handling, and tailoring the GraphQL query to your exact data needs, you can confidently run this pattern in production environments.

Call to action: Import this template into your n8n instance, connect it to a non-production AppSync endpoint, and adapt the query filters and fields to match your project model. Once validated, integrate it as a reusable component in your broader automation architecture.

Reverse Engineer Short-Form Videos with n8n & AI

Short-form videos on Instagram Reels and TikTok are full of subtle creative decisions: framing, pacing, camera motion, lighting, and visual style. This guide shows you how to use an n8n workflow template to automatically download a Reel or TikTok, convert it to a base64 payload, send it to Google Gemini for detailed forensic analysis, and receive a structured “Generative Manifest” in Slack that you can reuse for AI video generation.

What you will learn

By the end of this tutorial-style walkthrough, you will be able to:

• Explain why automating video reverse engineering is useful for content and AI workflows
• Understand each part of the n8n workflow and how the nodes connect
• Configure the Instagram and TikTok scraping branches using Apify
• Send an MP4 video to Google Gemini as base64 and retrieve a “Generative Manifest”
• Deliver the AI-generated manifest to Slack as a reusable prompt file
• Design and improve the analysis prompt for more accurate, production-ready outputs

Why automate reverse engineering of short-form videos?

Manually breaking down a short-form video is slow and inconsistent. You have to watch frame by frame, guess lens choices, estimate timing, and describe motion in words. This is difficult to repeat across many videos or share with a team.

By automating the process with n8n and AI, you can:

• Save time by letting a workflow handle downloading, converting, and analyzing videos
• Produce consistent, structured descriptions every time
• Generate highly detailed prompts that can be fed directly into generative video models
• Create a repeatable pipeline for reverse engineering and recreating short-form content

High-level overview of the workflow

Here is what the n8n workflow template does from start to finish:

• Collects a user-submitted Instagram Reel or TikTok URL from a form
• Detects which platform the URL belongs to and routes it to the correct scraper
• Uses Apify acts to extract the video asset and metadata
• Downloads the MP4 file and converts it to a Base64-encoded string
• Sends the encoded video plus a structured reverse engineering prompt to Google Gemini
• Receives a detailed “Generative Manifest” from Gemini
• Converts the manifest text into a file and posts it to Slack for review and reuse

Tools and services used in the template

• n8n – visual automation platform that orchestrates triggers, conditions, and HTTP requests
• Apify acts – scrapers for Instagram Reels and TikTok that return video URLs and metadata
• Google Gemini – generative language model used to analyze the video and output the Generative Manifest
• Slack – messaging platform used to deliver the resulting prompt file to your team

Prerequisites before you start

To run this workflow template successfully, you will need:

1. An n8n instance, either self-hosted or n8n cloud
2. An Apify API key with access to Instagram and TikTok scraping acts
3. A Google Cloud API key with access to Gemini, or equivalent generative model credentials
4. Slack OAuth app credentials with permission to post messages and upload files

How the n8n workflow is structured

The workflow is organized into three main stages:

1. Input and platform routing
2. Platform-specific processing (Instagram branch and TikTok branch)
3. Delivery of the Generative Manifest to Slack

Let us walk through each stage step by step.

Stage 1 – Input and routing

Nodes involved: form_trigger → set_base_prompt → switch_platform

1. Form input

The form_trigger node is where the workflow begins. It exposes a simple form with one required field:

• A single URL input where the user pastes either an Instagram Reel link or a TikTok link

Once the user submits the form, the URL is passed into the rest of the workflow.

2. Base analysis prompt

The set_base_prompt node prepares the core instructions that will be sent to Google Gemini later. It:

• Defines a detailed “Digital Twin Architect” persona that acts as a forensic analyst
• Instructs the model to be precise, non-interpretive, and deterministic
• Sets the structure for the Generative Manifest, such as sections for shots, camera, lighting, and physics

This base prompt is later combined with platform-specific metadata to give Gemini more context.

3. Platform detection and routing

The switch_platform node inspects the submitted URL and decides which branch of the workflow to run. It typically uses simple checks like:

• If the URL contains instagram.com then route to the Instagram Reel branch
• If the URL contains tiktok.com then route to the TikTok branch

From here, only the relevant branch is executed for that URL.

Stage 2 – Platform-specific branches

The workflow has two parallel branches, one for Instagram Reels and one for TikTok. Both follow the same logical pattern:

1. Scrape metadata and video URL
2. Build a final prompt that includes metadata
3. Download the MP4 video
4. Convert the video to base64
5. Send the video and prompt to Gemini
6. Extract and store the AI result

2.1 Instagram Reel branch

Nodes involved: scrape_ig_reel → build_final_ig_prompt → download_ig_reel → ig_to_base64 → analyze_ig_reel → set_ig_result

Step 1 – Scrape the Instagram Reel

The scrape_ig_reel node calls an Apify act dedicated to Instagram. This node:

• Uses the Reel URL to fetch post-level data
• Retrieves the direct videoUrl for the MP4 file
• Collects metadata such as caption, hashtags, and possibly a title

Step 2 – Build the final prompt for Instagram

The build_final_ig_prompt node merges two sources of information:

• The generic base prompt from set_base_prompt
• Instagram-specific metadata returned by the scraper (caption, tags, etc.)

The result is a tailored prompt that gives Gemini both the forensic instructions and context about the content, which helps it interpret timing and on-screen text more accurately.

Step 3 – Download the Reel video

The download_ig_reel node uses the scraped videoUrl to download the Reel as an MP4 file. The binary data is stored in n8n so it can be transformed in the next step.

Step 4 – Convert MP4 to base64

The ig_to_base64 node converts the MP4 binary into a Base64-encoded string and stores it in a JSON property. This step is important because:

• Google Gemini expects inline media data in base64 format
• The workflow needs to pass the video payload directly inside the API request

Step 5 – Analyze the Reel with Google Gemini

The analyze_ig_reel node sends a request to the Gemini API that includes:

• The full text prompt created in build_final_ig_prompt
• The base64-encoded video data with the correct MIME type, such as video/mp4

Gemini responds with a detailed Generative Manifest that describes the video shot by shot, including camera behavior, timing, and other requested details.

Step 6 – Store the Instagram analysis result

The set_ig_result node parses Gemini’s response and extracts the main textual result. Typically this is the model’s first candidate output. It then stores this text in a property named result, which will be used later when sending content to Slack.

2.2 TikTok branch

Nodes involved: scrape_tiktok → build_final_tiktok_prompt → download_tiktok → tiktok_to_base64 → analyze_tiktok → set_tiktok_result

The TikTok branch mirrors the Instagram branch, but uses TikTok-specific scrapers and field names.

Step 1 – Scrape the TikTok video

The scrape_tiktok node calls an Apify act designed for TikTok. It:

• Uses the TikTok URL to fetch video metadata
• Returns a direct video URL for the MP4 file
• Provides TikTok-specific fields like description, tags, or sound information (depending on the act configuration)

Step 2 – Build the final TikTok prompt

The build_final_tiktok_prompt node combines:

• The shared base prompt from set_base_prompt
• TikTok metadata from the scraper’s response

This gives Gemini a similar level of context as in the Instagram branch, but tailored to TikTok’s structure and content style.

Step 3 – Download the TikTok video

The download_tiktok node retrieves the MP4 file from the scraped video URL and stores the binary data in n8n.

Step 4 – Convert MP4 to base64

The tiktok_to_base64 node encodes the binary video data into a base64 string inside a JSON field, preparing it for the Gemini API request.

Step 5 – Analyze the TikTok video with Gemini

The analyze_tiktok node sends the TikTok-specific prompt and base64 video payload to Google Gemini, again specifying video/mp4 as the MIME type. Gemini returns a TikTok-focused Generative Manifest.

Step 6 – Store the TikTok analysis result

The set_tiktok_result node extracts the main text candidate from the Gemini response and stores it in a result property, aligning with the Instagram branch so that the downstream Slack delivery can handle either platform uniformly.

Stage 3 – Delivering the Generative Manifest to Slack

Nodes involved: Convert to File → upload_file → send_prompt_msg

1. Convert text result to a file

Once either branch has produced a result property, the Convert to File node turns this text into a downloadable file. For example, it might create a file named prompt.md or manifest.txt. This makes it easier for collaborators to store, edit, or import the prompt into other tools.

2. Upload the file to Slack

The upload_file node uses the Slack file upload API to send the generated prompt file to a chosen Slack channel. This requires that your Slack app has the correct OAuth scopes, such as files:write, and permission to post in that channel.

3. Post a message with a link to the manifest

The send_prompt_msg node posts a short Slack message that:

• Mentions that a new Generative Manifest is available
• Includes a link or reference to the uploaded file

This way, teammates can quickly open the file, review the analysis, and copy the prompt into their generative video tools.

Designing an effective Generative Manifest prompt

The quality of the Generative Manifest that Gemini returns is heavily influenced by how you design the base prompt in set_base_prompt. In this template, the persona “Digital Twin Architect” is used to enforce a forensic, deterministic style and avoid vague or interpretive descriptions.

Key sections you should include in your prompt design:

• Global aesthetic and camera emulation
  Describe the overall look and feel of the video, such as:
  • Camera sensor or film stock being emulated
  • White balance and color temperature
  • Grain level, contrast, and saturation
• Per-shot breakdowns
  Ask for a shot-by-shot analysis that includes:
  • Exact time offsets and durations
  • Framing and composition details
  • Camera movement curves and speed
  • Focus pulls and depth of field changes
  • On-screen micro-actions tied to precise timestamps
• Lighting blueprint and environment
  Request a detailed lighting plan that covers:
  • Light temperature and color
  • Key light size, distance, and angle
  • Practical light sources in the scene
  • Atmospherics like dust or particles and how they catch light
• Physics and simulation constraints
  For generative video tools that support physics, include:
  • Cloth behavior and how it reacts to motion or wind
  • Fluid dynamics, splashes, or smoke movement
  • Particle density and distribution
  • How these elements interact with light and objects

Tips to improve analysis accuracy

To get more reliable and detailed Generative Manifests, consider these best practices:

• Use high quality source videos
  Provide the highest bitrate version you can. Overly compressed MP4s make it harder for Gemini to detect subtle motion, grain, and fine texture.
• Include rich metadata in the prompt
  Feed extra context like the uploader handle, caption text, visible titles, and on-screen overlays into the prompt. This helps the model align visual events with meaning and timing.
• Adjust model settings for precision
  If your Gemini configuration allows it, lower the temperature and limit candidate diversity to favor precise, rule-based outputs instead of creative variations.
• Split long videos into segments
  For longer content, cut the video into shot segments before analysis. Running the workflow on shorter segments often yields more granular, accurate manifests.

Security, privacy, and legal considerations

Before you automate video analysis, keep these points in mind:

• Ensure you have legal rights to download and analyze