Turn your Twitch streams into ready-to-use highlight scripts with a fully automated n8n workflow. In this guide you will learn how to:
Accept Twitch clip transcripts via a webhook
Split long transcripts into smaller chunks for better processing
Create semantic embeddings with Cohere
Store and search those embeddings in Weaviate
Use a Hugging Face chat model and n8n Agent to write highlight scripts
Log results to Google Sheets for review and reuse
By the end, you will have a working Twitch Clip Highlights Script workflow that connects n8n, Cohere, Weaviate, Hugging Face, and Google Sheets into a single automated pipeline.
Why automate Twitch clip highlights?
Reviewing clips manually is slow and inconsistent. Automated highlight generation helps you:
Find your best moments faster, without scrubbing through hours of VODs
Repurpose Twitch content for YouTube Shorts, TikTok, Instagram, and more
Maintain a consistent style and structure for highlight reels
Build a searchable archive of your stream’s key moments
This workflow uses:
n8n for orchestrating the entire process
Cohere embeddings to convert text into semantic vectors
Weaviate as a vector database for fast similarity search
Hugging Face chat model to generate human-readable highlight scripts
Google Sheets for simple logging and review
How the n8n Twitch highlights workflow works
Before we build it step by step, here is the high-level flow of the n8n template:
Webhook receives clip data and transcript.
Text Splitter breaks long transcripts into overlapping chunks.
Cohere Embeddings converts each chunk into a vector.
Weaviate Insert stores vectors plus metadata in a vector index.
Weaviate Query retrieves the most relevant chunks for a highlight request.
Tool + Agent passes those chunks to a Hugging Face Chat model.
Agent produces a concise, readable highlight script.
Google Sheets logs the script, metadata, and timestamps for later use.
Next, we will walk through each part of this workflow in n8n and configure it step by step.
Step-by-step: Build the Twitch Clip Highlights Script workflow in n8n
Step 1 – Create the Webhook endpoint
The Webhook node is your workflow’s entry point. It receives clip data from your clip exporter or transcription service.
In n8n, add a Webhook node.
Set the HTTP Method to POST.
Set the Path to twitch_clip_highlights_script.
This endpoint should receive JSON payloads that include at least:
clip_id – unique ID of the clip
streamer – streamer or channel name
timestamp – when the clip occurred
transcript – full transcript text of the clip
You can adapt the field names later in your node mappings, as long as the structure is consistent.
Example webhook payload
{ "clip_id": "abc123", "streamer": "GamerXYZ", "timestamp": "2025-09-28T20:45:00Z", "transcript": "This is the full transcript of the clip..."
}
Use this sample payload to test your Webhook node while you build the rest of the workflow.
Step 2 – Split long transcripts into chunks
Long transcripts are harder to embed and can exceed token limits for language models. Splitting them into overlapping chunks improves both embedding quality and downstream summarization.
Add a Text Splitter node after the Webhook.
Set the Chunk Size to something like 400 characters.
Set the Chunk Overlap to around 40 characters.
These values are a good starting point for spoken transcripts. The overlap keeps context flowing between chunks so that important details are not lost at chunk boundaries.
Tip: For most Twitch clips, 300-500 characters per chunk with a small overlap works well. If you notice that the model misses context, try increasing the overlap slightly.
Step 3 – Generate embeddings with Cohere
Next, you will turn each transcript chunk into a numeric vector using Cohere embeddings. These vectors capture semantic meaning and are what Weaviate will use for similarity search.
In n8n, configure your Cohere credentials under Settings > API credentials.
Add an Embeddings node after the Text Splitter.
Select Cohere as the provider.
Choose a stable model. The template uses the default model.
Map the chunk text from the Text Splitter as the input to the Embeddings node.
The Embeddings node will output a numeric vector for each chunk. You will store these vectors, along with metadata, in Weaviate.
Best practice: When processing many clips, batch embedding requests to reduce API calls and cost. n8n can help you group items and send them in batches.
Step 4 – Store vectors and metadata in Weaviate
Weaviate is your vector database. It stores both the embeddings and important metadata so you can later search for relevant moments and still know which clip and timestamp they came from.
Add a Weaviate Insert node after the Embeddings node.
Set indexName (or class name) to twitch_clip_highlights_script.
Map the embedding vector output from the Embeddings node.
Include metadata fields such as:
clip_id
streamer
timestamp
A short text excerpt or full chunk text
Optional: source URL or VOD link
Persisting metadata is crucial. With clip_id, streamer, timestamp, and source URL stored, you can:
Quickly retrieve the exact segment you need
Deduplicate clips
Filter results by streamer, date, tags, or language
Vector store tuning tip: Configure Weaviate with an appropriate similarity metric such as cosine similarity or dot product, and consider adding filters (for example tags, language, or streamer) to narrow down search results when querying.
Step 5 – Query Weaviate for highlight-worthy chunks
Once you have clips stored, you need a way to pull back the most relevant moments when you want to generate highlight scripts. This is where the Weaviate Query node comes in.
Add a Weaviate Query node to your workflow for the retrieval phase.
Provide a short query prompt or natural language question, such as:
“Find the funniest moments from yesterday’s stream”
“Moments where the streamer wins a match”
Configure the node to return the top N matching chunks based on semantic similarity.
The Query node will return a ranked list of candidate chunks that best match your request. These chunks will be passed into the language model to create a coherent highlight script.
Step 6 – Use a Tool + Agent with a Hugging Face chat model
Now you have the right chunks, you need to turn them into a readable highlight script. n8n’s Tool and Agent pattern connects Weaviate results with a chat model from Hugging Face.
Add a Chat node and select a Hugging Face chat model.
Configure your Hugging Face API key in n8n credentials.
Connect the Weaviate Query node as a tool that the Agent can call to retrieve relevant chunks.
Add an Agent node:
Use the Chat node as the underlying model.
Design a prompt template that explains how to use the retrieved chunks to produce a highlight script.
Example agent prompt template
"Given the following transcript chunks, identify the top 3 moments suitable for a 30-60s highlight. For each moment provide: 1) Start/end timestamp 2) One-sentence summary 3) Two short lines that can be used as narration."
The Agent node will:
Assemble the final prompt using your template and the retrieved chunks
Call the Hugging Face chat model
Return a structured, human-friendly highlight description
You can optionally add a Memory node to keep buffer memory, which allows the Agent to maintain context across multiple turns or related highlight requests.
Step 7 – Log generated scripts to Google Sheets
To track your highlights and review them later, log every generated script to a Google Sheet.
Add a Google Sheets node after the Agent.
Set the operation to Append.
Map fields such as:
Stream or streamer name
Clip ID or list of clip IDs used
The generated highlight script
Summary tags or keywords
Generation timestamp
Link to the original clip or VOD
This sheet becomes your simple dashboard for:
Quality review before publishing
Tracking which clips have already been used
Handing off scripts to editors or social media tools
Best practices for a reliable Twitch highlights pipeline
1. Choose sensible chunk sizes
Start with 300-500 characters per chunk.
Use a small overlap (for example 40 characters) to preserve context.
Increase overlap if the model seems to miss setup or punchlines that span chunk boundaries.
2. Store rich metadata in Weaviate
Always include:
clip_id
streamer
timestamp
Source URL or VOD link
This makes later filtering, deduplication, and manual review much easier.
3. Tune vector search and performance
Select a similarity metric like cosine or dot product that fits your Weaviate setup.
Store additional fields like language, tags, or game so you can filter queries.
Batch embedding calls to Cohere to reduce API costs.
4. Monitor rate limits and costs
Track usage for both Cohere and Hugging Face APIs.
Use smaller, cheaper models for routine summarization.
Reserve larger models for final polished scripts or special highlight reels.
5. Respect privacy and content rights
Only process clips you have permission to use.
Follow Twitch and platform policies when storing and distributing content.
Consider adding a moderation step for sensitive or inappropriate content.
Testing and validating your n8n workflow
Before you rely on this workflow for production, validate each part.
Test the Webhook Send a single small payload (like the sample above) and watch the execution in n8n. Confirm that all nodes receive the expected data.
Check embeddings in Weaviate After inserting vectors, run a few manual queries in Weaviate and verify that:
Embeddings are stored correctly
Metadata fields are present and accurate
Retrieved chunks are semantically relevant to your queries
Review Agent outputs Inspect the Agent node’s output before auto-posting anywhere. If the scripts are not in your desired voice:
Refine the prompt template
Add examples of good highlight scripts
Adjust the number of chunks or context length
Troubleshooting common issues
Embeddings do not appear in Weaviate Check:
Weaviate credentials in n8n
Field mapping in the Insert node
That the embedding vector is correctly passed from the Embeddings node
Poor quality highlight scripts Try:
Adding more context or more top chunks from Weaviate
Increasing the token window for the chat model
Refining the Agent prompt with clearer instructions and examples
Empty or malformed webhook payloads This often comes from a misconfigured clip exporter. Add a temporary Google Sheets or logging node right after the Webhook to capture raw payloads and see what is actually arriving.
Scaling the workflow for multiple streamers
Once the basic pipeline works, you can extend it to handle more channels and more volume.
Multi-tenant indexing – Use a namespace or separate index per streamer in Weaviate.
API key management – Rotate Cohere and Hugging Face keys if you approach quotas.
Moderation step – Insert a moderation or classification node to flag sensitive content before generating or publishing scripts.
Downstream automation – Connect the generated scripts to:
Social platforms (YouTube, TikTok, Instagram)
Video editing APIs or tools that create short-form edits
Content management systems or scheduling tools
FAQ and quick recap
What does this n8n template automate?
It automates the flow from raw Twitch clip transcript to a ready-to-use highlight script. It handles ingestion, splitting, embedding, semantic search, script generation, and logging.
On a Tuesday night, somewhere between a clutch win in Valorant and a chaotic chat spam, Mia realized she had a problem.
Her Twitch channel was finally growing. She streamed four nights a week, her community was engaged, and clips were piling up. But every time she wanted to post a highlight reel on social, she lost hours scrolling through clips, rewatching moments, and trying to write catchy captions from memory.
By the time she finished, she was too exhausted to edit the next video. The content was there, but the workflow was broken.
That was the night she stumbled onto an n8n workflow template that promised something almost unbelievable: an automated Twitch clip highlights script powered by n8n, LangChain tools, Weaviate vector search, and an LLM that could actually write summaries and captions for her.
The pain of manual Twitch highlights
Mia’s problem was not unique. Like many streamers and content creators, she produced hours of content every week. The real struggle was not recording the content, it was turning that content into something reusable, searchable, and shareable.
Every week she faced the same issues:
Digging through dozens of Twitch clips to find memorable moments
Trying to remember timestamps and context from long streams
Manually writing short highlight scripts and social media captions
Keeping track of which clips had already been used, and which were still untouched
She knew that if she could automate even part of this process, she could post more consistently, experiment with new formats, and spend more time streaming instead of sorting through clips.
That is when she decided to build a Twitch clip highlights script workflow with n8n.
Discovering an automated highlight workflow
While searching for “n8n Twitch highlights automation,” Mia found a workflow template that looked almost like a map of her ideal system. The diagram showed a clear path:
Webhook → Text splitter → Embeddings → Vector store (Weaviate) → Agent / Chat LLM → Google Sheets log
Instead of Mia doing everything manually, each node in the n8n workflow would take over a piece of the job:
A webhook to receive clip data and transcripts
A text splitter to break long transcripts into chunks
Embeddings with Cohere to convert text into vectors
Weaviate as a vector store to make clips searchable
A query tool to find the most relevant chunks for a highlight
Memory and a chat LLM to generate highlight scripts and summaries
An agent to orchestrate tools and log results to Google Sheets
The idea was simple but powerful. Instead of Mia hunting for clips and writing everything herself, she would ask the system for something like “best hype moments this week” and let the workflow handle the heavy lifting.
Setting the stage in n8n
Mia opened n8n, imported the template, and started customizing it. The workflow was modular, so she could see exactly how each part connected. But to bring it to life, she had to walk through each step and wire it to her own Twitch clips.
1. The webhook that listens for new clips
The first scene in her new automation story was a webhook node.
She configured an n8n Webhook node with a path like:
/twitch_clip_highlights_script
This webhook would receive POST requests whenever a new clip was ready. The payload would include:
Clip ID
Clip URL
Timestamp or time range
Transcript text (from a separate transcription service)
Her clip ingestion system was set to send JSON data to this endpoint. Now, every time a clip was created and transcribed, n8n would quietly catch it in the background.
2. Splitting long transcripts into meaningful chunks
Some clips were short jokes, others captured multi-minute clutch plays with commentary. To make this text usable for semantic search, Mia needed to break it into smaller, overlapping chunks without losing context.
She added a Character Text Splitter node and used the recommended settings from the template:
Chunk size: 400 characters
Chunk overlap: 40 characters
This way, each chunk was long enough to understand the moment, but small enough for the embedding model to stay focused. The overlap helped preserve continuity between chunks so important phrases were not cut in awkward places.
3. Giving the clips a semantic fingerprint with Cohere embeddings
Next, Mia connected those chunks to a Cohere Embeddings node. This was where the text turned into something the vector database could search efficiently.
She selected a production-ready Cohere model, set up her API key in n8n credentials, and made sure each transcript chunk was sent to Cohere for embedding. Each chunk returned as a vector, a numeric representation of its meaning.
With embeddings in place, her future queries like “funny chat interactions” or “intense late-game plays” would actually make sense to the system.
4. Storing everything in Weaviate for later discovery
Now that each chunk had an embedding, Mia needed a place to store and search them. That is where Weaviate came in.
She added an Insert (Weaviate) node and created an index, for example:
twitch_clip_highlights_script
For each chunk, she stored:
Clip ID
Timestamp
Original text chunk
Clip URL
The generated embedding vector
This meant that any search result could always be traced back to the specific clip and moment where it came from. No more losing track of which highlight belonged to which VOD.
The turning point: asking the system for highlights
With the pipeline set up to ingest and store clips, Mia reached the real test. Could the workflow actually help her generate highlight scripts on demand?
5. Querying Weaviate for the best moments
She added a Query + Tool step that would talk to Weaviate. When she wanted to create a highlight reel, she would define a query like:
“Best hype moments from last night’s stream”
“Funny chat interactions”
“Clutch plays in the last 30 minutes”
The query node asked Weaviate for the top matching chunks, returning the most relevant segments ranked by semantic similarity. These chunks, along with their metadata, were then passed along to the agent and the LLM.
Instead of scrubbing through hours of footage, Mia could now ask a question and get back the most relevant transcript snippets in seconds.
6. Letting an agent and chat LLM write the script
The final piece was the storytelling engine: a combination of an Agent node and a Chat LLM.
In the template, the LLM was a Hugging Face chat model. Mia could swap in any compatible model she had access to, but the structure stayed the same. The agent was configured to:
Receive the highlight query, retrieved chunks, and clip metadata
Use the vector store tool to pull context as needed
Follow a clear prompt that requested a concise highlight script or caption
Return structured output with fields she could log and reuse
To keep the results predictable, she used a system prompt similar to this:
System: You are a Twitch highlights assistant. Given transcript chunks and clip metadata, return a JSON with title, short_summary (1-3 sentences), highlight_lines (3 lines max), key_moments (timestamps), and tags.
User: Here are the top transcript chunks: [chunks]. Clip URL: [url]. Clip timestamp: [timestamp]. Generate a highlight script and tags for social sharing.
The agent then produced a neat JSON object that looked something like:
title – a catchy headline for the moment
short_summary – 1 to 3 sentences summarizing the clip
highlight_lines – 3 lines of script or caption-ready text
key_moments – timestamps inside the clip
tags – keywords for search and social platforms
For the first time, Mia watched as her raw Twitch transcript turned into something that looked like a ready-to-post highlight script.
From chaos to organized content: logging in Google Sheets
Before this workflow, Mia’s clip notes were scattered across sticky notes, Discord messages, and half-finished spreadsheets. Now, every generated highlight flowed into a single organized log.
The final node in the workflow was a Google Sheets integration. After the agent produced the JSON result, n8n appended it as a new row in a sheet that contained:
Title
Clip URL
Timestamp or key moments
Short summary
Highlight lines
Tags
This sheet became her content brain. She could filter by tags like “funny,” “clutch,” or “community,” sort by date, and quickly assemble highlight compilations or social calendars.
And because the workflow was modular, she knew she could extend it later to:
Trigger a short video montage generator using timestamps
Auto-post captions to social platforms via their APIs
Send clips and scripts to an editor or Discord channel for review
Keeping the workflow reliable: best practices Mia followed
As the workflow started to prove itself, Mia wanted to make sure it would scale and stay safe. She adopted a few best practices built into the template’s guidance.
Securing credentials She stored API keys and secrets in n8n credentials, not in plain text, and restricted exposed endpoints. Where possible, she used OAuth or scoped keys.
Monitoring costs Since embeddings and LLM calls can add up, she monitored usage, batched jobs when testing large sets of clips, and tuned how often queries were run.
Adjusting chunk sizes For fast, dense dialogue, she experimented with slightly smaller chunk sizes and overlap to see what produced the most faithful summaries.
Persisting rich metadata She made sure clip IDs, original transcripts, and context like game title or chat snippets were stored along with vectors. That way, she could always reconstruct the full story behind each highlight.
Rate limiting webhook traffic To avoid sudden bursts overloading her pipeline, she applied rate limiting on webhook consumers when importing large historical clip batches.
Testing the workflow before going all in
Before trusting the system with her entire catalog, Mia started small. She fed a handful of clips into the pipeline and reviewed the results manually.
She checked:
Relevance – Did the retrieved chunks actually match the query, like “best hype moments” or “funny chat interactions”?
Context – Did the summaries respect the original timestamps and tone of the clip?
Shareability – Were the highlight scripts short, punchy, and ready for social posts?
When something felt off, she tweaked the workflow. That led her to a few common fixes.
How she handled common issues
Low-quality or vague summaries
When some early summaries felt generic, Mia tightened the prompt, increased the number of retrieved chunks, and tried a higher-capacity LLM model. She also leaned on a more structured prompt format to keep the output consistent.
Missing context in highlights
In clips where the humor depended heavily on chat or game situation, she noticed the LLM sometimes missed the joke. To fix this, she stored richer metadata with each vector, such as speaker labels, game titles, or relevant chat snippets. That extra context helped the agent produce more accurate summaries.
Staying compliant with user content
As her workflow grew, Mia kept an eye on platform rules and privacy. She made sure not to store personally identifiable information without permission and restricted access to her Google Sheets log. Only trusted collaborators could view or edit the data.
This kept her automation aligned with Twitch guidelines and good data hygiene practices.
Where Mia took it next
Once the core pipeline was stable, Mia started thinking bigger. The template she had used suggested several extensions, and she began experimenting with them:
Multi-language highlights for her growing non-English audience
Automated clip categorization into labels like “reaction,” “play,” or “funny,” using classifier models
Auto-generated thumbnails and social media images to match each highlight
A small dashboard where she could review, approve, and schedule highlights for publishing
Her Twitch channel had not magically doubled overnight, but her consistency did. She spent less time hunting for moments and more time creating them.
What this n8n Twitch highlights workflow really gives you
Mia’s story is what happens when you combine n8n, embeddings, a vector store, and an LLM into a single, repeatable pipeline.
The workflow she used follows a simple pattern:
Webhook → Text splitter → Embeddings → Weaviate → Agent / LLM → Google Sheets
In practice, that means:
Your Twitch clips become searchable by meaning, not just title
Every highlight is logged with title, timestamps, summaries, and tags
You get a reproducible, extensible system you can keep improving
Start your own Twitch highlights story
If you are sitting on hours of VODs and a backlog of clips, you do not need to build this from scratch. The workflow template that helped Mia is available for you to explore and adapt.
Here is how to get started:
Spin up a free n8n instance
Import the Twitch clip highlights workflow template
Connect your Cohere and Weaviate accounts
Point your transcription or clip ingestion system to the webhook
Run a few clips through the pipeline and iterate from there
If you want a guided setup or a custom version tailored to your channel, you can reach out for consulting and a step-by-step walkthrough. Contact us to get help tuning this Twitch clip highlights script to your exact needs.
Your next viral highlight might already be sitting in your VODs. With n8n, you can finally let your workflow catch up to your creativity.
Automated Morning Briefing Email with n8n: Turn RAG + Embeddings into Your Daily Advantage
Every morning, you and your team wake up to a familiar challenge: too much information, not enough clarity. Slack threads, dashboards, tickets, emails, docs – the signal is there, but it is buried in noise. Manually pulling it all together into a focused briefing takes time and energy that you could spend on real work and strategic decisions.
This is where automation can change the game. In this guide, you will walk through a journey from scattered data to a calm, curated Morning Briefing Email, powered by n8n, vector embeddings, Supabase, Cohere, and an Anthropic chat model. You will not just build a workflow. You will create a system that turns raw information into daily momentum.
The workflow uses text splitting, embeddings, a Supabase vector store, a RAG (retrieval-augmented generation) agent, and simple alerting and logging. The result is a reliable, context-aware morning briefing that lands in your inbox automatically, so you can start the day aligned, informed, and ready to act.
From information overload to focused mornings
Before diving into nodes and configuration, it is worth pausing on what you are really building: a repeatable way to free your brain from manual status gathering. Instead of chasing updates, you receive a short, actionable summary that highlights what truly matters.
By investing a bit of time in this n8n workflow, you create a reusable asset that:
Saves you from daily copy-paste and manual summarization
Aligns your team around the same priorities every morning
Scales as your data sources and responsibilities grow
Becomes a foundation you can extend to other automations
Think of this Morning Briefing Email as your first step toward a more automated workday. Once you see how much time one workflow can save, it becomes easier to imagine a whole ecosystem of automations doing the heavy lifting for you.
Why this n8n architecture sets you up for success
There are many ways to send a daily email. This one is different because it is built for accuracy, context, and scale. The architecture combines vector embeddings, a Supabase vector index, and a RAG Agent so your summaries are not just generic AI text, but grounded in your real data.
Here is what this architecture gives you:
Context-aware summaries using Cohere embeddings and a Supabase vector store, so the model pulls in the most relevant pieces of information.
Up-to-date knowledge retrieval via a RAG Agent that blends short-term memory with retrieved documents, rather than relying on a static prompt.
Scalability and performance through text chunking and vector indexing, which keep response times predictable as your data grows.
Operational visibility with Google Sheets logging and Slack alerts, so you can trust this workflow in production and quickly spot issues.
You are not just automating an email. You are adopting a modern AI architecture that you can reuse for many other workflows: internal search, knowledge assistants, support summaries, and more.
The workflow at a glance
Before we go step by step, here is a quick overview of the building blocks you will be wiring together in n8n:
Webhook Trigger – receives the incoming content or dataset you want summarized.
Text Splitter – breaks long content into manageable chunks (chunkSize: 400, chunkOverlap: 40).
Embeddings (Cohere) – converts each chunk into vectors using embed-english-v3.0.
Supabase Insert – stores those vectors in a Supabase index named morning_briefing_email.
Supabase Query + Vector Tool – retrieves the most relevant pieces of context for the RAG Agent.
Window Memory – maintains a short history so the agent can stay consistent across runs if needed.
Chat Model (Anthropic) – generates the final briefing text based on the retrieved context and instructions.
RAG Agent – orchestrates retrieval, memory, and the chat model to produce the email body.
Append Sheet – logs the final output in a Google Sheet tab called Log.
Slack Alert – posts to #alerts when something goes wrong, so you can fix issues quickly.
Each of these pieces is useful on its own. Together, they form a powerful pattern you can replicate for other AI-driven workflows.
Building your Morning Briefing journey in n8n
1. Start with a Webhook Trigger to receive your data
Begin by creating an HTTP POST Webhook node in n8n and name it something like morning-briefing-email. This will be your entry point, where internal APIs, ETL jobs, or even manual tools can send content for summarization.
Once this is in place, you have a stable gateway that any system can use to feed information into your briefing pipeline.
2. Split long content into smart chunks
Next, add a Text Splitter node. Configure it as a character-based splitter with:
chunkSize: 400
chunkOverlap: 40
This balance is important. Smaller chunks keep embeddings efficient and retrieval precise, while a bit of overlap preserves context across chunk boundaries. You can always tune these numbers later, but this starting point works well for most use cases.
3. Turn text into embeddings with Cohere
Now it is time to give your workflow a semantic understanding of the text. Add an Embeddings node configured to use Cohere and select the embed-english-v3.0 model.
Make sure your Cohere API key is stored securely in n8n credentials, not hard-coded in the workflow. Each chunk from the Text Splitter will be passed to this node, which outputs high-dimensional vectors that capture meaning rather than just keywords.
These embeddings are the foundation of your retrieval step and are what allow the RAG Agent to pull in the most relevant context later.
4. Store vectors in a Supabase index
With embeddings in hand, add a Supabase Insert node to push the vectors into your Supabase vector index. Use an index named morning_briefing_email so you can easily reuse it for this workflow and related automations.
Alongside the vector itself, store useful metadata such as:
Title
Source (for example, which system or document it came from)
Timestamp or date
This metadata helps later when you want to audit how a briefing was generated or trace a specific point back to its origin.
5. Retrieve relevant context with Supabase Query and the Vector Tool
When it is time to actually generate a morning briefing, you will query the same Supabase index for the most relevant chunks. Add a Supabase Query node configured for similarity search against morning_briefing_email.
Wrap this query with a Vector Tool node. The Vector Tool presents the retrieved documents in a format that the RAG Agent can easily consume. This is the bridge between your stored knowledge and the AI model that will write your briefing.
6. Add Window Memory and connect the Anthropic chat model
To give your workflow a sense of continuity, add a Window Memory node. This short-term conversational memory lets the RAG Agent maintain a small history, which can be helpful if you extend this workflow later or chain multiple interactions together.
Then, configure a Chat Model node using an Anthropic-based model. Anthropic models are well suited for instruction-following, which is exactly what you need for clear, concise morning briefings.
At this point, you have all the ingredients: context from Supabase, a memory buffer, and a capable language model ready to write.
7. Orchestrate everything with a RAG Agent
Now comes the heart of the workflow: the RAG Agent. This node coordinates three inputs:
Retrieved documents from Supabase via the Vector Tool
Window Memory history
The Anthropic chat model
Configure the RAG Agent with a clear system prompt that defines the style and structure of your briefing. For example:
System: You are an assistant for Morning Briefing Email. Produce a short, actionable morning briefing (3-5 bullet points), include urgent items, outstanding tasks, and a short quick-glance summary.
This is where your workflow starts to feel truly transformative. Instead of a raw data dump, you get a focused, human-readable summary you can act on immediately.
8. Log every briefing and protect reliability with alerts
To keep a record of what is being sent, add an Append Sheet node and connect it to a Google Sheets document. Use a sheet named Log to store each generated briefing, along with any metadata you find useful. This gives you an audit trail and makes it easy to analyze trends over time.
Finally, add a Slack Alert node that posts to a channel such as #alerts whenever the workflow encounters an error. This simple step is what turns an experiment into a system you can trust. If something breaks, you will know quickly and can respond before your team misses their morning update.
Configuration tips to get the most from your automation
Once the basic pipeline is working, a few targeted tweaks can significantly improve quality and robustness.
Chunk sizing: If your source documents are very long or very short, experiment with different chunkSize and chunkOverlap values. Larger chunks reduce the number of API calls but can blur the boundaries between topics. Smaller chunks increase precision at the cost of more calls.
Rich metadata: Capture fields like source URL, timestamp, and author with each vector. This makes it easier to understand why certain items appeared in the briefing and to trace them back to the original data.
Security best practices: Store all API keys (Cohere, Supabase, Anthropic, Google Sheets) in n8n credentials. Protect your webhook with access controls and request validation, such as an API key or HMAC signature.
Rate limit awareness: Monitor your Cohere and Anthropic usage. For high-volume workloads, batch embedding requests where possible to stay within rate limits and keep costs predictable.
Relevance tuning: Adjust how many nearest neighbors you retrieve from Supabase. Too few and you might miss important context, too many and you introduce noise. Iterating on this is a powerful way to improve briefing quality.
Testing your n8n Morning Briefing workflow
Before you rely on this workflow every morning, take time to test it end to end. Testing is not just about debugging. It is also about learning how the system behaves so you can refine it confidently.
Send a test POST payload to the webhook. For example: { "title": "Daily Ops", "body": "...long content...", "date": "2025-01-01" }
Check your Supabase index and confirm that vectors have been inserted correctly, along with the metadata you expect.
Trigger the RAG Agent and review the generated briefing. If it feels off, adjust the system prompt, tweak retrieval parameters, or fine-tune chunk sizes.
Verify that the Google Sheets Append node logs the output in the Log sheet and simulate an error to ensure the Slack Alert fires in #alerts.
Each test run is an opportunity to learn and improve. Treat this phase as a chance to shape the exact tone and depth you want in your daily emails.
Scaling your Morning Briefing as your needs grow
Once you see how effective this workflow is, you may want to expand it to more teams, more data sources, or more frequent runs. The architecture you have chosen is ready for that.
Separate ingestion from summarization: If live ingestion becomes expensive or complex, move embeddings creation and vector insertion into a scheduled job. Your morning briefing can then query an already up-to-date index.
Use caching for hot data: For information that changes slowly but is requested often, introduce caching to speed up retrieval and reduce load.
Consider specialized vector databases: If you outgrow Supabase in terms of performance or scale, you can migrate to a dedicated vector database such as Pinecone or Milvus, as long as it fits your existing tooling and architecture.
The key is that you do not need to rebuild from scratch. You can evolve this workflow step by step as your organization and ambitions grow.
Troubleshooting: turning issues into improvements
Even well designed workflows hit bumps. When that happens, use these checks to quickly diagnose the problem and turn it into a learning moment.
No vectors in Supabase? Confirm that the Embeddings node is using valid credentials and that the Text Splitter is producing non-empty chunks.
Briefings feel low quality? Refine your system prompt, increase the number of retrieved neighbors, or adjust chunk sizes for better context.
Rate limit errors from Cohere or Anthropic? Implement retry and backoff strategies in n8n and consider batching embedding requests.
n8n workflow failures? Use n8n execution logs together with your Slack Alert node to capture stack traces and pinpoint where things are breaking.
Each fix you apply makes the workflow more resilient and prepares you for building even more ambitious automations in the future.
Prompt ideas to shape your Morning Briefing
Your prompts are where you translate business needs into instructions the model can follow. Here are two examples you can use or adapt:
Prompt (summary): Produce a 3-5 bullet morning briefing with: 1) urgent items, 2) key updates, 3) blockers, and 4) action requests. Use retrieved context and keep it under 150 words.
Prompt (email format): Write an email subject and short body for the team’s morning briefing. Start with a one-line summary, then list 3 bullets with actions and deadlines. Keep tone professional and concise.
Do not hesitate to experiment. Small prompt changes can dramatically shift the clarity and usefulness of your briefings.
From one workflow to a culture of automation
By building this n8n-powered Morning Briefing Email, you have created more than a daily summary. You have built a reusable pattern that combines a vector store, embeddings, memory, and a RAG Agent into a reliable, production-ready pipeline.
The impact is tangible: accurate, context-aware briefings that save time, reduce cognitive load, and keep teams aligned. The deeper impact is mindset. Once you see what a single well designed workflow can do, it becomes natural to ask, “What else can I automate?”
As you move this into production, make sure you:
Protect your webhook with strong authentication and request validation
Monitor usage and costs across Cohere, Supabase, and Anthropic
Maintain a clear error-notification policy using Slack alerts and n8n logs
From here, you can branch out to automated weekly reports, project health summaries, customer support digests, and more, all built on the same RAG + embeddings foundation.
Call to action: Spin up this Morning Briefing workflow in your n8n instance and make tomorrow morning the first where your day starts with clarity, not chaos. If you want a downloadable n8n workflow export or guidance on configuring credentials for Cohere, Supabase, Anthropic, or Google Sheets, reach out to our team or leave a comment below. Use this template as your starting point, then iterate, refine, and keep automating.
n8n If & Switch: A Practical Guide to Smarter, Growth-Focused Automation
From manual decisions to automated clarity
Every growing business eventually hits the same wall: too many tiny decisions, not enough time. You start with simple workflows, then suddenly you are juggling edge cases, exceptions, and “if this, then that” rules scattered across tools and spreadsheets. It gets noisy, and that noise steals focus from the work that really moves you forward.
This is exactly where conditional logic in n8n becomes a turning point. With the If and Switch nodes, you can teach your workflows to make decisions for you. They quietly handle routing, filtering, and branching so you can spend your energy on strategy, creativity, and growth.
In this guide, you will walk through a real n8n workflow template that reads customer records from a datastore and routes them based on country and name. Along the way, you will see how a few well-placed conditions can turn a basic flow into a powerful, reliable automation system.
Adopting an automation mindset
Before diving into the nodes, it helps to shift how you think about automation. Instead of asking “How do I get this one task done?” try asking:
“How can I teach my workflow to decide like I do?”
“Where am I repeating the same judgment calls again and again?”
“Which decisions could a clear rule handle, so my team does not have to?”
The n8n If and Switch nodes are your tools for encoding that judgment. They let you build logic visually, without code, so you can:
Filter out noise and focus only on what matters
Handle different customer types or regions with confidence
Keep workflows readable and maintainable as they grow
Think of this template as a starting point. Once you understand how it works, you can extend it, adapt it to your data, and gradually automate more of the decisions that currently slow you down.
When to use If vs Switch in n8n
Both nodes help you route data, but they shine in different situations:
If node: simple decisions and combined conditions
Use the If node when you want a clear yes/no answer. It is perfect when:
You have a single condition, such as “Is this customer in the US?”
You need to combine a few checks with AND / OR logic, for example:
Country is empty OR
Name contains “Max”
The If node returns two paths: true and false. That simple split is often enough to clean up your flow and make it easier to follow.
Switch node: many outcomes, one clear router
Use the Switch node when you need to handle three or more distinct outcomes. Instead of chaining multiple If nodes, a Switch node lets you define clear rules and send each item to the right branch, such as routing customers by country.
Together, If and Switch let you express complex business logic in a way that stays understandable and scalable, even as your automation grows.
Meet the example workflow template
The n8n template you will use in this guide is built around a simple but powerful scenario: reading customer data and routing records based on country and name. It is small enough to understand quickly, yet realistic enough to reuse in your own projects.
The workflow includes:
Manual Trigger – start the flow manually for testing and experimentation
Customer Datastore – fetches customer records using the getAllPeople operation
If nodes – handle single-condition checks and combined AND / OR logic
Switch node – routes customers into multiple branches by country, with a fallback
Within this single template, you will see three essential patterns that apply to almost any automation:
A single-condition If to filter by country
An If with AND / OR to combine multiple checks
A Switch node to create multiple branches with a safe fallback
Once you grasp these patterns, you can start recognizing similar opportunities in your own workflows and automate them with confidence.
Step 1: Build the foundation of the workflow
Let us start by creating the basic structure. This foundation is where you will plug in your conditions and routing rules.
Add a Manual Trigger node. Use this to run the workflow on demand while you are experimenting and refining your logic.
Add your Customer Datastore node. Set the operation to getAllPeople so the node retrieves all customer records you want to route.
Connect the Datastore to your logic nodes. In n8n you can connect a single node to multiple downstream nodes. Connect the datastore output to:
The If node for the single-condition example
The If node for combined AND / OR logic
The Switch node for multi-branch routing
Prepare to use expressions. You will reference fields like country and name using expressions such as:
={{$json["country"]}}
={{$json["name"]}}
Run and inspect. Click Execute Workflow as you go and inspect the input and output of each node. This habit helps you trust your automations and refine them faster.
With this structure in place, you are ready to add the decision-making logic that will turn this workflow into a smart router for your customer data.
Step 2: Single-condition If – filtering by country
Imagine you want to treat US-based customers differently, for example to send them region-specific notifications or apply US-only business rules. A single If node can handle that routing for you, reliably and automatically.
Configuration for a simple country filter
Set up your If node like this:
Condition type:string
Value 1:={{$json["country"]}}
Value 2:US
With this configuration the If node checks whether $json["country"] equals US.
If the condition is true, the item goes to the true output.
All other items flow to the false output.
How this small step creates leverage
This simple split unlocks a lot of possibilities:
Send US customers into a dedicated notification or marketing sequence
Apply region-specific logic, taxes, or compliance steps only where needed
Route customers into different tools or services based on their country
One clear condition, one If node, and you have turned a manual decision into an automated rule that runs every time, without you.
Step 3: If with AND / OR – combining multiple checks
Real-world data is rarely perfect. You might have missing fields, special cases, or customers who need extra attention. That is where combining conditions in an If node becomes powerful.
In this template you will see an example that handles records where either the country is empty or the name contains “Max”. This could represent incomplete data, test accounts, or VIPs that require special handling.
Key settings for combined conditions
Configure your If node with multiple string conditions, for example:
{{$json["country"]}} isEmpty
{{$json["name"]}} contains "Max"
Then use the Combine field to decide how these conditions interact:
Combine operation:ANY for OR logic
Combine operation:ALL for AND logic
In this template, the configuration uses combineOperation: "any". That means the If node returns true when either condition matches.
If the country is empty, the item matches.
If the name contains “Max”, the item matches.
If both are true, it also matches.
Practical ways to use combined conditions
Once you understand combined conditions, you can start using them to clean data and treat important records differently:
Data validation Route records with missing country values to a cleaning or enrichment step, such as a manual review queue or an external API.
Special handling Flag customers whose name matches certain keywords, such as VIPs, test accounts, or internal users, and route them into dedicated flows.
This is how you gradually build smarter automations: by capturing the small rules you already follow in your head and turning them into reusable, visible logic in n8n.
Step 4: Switch node – routing to multiple branches by country
As your automation grows, you will often have more than two possible outcomes. Maybe you want different flows for the US, Colombia, and the UK, with a safety net for all other countries. A Switch node makes this kind of branching clean and easy to understand.
Example Switch configuration
Configure your Switch node as follows:
Value to check:={{$json["country"]}}
Data type:string
Rules & outputs:
Rule 0:US (routes to output 0)
Rule 1:CO (routes to output 1)
Rule 2:UK (routes to output 2)
Fallback output:3 – catches all records that do not match a rule
Why the fallback output matters
The fallback output is your safety net. It ensures that any unexpected or new country values are still processed. Without it, data could silently disappear from your workflow.
Use the fallback branch to:
Log unknown or new country values for review
Send these records into a manual validation queue
Apply a default, generic flow when no specific rule exists yet
This approach gives you confidence that your automation will behave predictably, even as your data changes or your customer base expands into new regions.
Best practices to keep your automations scalable
As you build more If and Switch logic into your workflows, a few habits will help you stay organized and avoid confusion:
Use Switch for clarity when you have 3+ outcomes. A single Switch node is almost always easier to read than a chain of nested If nodes.
Always include a fallback route in Switch nodes. This protects you from silent data loss and makes your workflow more resilient.
Standardize your data before comparing. If you are unsure about capitalization, use expressions like ={{$json["country"]?.toUpperCase()}} to normalize values before checking them.
Document your logic on the canvas. Use sticky notes or comments in n8n to explain why certain conditions exist. This makes onboarding collaborators faster and helps your future self remember the reasoning.
Use Code nodes for very complex logic. When you have many conditions or intricate rules, consider a Code node, but keep straightforward boolean checks in If nodes to maintain visual clarity.
These small practices compound over time, turning your n8n instance into a clear, maintainable system instead of a tangle of ad hoc rules.
Troubleshooting your conditions with confidence
Even with a strong setup, conditions may not always behave as expected. When that happens, treat it as an opportunity to deepen your understanding of your data and your automation.
If your conditions are not matching, try this checklist:
Inspect Input and Output data. While executing the workflow, open each node and look at the actual JSON values under Input and Output. This often reveals small mistakes immediately.
Check for spaces and case sensitivity. Leading or trailing spaces and inconsistent capitalization can cause mismatches. Use helpers like trim() or toUpperCase() in your expressions when needed.
Verify operators. Make sure you are using:
isEmpty for missing fields
contains for partial matches
Equality operators for exact matches
With a little practice, debugging conditions becomes straightforward, and each fix makes your automation more robust.
Real-world ways to apply If and Switch logic
The patterns in this template show up in many real automation scenarios. Here are a few examples you can adapt directly:
Region-based notifications Send country-specific promotions, legal updates, or compliance messages by routing customers based on their country code.
Data cleanup flows Detect incomplete or suspicious records and route them to manual review, enrichment APIs, or dedicated cleanup pipelines.
Feature toggles and test routing Use name or email patterns to enable or disable parts of a flow for specific users, internal testers, or beta groups.
As you explore this template, keep an eye out for similar patterns in your own processes. Anywhere you are making repeated decisions by hand is a strong candidate for an If or Switch node.
Your next step: experiment, extend, and grow
The If and Switch nodes are not just technical tools. They are building blocks for a more focused, less reactive way of working. Each condition you automate is one less decision you have to make manually, one more piece of mental space you get back.
Use this template as a safe playground:
Open n8n and import the example workflow.
Run it with your own sample customer data.
Adjust the conditions for your real-world rules, such as different countries, name patterns, or validation checks.
Add new branches, new rules, and see how far you can take it.
Start simple, then iterate. Over time, you will build a library of automations that quietly support your business or personal projects, so you can focus on the work that truly matters.
Call to action: turn this template into your own automation engine
If you are ready to move from theory to practice, now is the moment. Open n8n, load this workflow, and begin shaping it around your data and your goals. Treat it as a starting point for a more automated, more intentional way of working.
If you would like a downloadable starter template or guidance on adapting these rules to your dataset, reach out to our team or leave a comment. We are here to help you refine your logic, improve your flows, and build automations you can rely on.
Understand the difference between the If node and the Switch node in n8n
Use conditional logic in n8n to filter and route data without code
Configure a complete country-based routing workflow step by step
Apply AND / OR conditions with the If node
Create multiple branches with the Switch node using a fallback route
Test, debug, and improve your conditional workflows using best practices
This tutorial is based on a real n8n workflow template that routes customers by country. You can follow along and then adapt it to your own data.
Core idea: Conditional logic in n8n
Conditional logic is the backbone of workflow automation. It lets you decide what should happen next based on the data that flows through your n8n nodes.
In n8n, two nodes are central to this kind of decision making:
If node – evaluates one or more conditions and splits items into true or false paths
Switch node – compares a value against multiple possible options and routes items to different outputs
Both are used for conditional logic in n8n, but they shine in different situations. Understanding when to use each is key to clean, maintainable workflow routing and data filtering.
If vs Switch in n8n: When to use which?
The If node
The If node is ideal when you need simple checks, such as:
A yes/no decision, for example “Is this customer in the US?”
A small number of conditions combined with AND or OR logic
Pre-checks before more complex routing, such as skipping invalid records
It has two outputs:
True – items that match your conditions
False – items that do not match
The Switch node
The Switch node is better when you need to route data into more than two branches, for example:
Different countries should be sent to different services
Different statuses (pending, approved, rejected) require different actions
You want a clear visual overview of many possible outcomes
Instead of chaining multiple If nodes, a Switch node lets you define multiple rules in one place and keep the workflow readable.
Quick rule of thumb:
Use If for simple true/false checks or small sets of conditions
Use Switch for multiple distinct routes from the same decision point
Related keywords: n8n If node, n8n Switch node, workflow routing, data filtering, conditional logic in n8n.
Workflow we will build: Country-based routing
To see all this in action, we will walk through a practical example: a workflow that fetches customer records and routes them based on their country field.
The template uses the following nodes:
Manual Trigger – starts the workflow on demand
Customer Datastore (getAllPeople) – returns all customer records
If: Country equals US – filters customers whose country is US
If: Country is empty or Name contains “Max” – demonstrates combining conditions with AND / OR logic
Switch: Country based branching – routes customers to separate branches for US, CO, UK, or a fallback route
Why this example works well for learning
This pattern is very common in automation:
You pull records from a data source
You check specific fields, such as country or name
You route each record to the right process or destination
It shows how to:
Handle missing data (empty country)
Use partial matches (name contains “Max”)
Create multiple routes from one decision point with a fallback
Step 1: Trigger and load your customer data
Manual Trigger
Start with a Manual Trigger node. This lets you run the workflow on demand while you are building and testing it.
Customer Datastore (getAllPeople)
Next, add the Customer Datastore (getAllPeople) node:
Connect it to the Manual Trigger
Configure it so that it returns all customer records
Each item typically includes fields like name and country. These fields are what you will reference in your If and Switch nodes.
Step 2: Use the If node for a single condition
First, you will use the n8n If node to filter customers from a specific country, for example all customers in the United States.
Goal
Route all customers where country = "US" to the true output, and everyone else to the false output.
Configuration steps
Add an If node and connect it to the Customer Datastore node.
Inside the If node, create a new condition.
Set the Type to String.
For Value 1, use an expression that points to the country field:
{{$json["country"]}}
Set Operation to equals (or the equivalent in your UI).
Set Value 2 to:
US
Save the node and keep the two outputs:
True output – all items where country is exactly US
False output – all remaining items
Tip: Use consistent country codes, such as ISO alpha-2 (US, UK, CO), to avoid mismatches between your data and your conditions.
Step 3: Combine conditions with AND / OR in the If node
The If node in n8n supports multiple conditions. You can control how they are evaluated with the Combine field.
Combine options
ALL – acts like a logical AND. Every condition must be true for the item to follow the true path.
ANY – acts like a logical OR. At least one condition must be true for the item to follow the true path.
Example: Country is empty OR Name contains “Max”
In the template, there is an If node that demonstrates this combined logic. It checks two things:
Whether the country field is empty
Whether the name field contains the string Max
To configure this:
Add two string conditions in the If node:
Condition 1:
Value 1:
{{$json["country"]}}
Operation: isEmpty
Condition 2:
Value 1:
{{$json["name"]}}
Operation: contains
Value 2:
Max
Now set Combine to ANY. The result:
Items where country is empty will go to the true output
Items where name contains “Max” will also go to the true output
All other items will go to the false output
This is a powerful pattern for building flexible filters with the If node.
Step 4: Use the Switch node for multiple branches
When you have more than two possible outcomes, multiple If nodes can quickly become hard to follow. This is where the n8n Switch node is more suitable.
Goal
Route customers based on their country value into separate branches for:
US
CO
UK
Any other country or missing value (fallback)
Configuration steps
Add a Switch node and connect it to the node that provides your items (for example the Customer Datastore or a previous If node).
Inside the Switch node, set:
Value 1 to:
{{$json["country"]}}
Data Type to string
Add rules for the countries you care about. For example:
Rule 1:
Value: US
Output: 0
Rule 2:
Value: CO
Output: 1
Rule 3:
Value: UK
Output: 2
Set a Fallback Output, for example:
Fallback Output: 3
This will be used for any item where country does not match US, CO, or UK, or is missing.
At runtime, the Switch node evaluates the value of {{$json["country"]}} for each item:
If it matches US, the item goes to output 0
If it matches CO, the item goes to output 1
If it matches UK, the item goes to output 2
If it matches none of the above, the item goes to the fallback output 3
This gives you a clear branching structure for your workflow routing.
Working with expressions and data normalization
Both If and Switch nodes rely on expressions to read data from incoming items. In n8n, the most common pattern is to reference fields from the JSON payload of each item.
Basic expressions
To reference fields in expressions:
Country:
{{$json["country"]}}
Name:
{{$json["name"]}}
Normalizing data before comparison
Real-world data is often inconsistent. To avoid subtle mismatches, normalize values before you compare them. You can do this in a Set node or a Function node.
Examples:
Trim whitespace and convert to uppercase:
{{$json["country"]?.trim().toUpperCase()}}
Map full country names to codes, for example:
“United States” → “US”
“United Kingdom” → “UK”
This mapping can be implemented in a Function node or via a lookup table.
Normalizing early in your workflow helps your If and Switch conditions behave predictably.
Testing and debugging your conditional workflow
As you build conditional logic, testing is essential. n8n offers several features that make it easier to see how items move through your workflow.
Execute Workflow:
Click Execute Workflow from the editor.
After execution, double click any node to inspect its Input and Output items.
Logger or HTTP Request nodes:
Insert a Logger node or an HTTP Request node in a branch to inspect what data that branch receives.
Triggers:
Use a Manual Trigger while developing to control when the workflow runs.
When integrating with external systems, you can switch to a Webhook trigger and still inspect items in the same way.
Complex conditions in JavaScript:
For very complex logic, use a Function node.
In the Function node, you can evaluate multiple JavaScript conditions and return a simple route key, such as:
item.route = "US";
Then use a Switch node to route based on item.route.
Best practices for If and Switch nodes
Prefer Switch for many outcomes:
Use the Switch node when you have several distinct routes.
This is usually more readable than chaining multiple If nodes.
Normalize data early:
Handle case differences, extra spaces, and synonyms as soon as possible.
This reduces unexpected behavior in your conditions.
Keep conditions simple and documented:
Avoid very complex logic inside a single If or Switch node.
Use node descriptions to explain what each condition is for.
Use fallback routes:
Always define a fallback output in Switch nodes when possible.
This prevents items from being lost when they do not match any rule.
Fix “Could not Load Workflow Preview” in n8n (Step-by-Step Guide)
Seeing the message “Could not load workflow preview. You can still view the code and paste it into n8n” when importing a workflow can be worrying, especially if you need that automation working immediately.
This guide explains, in a practical and educational way, why this happens and shows you exactly how to rescue, clean, and import the workflow into your n8n instance.
What You Will Learn
By the end of this tutorial, you will know how to:
Understand the main causes of the “Could not load workflow preview” error in n8n
Access and validate the raw workflow JSON safely
Import workflows into n8n even when the preview fails
Fix version, node, and credential compatibility issues
Use CLI or API options when the UI import is not enough
Apply best practices so exported workflows are easier to share and reuse
1. Understand Why n8n Cannot Load the Workflow Preview
When the preview fails, it usually means the UI cannot render the workflow, not that the workflow is lost. The underlying JSON is often still usable.
Common reasons for the preview error
Unsupported or custom nodes Workflows created in another n8n instance may use:
Third-party or community nodes that you do not have installed
Custom nodes created specifically for that environment
These nodes can prevent the visual preview from loading.
Version mismatch The workflow JSON might rely on:
Node properties added in newer n8n versions
Features your current n8n version does not recognize
Missing credentials Some nodes need credentials that:
Do not exist in your instance yet
Use a different credential type name or structure
The preview can fail if these references are inconsistent.
Very large or complex workflows Large JSON payloads, many nodes, or deeply nested expressions can hit UI limits and stop the preview from rendering correctly.
Invalid or corrupted JSON If the export is truncated, malformed, or edited incorrectly, the preview cannot parse it.
Browser or UI rendering issues In rare cases, browser extensions, caching, or UI limitations interfere with the preview, even though the JSON itself is fine.
The key idea: the preview can fail while the workflow JSON is still recoverable and importable.
2. First Rescue Step: View and Validate the Raw Workflow JSON
When the preview fails, your main goal is to get to the raw JSON. That JSON file contains everything n8n needs to reconstruct the workflow.
How to open the raw workflow code
In the n8n UI, look for a link such as “view the code” next to the error message. Clicking it usually opens:
A modal window with the workflow JSON, or
A new browser tab showing the JSON
If you downloaded an exported workflow file (typically .json): Open it with a text or code editor, for example:
Tip: Before editing anything, save a backup copy of the original JSON file. You can always go back if something breaks.
3. Import the Workflow JSON into n8n (Even Without Preview)
Once you have valid JSON, you can import the workflow directly into your n8n instance. The preview is optional, the import is what matters.
Step-by-step: Import a workflow JSON via the UI
Open your n8n instance and go to the Workflows page.
Click the Import option:
This might be in a three-dot menu
Or labeled as “Import” or “Import from file”
Choose how to provide the workflow:
Paste RAW JSON directly into the import dialog, or
Upload the .json file you previously downloaded
Review the import summary:
n8n may show warnings about missing credentials or unknown nodes
Read these messages carefully before confirming the import
Confirm to complete the import.
Typical warnings during import and what they mean
Missing credentials n8n imports the workflow structure but not the actual secrets. After import you will:
Create or map the required credentials in your instance
Attach them to the relevant nodes in the editor
Unknown nodes n8n has detected node types that your instance does not recognize. These are often:
Custom nodes from other installations
Community nodes not installed in your environment
Version incompatibility The workflow may include:
Node parameters or properties that your n8n version does not support
Newer node versions referenced in the JSON
In this case, you might need to edit the JSON or update n8n.
4. Fix Version and Node Compatibility Problems
If the workflow was created with newer features or custom node types, you might need to adjust the JSON before or after import.
How to inspect and edit workflow JSON safely
Open the JSON file in a code editor.
Search for node definitions, especially:
"type" fields that represent the node name
"typeVersion" fields that indicate the node version
Compare these with the nodes available in your n8n instance.
For custom node types:
Install the corresponding custom node package in your n8n instance, or
Replace the custom node with a built-in node that can perform a similar task
If some nodes completely block import:
Make a copy of the JSON file
Temporarily remove or comment out (in your editor, not in actual JSON syntax) the problematic nodes
Import the simplified workflow first
Then re-create or replace those nodes directly in the n8n editor
Review expressions and advanced syntax:
Look for complex expressions like {{$json["field"]["nested"]}} or long function-style expressions
If the import keeps failing, simplify these to static placeholder values
After a successful import, open the workflow in the editor and rebuild the expressions there
Always keep your original JSON as a reference so you can copy expressions or node configurations back as needed.
5. Reattach Missing Credentials Safely
For security reasons, credentials are never exported with workflows. This is expected behavior, not an error.
After importing, reconnect all required credentials
In your n8n instance, create new credentials for each service used in the workflow, for example:
API keys
Database connections
Cloud provider logins
Open the imported workflow in the editor:
Click each node that requires authentication
In the node settings, select or create the matching credential entry
For teams or multiple environments (dev, staging, production):
Use environment-specific credentials in each n8n instance
Consider using a secret manager or environment variables to standardize how credentials are created and referenced
6. Use CLI or API When UI Import Fails
If the UI keeps failing or you prefer automation, you can import workflows using the n8n CLI or REST API, depending on your setup and n8n version.
CLI / API import concepts
Use the REST API endpoint such as /workflows to:
POST workflow JSON directly into n8n
Automate imports in scripts or CI pipelines
On self-hosted instances, check for:
Admin utilities or CLI commands provided by your specific n8n version
Developer or migration tools that handle workflow import programmatically
Before sending JSON to the API:
Confirm that the payload matches the expected workflow schema
Ensure required top-level fields (like nodes, connections, and metadata) are present
Because CLI and API usage can differ between releases, always refer to the official n8n documentation for your exact version for the current commands and endpoints.
7. Quick Fixes for Frequent Problems
Use this section as a checklist when troubleshooting a stubborn workflow JSON.
Validation errors Run the JSON through a validator and fix:
Trailing commas
Mismatched brackets
Encoding or copy-paste issues
Unknown node types If n8n reports unknown nodes:
Install the missing custom or community nodes, then restart n8n
Or edit the JSON to replace these nodes with supported ones
Large JSON fails to preview Skip the preview and:
Use the “Paste RAW JSON” option directly
Or import via file upload or API
Browser-related issues If you suspect the UI:
Try another browser
Disable extensions, especially those that modify page content
Use a private or incognito window to bypass cached scripts
8. Best Practices When Exporting and Sharing n8n Workflows
Prevent future preview and import headaches by following these recommendations whenever you share workflows with others or between environments.
Include a README Alongside the JSON export, add a short text file that lists:
Required custom or community nodes
Credential types needed (for example, “Google Sheets API credential”)
Document the n8n version Mention the exact n8n version used to create the workflow. This helps:
Match versions for compatibility
Decide whether to upgrade or adjust the JSON
Use environment variables for secrets Avoid hardcoding:
API keys
Tokens
Passwords
Instead, rely on environment variables and credential entries inside n8n.
Export smaller functional units Instead of one huge workflow:
Split automations into smaller, focused workflows
Make each module easier to preview, import, and debug
9. Example Checklist: Cleaning a Workflow JSON for Import
Use this simple workflow JSON cleanup checklist whenever you get the “Could not load workflow preview” error.
Validate the JSON Run the file through a JSON validator and fix any syntax errors.
Check node types Search for "type" values:
Compare them with the nodes available in your n8n instance
If you find unsupported or unknown types, temporarily remove them in a copy of the JSON
Remove environment-specific data Delete or replace:
Absolute file paths
Local tokens
IDs that only exist in the original environment
Simplify advanced expressions For very complex expressions:
Replace them with static placeholders so the workflow imports cleanly
Rebuild or paste the full expressions back in the n8n editor once everything loads
10. Recap and Next Steps
The message “Could not load workflow preview” usually indicates a preview or compatibility issue, not a permanently broken workflow. In most cases you can still:
Access and validate the raw workflow JSON
Import the workflow via the n8n UI, CLI, or REST API
Fix problems related to:
Custom or unknown nodes
Version mismatches
Missing credentials
Large or complex workflow structures
If you have tried the steps above and still cannot import the workflow, prepare the following information before asking for help:
Your n8n version
A list of any custom or community nodes installed
The exact error messages you see in the UI or logs
A sanitized copy of the workflow JSON with all secrets removed
Auto-generate n8n Documentation with Docsify and Mermaid
Turn your n8n workflows into readable, searchable docs with live Mermaid diagrams and a built-in Markdown editor, so you can spend less time documenting and more time automating.
Imagine never writing another boring workflow doc by hand
You know that moment when someone asks, “So how does this n8n workflow actually work?” and you open the editor, squint at the nodes, and mumble something about “data flowing through here somewhere”? If your documentation strategy is currently “hope for the best,” you are in good company.
As your n8n automations multiply, keeping track of what each workflow does, why it exists, and how it is wired becomes a full-time job. Manually updating docs every time you tweak a node is not only tedious, it is a guaranteed way to end up with outdated, half-true documentation that nobody trusts.
This workflow template steps in as your documentation assistant. It auto-generates docs from your n8n workflows, wraps them in a lightweight Docsify site, and even draws pretty Mermaid diagrams so you can stop copy-pasting screenshots into wikis.
What this n8n + Docsify + Mermaid setup actually does
At a high level, this workflow takes your n8n instance, peeks at your workflows, and turns them into a browsable documentation site with diagrams and an editor. Here is what it handles for you:
Serves a Docsify-based single-page app so you can browse all your workflow documentation in the browser.
Fetches workflows from your n8n instance and builds a Markdown index table so you can quickly see what exists.
Auto-generates individual documentation pages with Mermaid flowcharts based on your workflow connections.
Provides a live Markdown editor with Docsify preview and Mermaid rendering for fine-tuning docs by hand.
Saves edited or auto-generated Markdown files into a configurable project directory on disk.
Optionally calls a language model to write human-friendly workflow descriptions and node summaries for you.
In short, it takes the repetitive “document everything” chore and hands it to automation, which feels nicely poetic.
Key building blocks of the workflow
Docsify frontend: your lightweight docs site
Docsify is the front-end engine that turns Markdown files into a responsive documentation site, all in the browser. No static site generator builds, no complicated pipelines.
The workflow generates a main HTML page that:
Loads Docsify in the browser.
Uses a navigation file (summary.md) on the left for browsing.
Serves content pages like README.md and workflow-specific docs such as docs_{workflowId}.md.
Mermaid diagrams: visual maps of your workflows
Mermaid.js converts text-based flowchart descriptions into SVG diagrams. The workflow reads your n8n workflow JSON and constructs a Mermaid flowchart string from node types and connections.
The result is a visual schematic on each doc page, so instead of saying “the webhook goes to the function node which then branches,” you can just point to a diagram and nod confidently.
Auto-generation logic: docs that appear when you need them
Whenever a docs page is requested and does not yet exist, the workflow creates a Markdown template that includes:
A header and basic structure.
A description section, which can be filled by you or generated with an LLM.
A Mermaid graph representing the workflow connections.
A metadata table with details like created date, updated date, and author.
This guarantees that every workflow has at least a minimal, accurate doc page without you opening a blank file and wondering where to start.
Live Markdown editor: tweak docs in the browser
The template also includes an editor view. It provides a split layout:
Left side: an editable Markdown textarea where you can refine descriptions, add notes, or fix typos.
Right side: a Docsify-powered preview that supports Mermaid diagrams and updates as you type.
When you hit the Save button, your Markdown file is written directly to the configured project directory so future visits load your polished version instead of regenerating it.
Optional LLM integration: let AI handle the wordy bits
If you enable it, the workflow can call a language model to:
Generate a concise, human-friendly overview of what the workflow does.
Summarize node configurations in readable form.
The LLM output is formatted into Markdown and merged into the doc template. It is meant as a helpful assistant, not an unquestioned source of truth, so you can always edit or override what it writes.
How the workflow responds to docs requests
Behind the scenes, the workflow behaves like a tiny docs server that reacts to incoming paths. Here is the flow, simplified:
Request comes in
Docsify or a user requests a specific docs path, for example /docs_{workflowId}.
Routing logic kicks in
A webhook node checks which file or path is being requested and decides which branch of the workflow to run. It can serve:
The main index table of workflows.
Tag-based views.
A single workflow documentation page.
The editor interface.
File check on disk
The workflow looks in the configured project directory:
If the Markdown file already exists, it returns the file right away.
If it does not exist, the workflow either:
Auto-generates a new doc page, or
Offers an editor template so you can start writing.
Mermaid diagram generation
The workflow reads your workflow JSON and constructs a Mermaid flowchart string based on the nodes and their connections. This text is embedded into the Markdown so Docsify can render it as a diagram.
Optional LLM step
If enabled, the workflow calls a language model to produce:
A human-readable workflow description.
Summaries of important node settings.
These are merged into the Markdown template before returning the page.
Saving edits for next time
When you use the editor and click Save, the content is written to disk in project_path. Future requests for that page read your saved Markdown instead of regenerating it.
The net effect is that your documentation grows and improves naturally as you browse and edit, without manual file juggling.
Configuration and deployment: set it up once, enjoy forever
All the important knobs live in a single CONFIG node so you do not have to chase variables around the workflow. Here is what you configure:
project_path – the directory where Markdown files are stored. This path must be writable by the n8n process. The workflow includes a step to create the directory if it does not exist.
instance_url – the public URL of your n8n instance, used to generate links back to the n8n workflow editor from the docs.
HTML_headers and HTML_styles_editor – custom HTML snippets that Docsify consumes, including:
Mermaid.js loading.
Styles and layout tweaks.
Meta tags or theme settings.
Deployment notes
To get everything running smoothly, keep these points in mind:
Run this workflow in an environment where n8n has file system access to project_path. If that is not possible, you can adapt it to store files in object storage such as S3 and serve them from a static host.
If your n8n instance is hosted in the cloud, set instance_url to the public URL and make sure CORS and host headers are configured correctly so Docsify links behave.
The editor writes files directly to disk. For production use, you will probably want to:
Restrict access to internal networks, or
Put authentication in front of the webhook.
Security and maintenance: a few important caveats
Automating documentation is great, but you still want to keep things safe and sane.
The example includes a live editor that writes files without authentication. Do not expose this directly on the public internet without extra access control.
Sanitize any user-provided content before saving if those files are later consumed by other systems or displayed in sensitive contexts.
If you use an LLM:
Store API keys securely and avoid hardcoding them in the workflow.
Review generated text for accuracy and avoid treating it as an authoritative source. Think of it as a helpful draft writer, not an auditor.
Customization ideas to level up your docs workflow
Once the basics are running, you can extend this setup to match your team’s workflow.
Git-backed documentation
Store the Markdown files in a Git repository and automatically commit on save. You can add a Git client step or another automation that commits and pushes changes so every doc edit is versioned.
Access control
Protect the editor and docs behind OAuth, an identity provider, or a reverse proxy. This lets you safely offer editing to internal users without opening it to the world.
Extra artifacts per workflow
Render more than just diagrams and descriptions:
Sample payloads.
Relevant logs or outputs.
Example executions or run history snippets.
Tag-based documentation views
Use n8n workflow tags to filter and generate focused documentation pages for specific teams, projects, or environments. For example, docs only for “billing” workflows or “marketing” automations.
Troubleshooting common issues
If something looks off, it is usually a small configuration detail. Here is what to check.
Mermaid diagrams not rendering
Verify that Mermaid.js is correctly loaded in your HTML_headers snippet.
Ensure the generated Mermaid text is valid. The workflow already includes logic to replace code blocks with Mermaid containers before rendering, but malformed diagrams can still break rendering.
Docsify preview looks broken or weird
Check the CSS and the Docsify theme link inside HTML_headers. A missing or incorrect stylesheet can make everything look slightly cursed.
If your site is served from a subdirectory, confirm that basePath and related settings are correct so Docsify can find your Markdown files.
Files are not being saved
Confirm that project_path exists or can be created. The workflow includes a mkdir step to create the directory if it is missing.
Make sure the n8n process has write permissions to that directory. Without that, the Save button will look enthusiastic but do nothing.
When this template is a perfect fit
This approach works especially well if you want:
Fast, always-up-to-date documentation for your automation team without manual copy-paste marathons.
Visual diagrams that help non-developers understand how workflows are wired.
A simple, browser-based editing experience for technical writers, operators, or anyone who prefers Markdown over mystery diagrams.
If you have ever thought “I really should document this” and then did not, this workflow is for you.
Every product team knows the feeling. Your Jira board is full of rich epics, but turning them into clear, polished Product Requirement Documents (PRDs) takes hours of focused work. It is important work, yet it often pulls you away from strategy, discovery, and building the next big thing.
This is where automation can become a real turning point. With n8n, OpenAI, Google Drive, and AWS S3 working together, you can transform raw Jira epics into structured PRDs automatically. The n8n workflow template in this guide is not just a technical shortcut, it is a practical stepping stone toward a more focused, automated way of working.
In this article, you will walk through the journey from problem to possibility, then into a concrete, ready-to-use n8n template. You will see exactly how the workflow is built, how each node works, and how you can adapt it, extend it, and make it your own.
From manual grind to meaningful work
Manually creating PRDs from Jira epics is repetitive and error prone. You copy details from Jira, reformat them in a document, try to keep a consistent structure across projects, and hope nothing gets missed. Over time, this drains energy and slows your team down.
Automating PRD creation changes the equation:
You save hours per week that can be reinvested in discovery, user research, and strategy.
You reduce human error, especially around missing details or inconsistent formatting.
You create a repeatable, standardized way to turn epics into PRDs on demand.
Instead of staring at a blank page, you start with a complete, AI-generated draft in Google Docs, plus archived copies in AWS S3. Your role shifts from “document assembler” to “editor and decision maker.” That is the mindset shift this n8n template supports.
Adopting an automation-first mindset
Before diving into nodes and settings, it helps to view this workflow as the first of many automations you can build. n8n makes it possible to connect tools you already use, then orchestrate them in a way that reflects how your team actually works.
With this template you are:
Letting Jira remain the source of truth for epics and issues.
Using OpenAI as a writing assistant that turns structured data into narrative content.
Relying on Google Drive and AWS S3 for collaboration and long-term storage.
As you implement it, you will likely see other opportunities to automate: review flows, notifications, versioning, and more. Think of this PRD workflow as a foundation you can build on, not a finished endpoint.
What this n8n template actually does
The provided n8n workflow template is a linear, easy-to-follow flow that starts with a manual trigger and ends with ready-to-edit PRDs. At a high level, here is what it accomplishes:
Starts the workflow on demand with a Manual Trigger.
Queries Jira for projects and filters them down to the ones you care about.
Fetches epics for each selected project using Jira’s APIs.
Aggregates epic data into a clean, structured format.
Sends that data to an AI agent (OpenAI via LangChain) to generate PRD content.
Creates a Google Doc for collaboration and stores plain text copies in AWS S3.
The result is a repeatable system: whenever you are ready for a fresh PRD draft, you execute the workflow and let n8n handle the heavy lifting.
Step-by-step journey through the workflow
1. Starting with intention: Manual Trigger
The Manual Trigger node is your starting point. It lets you run the workflow when you are ready to generate or refresh PRDs.
Action: Click “Execute workflow” in n8n.
Outcome: You stay in control of when drafts are generated, which is ideal while you are still experimenting and refining the process.
2. Gathering raw materials: Querying Jira projects
Next, the workflow reaches out to Jira to understand which projects exist and which ones you want to include.
Node:HTTP Request
Purpose: Call Jira’s /project/search endpoint to retrieve projects.
Key settings:
Use Jira Cloud credentials configured in n8n.
Enable pagination using responseContainsNextURL with nextPage and isLast, or adapt to Jira’s startAt and total if necessary.
The Code1 (merge values) node then flattens batched project results so you have a single, clean list to work with:
Node:Code1 (merge values)
Purpose: Concatenate response arrays into one collection.
3. Focusing on what matters: Filtering projects
Not every Jira project needs a PRD at the same time. The workflow uses an If node to filter out projects that do not match your criteria.
Node:If
Purpose: Include only desired projects.
Key settings:
Set conditions based on project key or other fields that identify relevant projects.
This is where you start tailoring the automation to your reality. You can focus on specific product lines, environments, or teams simply by updating the filter logic.
4. Pulling in the real story: Fetching Jira epics
Once you know which projects matter, the workflow fetches all epics for each one.
Node:Jira Software
Purpose: Retrieve issues of type Epic for each project.
Make sure the fields you need are included, such as summary, description, and any relevant custom fields.
This step transforms your Jira data into the raw narrative ingredients that the AI will later shape into a PRD.
5. Structuring the data: Grouping epics by project
To make the AI’s job easier, the workflow groups epics by project and extracts only the necessary information.
Node:Code
Purpose:
Group epics per project.
Return one item per project with an epics array that includes summary and description.
By structuring data clearly at this stage, you help ensure that the generated PRDs are coherent, organized, and easy to adapt to your team’s style.
6. Turning data into narrative: AI Agent with OpenAI
Now comes the transformational step. The aggregated epic data is sent to an AI agent that uses OpenAI to generate the PRD content.
Node:AI Agent (LangChain/OpenAI)
Purpose: Convert epics JSON into a structured PRD draft.
Key settings:
The prompt includes the epics JSON and clear instructions.
A structured output parser is used so the AI returns machine-readable sections and content.
This is where your time savings really show up. Instead of manually synthesizing every epic, the AI gives you a starting point that you can refine, adjust, and align with your product vision.
7. Making it collaborative and permanent: Google Drive and S3
Finally, the workflow turns the AI output into shareable documents and long-term records.
Nodes:Google Drive and S3
Purpose:
Create a Google Doc from plain text for collaborative editing.
Upload plain text copies to an AWS S3 bucket for archiving and version control.
Key settings:
Use the Google Drive createFromText node to convert text into a Google Doc.
Specify the target folder in Google Drive and ensure the account has write permission.
Set the S3 bucket, folder, and file naming convention (for example, include project key and timestamp).
At this point, your workflow has turned Jira epics into living documents your team can review, comment on, and evolve, while also storing a traceable record in S3.
Key configuration tips for a smooth setup
To get the most out of this n8n PRD template, pay attention to a few critical configuration details.
Jira authentication:
Use an API token or OAuth credentials configured in n8n.
For higher volumes, OAuth or app links are often more resilient to rate limits.
Pagination in Jira:
The HTTP Request node uses responseContainsNextURL with nextPage and isLast.
Verify that your Jira responses include these fields or adjust to use startAt and total pagination.
JQL precision:
Use accurate JQL such as issuetype = Epic AND project = PROJECTKEY.
Include all fields you need in the request so the AI has enough context.
OpenAI prompts:
Keep prompts deterministic and explicit.
Define an output schema via a structured output parser so results are consistent and easy to process.
Google Drive conversion:
Use the createFromText operation to generate a Google Doc from plain text.
Make sure the connected account can write to the chosen folder.
Security, compliance, and responsible automation
Automating PRD generation does not mean relaxing your security standards. You can design this workflow to respect privacy, compliance, and internal policies.
Limit data sent to OpenAI:
Avoid including sensitive personal information in prompts.
If epics contain confidential details, consider redacting or obfuscating them before sending to the AI.
Use least privilege for service accounts:
Create dedicated service accounts for Google Drive and AWS S3.
Grant only the permissions required for file creation and upload.
Audit and encryption:
Enable audit logging on Google Drive and S3 buckets.
Ensure encryption at rest is enabled for all storage.
Control your environment:
Consider self-hosting n8n for more control over data flow and network access.
Troubleshooting and learning from failures
Every automation journey includes a bit of debugging. When something breaks, treat it as a chance to improve the workflow.
Missing fields in Jira:
If descriptions are null, verify that the fields parameter includes description and any custom field IDs you need.
Rate limits from Jira or OpenAI:
If you see throttling, add retry logic or backoff strategies in the HTTP Request or OpenAI nodes.
Structured Output Parser errors:
If parsing fails, simplify the schema or loosen requirements temporarily to see what the model is returning.
Iterate until the structure is reliable, then tighten again.
Google Drive permission issues:
If file creation fails, double check that the service account has write access to the target folder and that sharing settings are correct.
Extending the template as your workflow matures
Once the basic automation is working, you can start turning it into a richer, more powerful system that matches how your team operates.
Scheduled runs:
Use n8n’s scheduling to generate weekly PRD drafts for all active projects.
Review and collaboration steps:
After creating the Google Doc, add a Slack node that posts a message to a channel or user group with the document link and a review checklist.
Versioning strategy:
Store each generated PRD in S3 with a timestamp.
Use S3 lifecycle rules to archive or clean up older versions automatically.
Linking back to Jira:
Update the relevant project or epic in Jira with a comment that includes the PRD link.
This keeps traceability between requirements and documentation.
Custom prompt templates:
Create multiple prompt variants tailored to different product types, such as mobile apps, platform features, or internal tools.
Each of these extensions moves you closer to a fully integrated product documentation pipeline that runs with minimal manual effort.
Best practices for AI-generated PRDs
AI can accelerate your work, but it is most powerful when combined with human judgment. Treat PRD generation as a partnership between automation and your product expertise.
Always review the drafts:
Use generated PRDs as starting points. Product managers should validate assumptions, refine language, and ensure alignment with strategy.
Standardize prompts and templates:
Keep prompt wording and structure consistent across projects to maintain predictable output quality.
Log generation metadata:
Capture who triggered the workflow, when it ran, which prompt version and model were used.
This makes it easier to trace issues and understand changes in output quality over time.
Iterate based on feedback:
Invite reviewers to share what worked and what did not in the generated PRDs.
Adjust prompts and instructions to the model to continuously improve results.
Pre-production checklist for a confident launch
Before you rely on this workflow for critical documentation, walk through a quick checklist to ensure everything is ready.
Create, Update & Get MailerLite Subscribers with n8n (So You Never Manually Copy Emails Again)
Picture this: you are copying a new subscriber’s email from one tool, pasting it into MailerLite, updating their city, double checking you did not misspell “Berlin”, and then repeating that for the next person. And the next. And the next. At some point your brain quietly leaves the chat.
Good news: n8n can do all of that for you, without complaining, getting bored, or mis-typing someone’s email. In this guide, you will learn how to use an n8n workflow template that:
Creates a MailerLite subscriber
Updates a custom field for that subscriber (like their city)
Retrieves the subscriber again so you can confirm everything looks perfect
All in one neat, repeatable automation. No more copy-paste marathons.
Why bother automating MailerLite with n8n?
MailerLite is a solid email marketing platform. n8n is a low-code workflow automation tool that connects your apps together so they talk nicely and do the boring stuff for you.
Put them together and you get a powerful combo for:
Onboarding flows – automatically add new users to MailerLite when they sign up
CRM enrichment – keep subscriber data in sync with your CRM or other tools
Data synchronization – make sure your email list is always up to date
The workflow in this template follows a simple pattern that you will use a lot in automation:
create -> update -> get
Once you understand this pattern, you can reuse it across many other integrations, not just MailerLite.
What this n8n + MailerLite workflow actually does
This template is a small, focused workflow that shows the full lifecycle of a subscriber inside MailerLite using the dedicated MailerLite node in n8n.
Here is the flow in human terms:
You manually start the workflow while testing.
n8n creates a new MailerLite subscriber with an email and a name.
n8n updates that same subscriber’s custom field, for example their city.
n8n fetches the subscriber again so you can confirm the field was updated correctly.
Under the hood, this happens through three MailerLite nodes connected in sequence:
Node 1 (MailerLite) – operation: create, sets email and name
Node 2 (MailerLite1) – operation: update, uses subscriberId from Node 1 to update a custom field like city
Node 3 (MailerLite2) – operation: get, uses the same subscriberId to retrieve the updated record
It is a small workflow, but it covers the three most common subscriber operations you will likely use over and over.
Grab the n8n MailerLite template JSON
If you would rather not build everything from scratch (fair), you can import the ready-made template into your n8n instance and be up and running in a minute or two.
Here is the exact workflow JSON used in the template:
Quick setup guide: from zero to automated subscriber
Let us walk through the setup in a clean, simple sequence. No fluff, just the steps you actually need.
Step 1 – Add a Manual Trigger
Start with a Manual Trigger node in n8n. This lets you click a button in the editor to run the workflow while you are still building and testing it.
Later, you can replace this trigger with something more useful in real life, such as:
A webhook that fires when someone submits a form
A scheduled trigger that runs periodically
Another app event, like a CRM update
Step 2 – Create the MailerLite subscriber
Next, add your first MailerLite node and configure it to create a subscriber.
In the node settings:
Set the operation to create subscriber
Fill in the email field
Set additionalFields.name or any other fields you want to store
The example template uses:
email: harshil@n8n.io
name: Harshil
Once this node runs, MailerLite creates a new contact and returns the subscriber data, including the email that we will reuse as the identifier in the next steps.
Step 3 – Update the subscriber’s custom field
Now add a second MailerLite node, which will handle the update operation.
In the settings for this node:
Set operation to update
In subscriberId, reference the email returned from the first MailerLite node using an expression:
{{$node["MailerLite"].json["email"]}}
Then configure the custom field update:
Open updateFields.customFieldsUi.customFieldsValues
Add a new custom field object with:
value: "Berlin"
fieldId: "city"
In other words, you are telling MailerLite: “For the subscriber whose ID is this email, set the custom field city to Berlin.” No more manual profile editing.
Step 4 – Get the subscriber to confirm the update
Finally, add a third MailerLite node and set its operation to get.
Again, use the same email expression in the subscriberId field:
{{$node["MailerLite"].json["email"]}}
When you run the workflow, this node fetches the latest version of the subscriber record. Open the node output and you should see the updated city custom field, now proudly set to Berlin.
Testing your MailerLite automation workflow
Before you unleash this on your actual audience, do a quick test run.
Import the template JSON into your n8n instance or recreate the nodes manually using the steps above.
Set up MailerLite credentials in n8n by adding your API key in the node credential section.
Execute the workflow using the Manual Trigger. Watch each node run in sequence.
Inspect the final MailerLite node output and confirm that:
The subscriber was created
The custom field (for example city) was updated
The get operation returns the updated data
If everything looks right, you have a working create-update-get flow for MailerLite.
Best practices for MailerLite automation in n8n
Once the basic flow works, a few small tweaks can make it more robust and less likely to break at 2 a.m.
Use email as subscriberId when it makes sense MailerLite lets you use the email as an identifier for many operations. This keeps things simple, especially in smaller workflows where you do not want to track multiple IDs.
Handle existing subscribers gracefully If your create operation might run for an email that already exists, decide how you want to handle it:
Use MailerLite’s upsert behavior if available
Or add a preliminary search/get step to check if the subscriber already exists, then branch to update instead of create
Double check custom field IDs Custom fields in MailerLite use specific IDs or keys. The example uses city, but in your account it might be different. Open your MailerLite settings to confirm the correct fieldId before wondering why nothing updates.
Add error handling for production For real-world workflows, add a Catch node or use the “Execute Workflow on Error” pattern. This lets you log failures, retry operations, or send yourself a warning when MailerLite is not in the mood.
Respect rate limits and plan retries If you are working with large lists, keep MailerLite’s rate limits in mind. Use n8n’s HTTP Request node options or node settings to add delays or exponential backoff so your workflow plays nicely with the API.
Common issues and how to fix them
Problem 1 – “Subscriber not found” on update or get
If the update or get step says the subscriber does not exist, the usual suspect is the subscriberId value.
Check that:
You are using the exact email returned by the create node
There is no extra whitespace around the email
If needed, you can trim whitespace directly in the expression:
={{$node["MailerLite"].json["email"].trim()}}
Problem 2 – Custom field not updating
If the custom field stubbornly refuses to change, verify the fieldId or key is correct.
In MailerLite:
Go to your custom fields settings
Find the field you want to use
Confirm the exact identifier that MailerLite expects
Make sure that ID matches what you put in the customFieldsValues configuration in n8n.
Problem 3 – Authentication or API errors
If n8n cannot talk to MailerLite at all, it is usually a credentials issue.
Re-check that your MailerLite API key is valid and active
Confirm it has the required permissions
Re-add the credentials in n8n and test a simple GET request to confirm everything works
Where to go next with this workflow
This simple create-update-get pattern is like the “Hello world” of integrations. Once you are comfortable with it, you can start making it more powerful and more tailored to your real processes.
Ideas for next steps:
Add conditional logic, for example only update certain fields if the user meets specific criteria
Sync subscribers from sources like Google Sheets, CRMs, or signup forms directly into MailerLite
Track subscriber activity or events and push that data into analytics tools
Extend the workflow with error handling, logging, and notifications when something fails
Before you know it, you will have a fully automated email list system that quietly keeps everything in sync while you focus on more interesting work than updating cities one by one.
Try the MailerLite n8n template now
Ready to retire manual subscriber updates?
Import the workflow template into your n8n instance
Connect your MailerLite credentials
Run the workflow and watch it create, update, and fetch a subscriber for you
If you want help tailoring this flow to your specific stack or use case, reach out or leave a comment. And if this guide helped you escape repetitive email list chores, consider subscribing for more n8n automation tutorials.
Call-to-action: Ready to automate your email list? Import the workflow, connect MailerLite, and run it. If you liked this guide, subscribe for more n8n automation tutorials.
Ever had an AI confidently say something like, “According to the document…” and then absolutely refuse to tell you which document it meant? That is what this workflow template fixes.
With this n8n workflow, you can take the raw, slightly chaotic output from an OpenAI assistant that uses file retrieval, and turn it into clean, human-friendly citations. No more mystery file IDs, no more guessing which PDF your assistant was “definitely sure” about. Just clear filenames, optional links, and nicely formatted content your users can trust.
What this n8n workflow actually does
This template gives you a structured, automated way to:
Collect the full conversation thread from the OpenAI Threads/Messages API
Extract file citations and annotations from assistant responses
Map ugly file_id values to nice, readable filenames
Swap raw citation text for friendly labels or links
Optionally convert Markdown output to HTML for your UI
In other words, it turns “assistant output with weird tokens and half-baked citations” into “polished, source-aware responses” without you manually clicking through logs like it is 2004.
Why bother with explicit citations in RAG workflows?
When you build Retrieval-Augmented Generation (RAG) systems with OpenAI assistants and vector stores, the assistant can pull in content from your files and attach internal citations. That is great in theory, but in practice you might see:
Raw citation tokens that look nothing like a useful reference
Strange characters or incomplete metadata
Inconsistent formatting across different messages in a thread
Adding a post-processing step in n8n fixes that. With this workflow you can:
Replace cryptic tokens with clear filenames and optional links
Aggregate citations across the entire conversation, not just a single reply
Render output as Markdown or HTML in a consistent way
Give end users transparent, trustworthy source references
Users get to see where information came from, and you get fewer “but which file did it use?” support messages. Everyone wins.
What you need before you start
Before you spin this up in n8n, make sure you have:
An n8n instance (cloud or self-hosted)
An OpenAI API key with access to assistants and files
An OpenAI assistant already set up with a vector store, with files uploaded and indexed
Basic familiarity with n8n nodes, especially the HTTP Request node
Once that is in place, the rest is mostly wiring things together and letting automation do the repetitive work for you.
High-level workflow overview
Here is the overall journey your data takes inside n8n:
User sends a message in the n8n chat UI
The OpenAI assistant responds, using your vector store for file retrieval
You fetch the full thread from the OpenAI Threads/Messages API for complete annotations
You split the response into messages, content blocks, and annotations
You resolve each citation’s file_id to a human-readable filename
You aggregate all citations, then run a final formatting pass
Optionally, you convert Markdown to HTML before sending it to your frontend
Main n8n nodes involved
The template uses a handful of core nodes to make this magic happen:
OpenAI Assistant (assistant resource) – runs your assistant configured with vector store retrieval.
HTTP Request (Get ALL Thread Content) – calls the OpenAI Threads/Messages API to fetch the full conversation with annotations.
SplitOut nodes – iterate over messages, content blocks, and annotations or citations.
HTTP Request (Retrieve file name from file ID) – calls the OpenAI Files API to turn file_id into a filename.
Set node (Regularize output) – normalizes each citation into a consistent object with id, filename, and text.
Aggregate node – combines all citations into a single list for easier processing.
Code node (Finally format the output) – replaces raw citation text in the assistant reply with formatted citations.
Optional Markdown node – converts Markdown output to HTML, if your frontend prefers HTML.
Step-by-step: how the template workflow runs
1. User sends a message and the assistant replies
The journey starts with the Chat Trigger node. A user types a message in your n8n chat UI, and that input is forwarded to the OpenAI Assistant node.
Your assistant is configured to use a vector store, so it can fetch relevant file snippets and attach citation annotations. The initial response might include short excerpts plus internal references that point back to your files.
2. Fetch the full thread content from OpenAI
The assistant’s immediate response is not always the full story. Some citation details live in the full thread history instead of the single message you just got.
To get everything, you use an HTTP Request node to call:
GET /v1/threads/{threadId}/messages
and you include this special header:
OpenAI-Beta: assistants=v2
This returns all message iterations and their annotations, so you can reliably extract the metadata you need for each citation.
3. Split messages, content blocks, and annotations
The Threads/Messages API response is nested. To avoid scrolling through JSON for the rest of your life, the workflow uses a series of SplitOut nodes to break it into manageable pieces:
Split the thread into individual messages
Split each message into its content blocks
Split each content block into annotations, typically found under content.text.annotations
By the end of this step, you have one item per annotation or citation, ready to be resolved into something readable.
4. Turn file IDs into filenames
Each citation usually includes a file_id. That is great for APIs, not so great for humans. To translate, the workflow uses another HTTP Request node to call the Files API:
GET /v1/files/{file_id}
This returns the file metadata, including the filename. With that in hand, you can show something like project-plan.pdf instead of file-abc123xyz. You can also use this metadata to construct links to your file hosting layer if needed.
5. Regularize and aggregate all citations
Once the file metadata is retrieved, a Set node cleans up each citation into a simple, consistent object with fields like:
id
filename
text (the snippet or text in the assistant output that was annotated)
Then an Aggregate node merges all those citation objects into a single array. That way, the final formatting step can process every citation in one pass instead of juggling them individually.
6. Replace raw text with formatted citations
Now for the satisfying part. A Code node loops through all citations and replaces the raw annotated text in the assistant’s output with your preferred citation style, such as _(filename)_ or a Markdown link.
Here is the example JavaScript used in the Code node:
// Example Code node JavaScript (n8n)
let saida = $('OpenAI Assistant with Vector Store').item.json.output;
for (let i of $input.item.json.data) { saida = saida.replaceAll(i.text, " _("+ i.filename+")_ ");
}
$input.item.json.output = saida;
return $input.item;
You can customize that replacement string. For instance, if you host files externally, you might generate Markdown links such as:
Adjust the formatting to match your UI design and how prominently you want to display sources.
7. Optional: convert Markdown to HTML
If your chat frontend expects HTML instead of raw Markdown, you can finish with a Markdown node. It takes the Markdown-rich assistant output and converts it into HTML, ready to render in your UI.
If your frontend already handles Markdown, or you prefer to keep responses as Markdown, you can simply deactivate this node.
Tips, best practices, and common “why is this doing that” moments
Rate limits and batching
If you are resolving a lot of file_id values one by one, you may run into OpenAI rate limits. To keep things smooth:
Batch file metadata requests where possible
Cache filename lookups in n8n (for example, with a database or in-memory cache)
Reuse cached metadata for frequently accessed files
Security and access control
Some quick security reminders:
Store your OpenAI API key inside n8n credentials, not directly in nodes
When exposing filenames or links, make sure your links respect your access controls
Avoid leaking private file URLs to users who should not see them
Dealing with ambiguous or overlapping text matches
Simple string replacement is convenient, but it can be a bit literal. If two citations share overlapping text, you might get unexpected substitutions.
To reduce this risk:
Prefer replacing the exact annotated substring from the citation object
Consider using unique citation tokens in the assistant output that you later map to friendly labels
Normalize whitespace or punctuation before replacement if your data is slightly inconsistent
Formatting styles that work well in UIs
Depending on your frontend, you can experiment with different citation formats, for example:
Inline citations like _(filename)_
A numbered “Sources” list at the end of the message with links
Hover tooltips that show extra metadata such as page numbers or section IDs
The workflow gives you the raw ingredients. How you present them is completely up to your UX preferences.
Ideas for extending this workflow
Once the basic pipeline is running, you can take it further:
Store file metadata in a database to speed up lookups and reduce API calls
Generate a numbered bibliography and replace inline citations with references like [1], [2], etc.
Include richer provenance data such as page numbers or section identifiers when available
Integrate access control logic so users only see citations for files they are allowed to access
Quick troubleshooting checklist
No annotations from OpenAI? Check that your assistant is configured to return retrieval citations and that you fetch the full thread via the Threads API.
File metadata calls returning 404? Verify that the file_id is correct and that the file belongs to your OpenAI account.
Replacements not appearing consistently? Confirm that the excerpt text matches exactly. If needed, normalize whitespace or punctuation before replacement.
Wrapping up
By adding this citation processing pipeline to your n8n setup, you turn a basic RAG system into a much more transparent and reliable experience. The workflow retrieves full thread content, extracts annotations, resolves file IDs to filenames, and replaces raw tokens with readable citations or links.
You can drop the provided JavaScript snippet into your n8n Code node and tweak the formatting to output Markdown links or HTML. From there, it is easy to layer on caching, numbering, or more detailed provenance data as your use case evolves.
Try the template in your own n8n instance
If you are tired of hunting through JSON to figure out which file your assistant used, this workflow template is for you. Spin it up in your n8n instance, connect it to your assistant, and enjoy the relief of automated, clear citations.
If you need a customized version for your dataset, or want help adding caching and numbering, feel free to reach out for a consultation or share your requirements in the comments.
