Build real scientific tools — n8n as your lab bench
5 lessons2026-08-06AI-generated
1Overview
Five sessions on building real scientific AI tools in n8n — scientific databaseAPIs, error handling, a tool-using research assistant, a five-stage literature pipeline, and your own domain-tuned review pipeline. By the end you can ship a small but useful research tool.
Five sessions that turn n8n from a demo into a lab bench. You query scientific APIs directly — PubMed esearch and efetch — then make the pipeline survive their failures instead of crashing on them, compose a research assistant out of search plus paper details plus synthesis, and run a five-stage pipeline that extracts structured data from abstracts. The last session points the whole thing at a question from your own field. → It assumes the basics chapter: a trigger, a model node and an IF branch are not re-explained.
1.1After this chapter you can
→Query PubMed (esearch + efetch) and other scientific APIs from n8n
→Build a pipeline that handles API failures without crashing
→Compose a research assistant from PubMed search + paper details + AI synthesis
→Run a five-stage pipeline that extracts structured data from abstracts
→Tune a literature review pipeline to a question from your own field
2Lessons 5
2.1Query PubMed with natural language in n8n
PubMed's API is free and needs no authentication, allowing natural‑language queries to retrieve and summarise paper abstracts within n8n.
Run a workflow that retrieves and summarises scientific papers without needing credentials
TryFind papers about CRISPR base editing from 2024
In n8n, open the imported workflow and click the Chat panel, then type the prompt exactly as shown. Watch the Execution Log on the right—each node will populate with API responses before the AI’s summary appears.
Download Session 5 — Pulling data from scientific databases from the Downloads page
In n8n, click Import from file to load the downloaded workflow and open the chat panel
Enter a natural‑language query such as “Find papers about CRISPR base editing from 2024” in the chat input
After the AI response appears, type another prompt like “What are the main limitations mentioned in those papers?”
Start a new search by typing “Find me papers about mRNA vaccine immunogenicity”
Open the execution log on the right and click each node to view its output at every stage
The workflowthe real, live n8n canvas — click to pan & zoom, or copy it onto your own
Loading the live workflow…
You'll see AI‑summarised lists of papers appear for each query and the execution log shows output from every node
Takeaway plain‑language prompts can drive PubMed searches and AI summarisation within a single n8n workflow
Check What result does the workflow produce after you type a natural‑language query into the chat panel and how is it displayed in the execution log?
2.2Make pipelines branch on input and handle errors
The workflow checks for a PubMed ID, routes accordingly, and captures HTTP errors without stopping the run.
Trigger all decision branches and see how failures are caught
TrySummarise paper 38167000
What is the latest in cancer immunotherapy?
Summarise 99999999
Paste the three lines into the n8n chat panel after importing the workflow. After each entry watch the execution log to see which path lights up green, and finally click the Fetch Paper node to verify the error connector turns red.
Download the session file from Downloads (curriculum.32dots.de/share)
In n8n, select Import from file and load the downloaded workflow
Open the chat panel, type a valid PMID such as 38167000 and run it
Observe the execution log to see which path is highlighted green
Type a query without a PMID like “What is the latest in cancer immunotherapy?” and run it
Type an invalid PMID such as 99999999 and run it
In the execution log, click the Fetch Paper node and note the red error connector
The workflowthe real, live n8n canvas — click to pan & zoom, or copy it onto your own
Loading the live workflow…
You'll see All three execution paths light up in the log, showing which node and branch ran each time
TakeawayPipelines can automatically choose a path based on data presence and capture HTTP errors without stopping the whole workflow
Check How can you tell from the execution log that an HTTP error was captured by the error connector without halting the entire pipeline?
2.3Generate a synthesized research summary with n8n
A research‑assistant workflow that searches PubMed, retrieves metadata and uses AI to synthesise a concise answer citing specific papers.
Ask a scientific question and receive a concise answer that cites specific papers
TryWhat are the current approaches to CAR‑T cell therapy in solid tumors?
Paste this text into the Chat panel of the imported ‘Session 7 — Research assistant’ workflow in n8n, then hit Enter. Watch the execution log progress through Plan Search → PubMed Search → Extract IDs → Paper Details → Format → AI Synthesizer for the synthesized answer.
Download the workflow file from Downloads at curriculum.32dots.de/share
In n8n, click Import from file, load the workflow and open the chat panel
Enter a research question such as “What are the current approaches to CAR‑T cell therapy in solid tumours?” and press Enter
Watch the execution log display the nodes Plan Search → PubMed Search → Extract IDs → Paper Details → Format → AI Synthesiser
Enter a follow‑up question like “Which of those approaches is furthest along in clinical trials?” and submit
Enter another query such as “What are the common limitations mentioned across these papers?” and submit
Open the Format Paper List node to inspect the generated list of papers
The workflowthe real, live n8n canvas — click to pan & zoom, or copy it onto your own
Loading the live workflow…
You'll see A synthesised response referencing PMIDs appears, followed by a coherent follow‑up using the same papers
Takeaway n8n can chain PubMed searches to an AI synthesiser delivering structured paper lists and summarised answers
Check In what way does the workflow allow you to ask follow‑up questions and receive answers that reference the same set of retrieved papers?
2.4Run a multi‑stage literature pipeline
A multi‑stage pipeline that turns a plain‑language query into a formatted Markdown comparison table of papers, including title, year, method, key finding and limitation.
Generate a formatted Markdown comparison table from a plain‑language query using the n8n workflow
TrymTOR inhibitor resistance mechanisms in breast cancer
After importing the workflow (⋯ → Import from file) open the Chat panel in n8n and paste the prompt there. Then hit Enter and watch the execution log on the right as each node lights up, ending with a Markdown table in the response.
Download the ‘Session 8 — Multi‑stage literature pipeline’ file from the curriculum site
In n8n, click Import from file and select the downloaded workflow
Open the Chat Panel, type your research query and submit
Observe the execution log as each node lights up while the five‑stage pipeline runs
Click the Stage 3 — AI Extract node in the log to view its raw JSON output
The workflowthe real, live n8n canvas — click to pan & zoom, or copy it onto your own
Loading the live workflow…
You'll see A comparison table with several papers appears and you can identify Stage 3 (AI extraction) in the execution log
Takeaway Chaining PubMed search, AI extraction and structuring lets n8n turn a simple query into a ready‑to‑use literature summary
Check Which specific node’s raw JSON output is inspected to verify the data before it is transformed into the final Markdown comparison table?
2.5Run your own literature review pipeline
You reuse the multi‑stage literature pipeline, customizing the PubMed query to your own field and evaluating the AI‑generated extraction for accuracy.
Generate a customised literature‑review workflow for a specific scientific question
TryIn n8n, open the imported "Session 8 — Multi-stage literature pipeline" workflow, set the PubMed query node to your chosen question (e.g., "CRISPR screens in primary T cells") with retmax=8, then click **Execute Workflow** and after it finishes, use **Export > CSV** on the AI extraction node’s output table.
Paste this into the n8n editor screen: edit the query field, run the workflow, and export the results. Verify that the exported CSV shows columns for method, sample size, key finding, and limitation before proceeding.
Go to Downloads and re‑import the file “Session 8 — Multi‑stage literature pipeline” into n8n
Open the first node and replace the PubMed query with your chosen question, keeping retmax at 8
Execute the workflow by clicking Run
Inspect the output table that lists method, sample size, key finding and limitation for each paper
Read three rows and record at least one instance where the AI mis‑identified a method, missed a limitation or fabricated a number
You'll see A table of extracted study details where you can identify one correct, one incorrect and one uncertain entry
Takeaway AI can auto‑extract study metadata but every result needs manual verification
Check When reviewing the generated table, what indicators should you record to assess whether the AI correctly extracted methods, limitations, or introduced errors?
3Templates 13 from n8n.io
Real workflows other people built and published, each with its live canvas.
Open one, read the nodes, copy what you need.
What it does. Two flows in one: (1) ingest documents into a vector store, (2) chat over them. The agent retrieves the top matching chunks and the model answers only from those.
When you'd use it. Your lab's SOPs, a folder of protocols, recent meeting notes — anything you want the AI to answer grounded in, not guess about. The 'hello world' of RAG.
🧵
Simple Memory — session key gotcha
The Simple Memory node needs a session key to separate different users' chat histories. When the Chat Trigger connects directly to the Agent you can leave the key mode on Connected Chat Trigger Node and n8n fills it in automatically. The moment you add any node between the Chat Trigger and the Agent (a Set node, a router, a webhook pre-processor), that auto-detection breaks — you must switch to Define below and enter: ={{ $('When chat message received').first().json.sessionId }}. This was the exact bug that broke Session 2 (cos2 voice-to-lab-ai) — intermediate nodes between trigger and memory made the key come through empty.
What it does. Upload PDFs through a form → chunk → embed → Pinecone. Chat side adds a Cohere reranker so the three chunks the model actually reads are the three best of twenty candidates, not just the three highest-scoring embeddings.
When you'd use it. Reading 30+ papers for a review and you want to chat over them. Reranking is the quiet step that separates 'cute demo' from 'I'd actually quote this in my thesis'.
What it does. Same RAG pattern, but the chat model runs on Ollama and the vectors sit in Qdrant — all on your own machine. No API keys, no cloud, no data leaving the building.
When you'd use it. When the documents contain unpublished data, patient information, or anything that can't leave institute hardware. Slower than the cloud version; zero data-leaving risk.
🧵
Simple Memory — session key gotcha
The Simple Memory node needs a session key to separate different users' chat histories. When the Chat Trigger connects directly to the Agent you can leave the key mode on Connected Chat Trigger Node and n8n fills it in automatically. The moment you add any node between the Chat Trigger and the Agent (a Set node, a router, a webhook pre-processor), that auto-detection breaks — you must switch to Define below and enter: ={{ $('When chat message received').first().json.sessionId }}. This was the exact bug that broke Session 2 (cos2 voice-to-lab-ai) — intermediate nodes between trigger and memory made the key come through empty.
What it does. Scrapes a whole YouTube channel via Apify, runs each transcript through an LLM to pull out entities and relationships, and MERGEs them into a Neo4j graph. A chat agent then answers questions against the graph instead of against a pile of text — "which videos talk about automation AND mention n8n?" is a graph query, not a keyword search.
When you'd use it. The clearest end-to-end GraphRAG example we found: ingest → entity extraction → graph write → query. Read it for the pattern, then point the ingestion end at your own corpus. Neo4j Aura's free tier holds ~200k nodes, which is plenty to try it.
What it does. Ingests documents into a graph instead of a vector store. The agent traverses the graph at query time — following entity → relation → entity hops — so answers can chain facts across papers, not just retrieve one chunk.
When you'd use it. When 'show me the chunk that mentions X' isn't enough and you need 'what connects X to Y'. Shines for mechanism-of-action questions, pathway walks, 'who cites whom'.
🧵
Simple Memory — session key gotcha
The Simple Memory node needs a session key to separate different users' chat histories. When the Chat Trigger connects directly to the Agent you can leave the key mode on Connected Chat Trigger Node and n8n fills it in automatically. The moment you add any node between the Chat Trigger and the Agent (a Set node, a router, a webhook pre-processor), that auto-detection breaks — you must switch to Define below and enter: ={{ $('When chat message received').first().json.sessionId }}. This was the exact bug that broke Session 2 (cos2 voice-to-lab-ai) — intermediate nodes between trigger and memory made the key come through empty.
What it does. Same idea as GraphRAG, but the graph is shaped by a hand-written ontology (your domain's types + allowed relations). The agent reasons inside those rules, which stops it from inventing relationships the domain doesn't permit.
When you'd use it. Any domain with a real schema — medical, legal, regulatory, experimental protocols. When 'close enough' isn't good enough and the agent must respect that Gene → encodes → Protein but never Gene → authored-by → Protein.
What it does. Mistral's OCR turns scanned PDFs (figures, tables, handwritten scans) into clean structured text. An AI research agent then reads the output and writes a synthesis. Handles what plain PDF-text extraction quietly fails on.
When you'd use it. Old scanned papers, historical lab notebooks, conference posters, anything where the PDF is actually an image. Also when tables matter — Mistral OCR preserves structure plain extraction wipes out.
What it does. Watches a Drive folder, extracts text + tables from every new PDF or DOCX, runs OCR on the scanned ones, and pings Slack with a summary. One pipeline that handles both native and scanned documents.
When you'd use it. A shared 'to-read' folder for the lab — new papers, SOPs, meeting notes — where you want a one-line summary in Slack so nothing slips. Same shape works for Mattermost.
What it does. Submit a paper or article URL in a form; the workflow scrapes it, runs a research-analysis agent over the text, passes the output through a second validation agent, and writes a formatted research document plus a log row in Google Sheets.
When you'd use it. The interesting part is the second agent: one writes, another checks the first one's work before anything is filed. That generator/reviewer split is the single most useful habit to steal from this template. Note it uses community nodes — self-hosted n8n only, which is what you are running.
🧵
Simple Memory — session key gotcha
The Simple Memory node needs a session key to separate different users' chat histories. When the Chat Trigger connects directly to the Agent you can leave the key mode on Connected Chat Trigger Node and n8n fills it in automatically. The moment you add any node between the Chat Trigger and the Agent (a Set node, a router, a webhook pre-processor), that auto-detection breaks — you must switch to Define below and enter: ={{ $('When chat message received').first().json.sessionId }}. This was the exact bug that broke Session 2 (cos2 voice-to-lab-ai) — intermediate nodes between trigger and memory made the key come through empty.
What it does. Pulls papers from a Google Drive folder, extracts the text, and has LLM agents score each one against explicit inclusion/exclusion criteria. Included papers go to a Qdrant vector store with Gemini embeddings; excluded ones go to a separate folder. Every decision, with its justification, is logged to Airtable.
When you'd use it. When the review has to be defensible, not just fast — the Airtable log is what turns "the AI picked these" into a PRISMA-compliant audit trail you can put in a methods section. The closest thing in this list to lessons 08 and 09.
What it does. Give it an arXiv ID or URL → downloads the PDF → an AI Agent writes a tight structured summary (contribution, method, results, limits). Handles the maths-heavy pre-prints arXiv is full of.
When you'd use it. Keeping up with a fast-moving ML / stats / comp-bio area where ten new pre-prints land every Friday. Paired with a schedule trigger + a Mattermost post, this is your personal 'paper of the day' bot.
What it does. Every morning: pulls new arXiv papers on your topics, embeds them into a small RAG store, and writes one Notion page per paper with summary + key quotes + 'why you'd care'. Your RAG index grows on its own.
When you'd use it. When you want a durable, searchable archive of what you're reading — not just a firehose. A month in, you can chat over every paper you've ever opened, grounded in the exact text.
What it does. Give it a question; the agent plans, searches, reads, re-queries, and iterates until it has a multi-page, cited answer. Same shape as the paid 'deep research' products — but running in your own n8n.
When you'd use it. The open-ended questions that take a human half a day — 'what's the current consensus on X', 'who are the serious labs working on Y', 'what are the three strongest arguments against Z'. Run it overnight; read the report in the morning.
🧵
Simple Memory — session key gotcha
The Simple Memory node needs a session key to separate different users' chat histories. When the Chat Trigger connects directly to the Agent you can leave the key mode on Connected Chat Trigger Node and n8n fills it in automatically. The moment you add any node between the Chat Trigger and the Agent (a Set node, a router, a webhook pre-processor), that auto-detection breaks — you must switch to Define below and enter: ={{ $('When chat message received').first().json.sessionId }}. This was the exact bug that broke Session 2 (cos2 voice-to-lab-ai) — intermediate nodes between trigger and memory made the key come through empty.
Different source formats (e.g., Wikipedia vs. PubMed) affect how well the model can answer, revealing strengths and limits of the fetch-and-stuff method
By attaching an OpenAI chat model to the AI Agent node and configuring system prompts with specific output formatting, you can force the LLM to return only the desired list items, reducing token usage.
Adding Simple Memory to an AI Agent stores the last N interactions, allowing follow‑up questions to inherit prior context without re‑prompting the user.
Connecting a Google Sheets "Append Row" node to the form trigger writes each submission as a new spreadsheet row, enabling persistent storage without code.
LLM spits out random text instead of a neat ranked list
Including one or two formatted examples inside the system prompt (one‑shot) guides the model to mimic the desired output style, striking a balance between zero‑shot and few‑shot prompting.
Unsure which branch runs first in a multi‑branch workflow
n8n runs each branch sequentially based on canvas position: top‑most to bottom‑most, left‑most when heights match. Understanding this lets you design workflows without manually reordering execution.
Community nodes are third‑party packages that add new actions to n8n without writing code. Installing them from the npm registry lets you use pre‑built integrations like Amplify or MCP directly in your workflows.
Want to handle each automation platform separately
A Manual Trigger lets you start a workflow on demand, while the SplitInBatches node can turn an input array into separate items for downstream processing.
Want to keep workflow info without extra spreadsheets
n8n Data Tables act like built‑in spreadsheets, allowing you to read/write rows directly from workflows, eliminating extra API calls to Google Sheets or Airtable.
A webhook node creates a public URL that can be called by any service to start a workflow. It acts as the entry point, similar to a door opening when someone knocks.
Enabling ‘Continue On Fail’ on a node prevents the entire workflow from stopping when that node encounters an error, allowing later nodes to handle fallback logic.
Uses n8n’s Gmail node to watch new emails, extracts keywords, and applies labels like accounting, personal, or meetings via the Gmail API. Works by matching subject/body patterns to predefined label rules.
Combines a Webhook trigger (for a quote form), a Twilio node to place a call, and a short delay so the call reaches you within seconds. The workflow bridges form data to a phone call automatically.
Need to copy Google Maps listings to a sheet and email them
Uses an HTTP Request node to query Google Maps search results, parses JSON with a Function node, writes rows to Google Sheets, then loops through each row with an Email Send node for cold outreach. Automates lead gathering from maps.
Leverages a Web Scraping node (or HTTP Request + HTML Extract) to search LinkedIn for SEO roles in Canada, stores results in Google Sheets, then runs a Function node that rates each posting on criteria (salary, seniority, remote). Provides a ranked list for focused applications.
Connects an HTTP Trigger (or file upload node) to a OpenAI node that runs a prompt asking ChatGPT to list each line item. The result is parsed and written into Google Sheets, turning a photo of a receipt into structured data.
Want to add meetings or fire off emails with a chat message
Combines an Email Trigger, a Schedule Trigger, and OpenAI nodes to interpret natural‑language commands (e.g., “schedule meeting with John tomorrow at 3pm”), then uses Google Calendar and Gmail nodes to create events or send emails. Provides a conversational interface for daily tasks.
Uses n8n’s Webhook node as an endpoint for a front‑end chat widget. The webhook forwards user messages to OpenAI, which returns suggested meeting times; those are then passed to Google Calendar to create events and to Gmail to send confirmations.
Integrates a Webhook (receiving invoice details), an OpenAI node that formats the data into a PDF using a template, then uploads the PDF to Google Drive and emails it via Gmail. Automates end‑to‑end invoicing without manual paperwork.
By using n8n's Airtable node as a trigger you can poll a base at a set interval (e.g., every minute) and fire the workflow whenever a row’s ‘Last Modified’ field changes. This creates real‑time notifications without writing code.
Turn Airtable record data into a ready‑to‑send email
The Google Gemini node can be used as a Large Language Model step that receives data from Airtable and returns structured JSON containing an email subject and body. Using the “Structured Output” option forces Gemini to output a predictable schema, making it easy to map into later nodes.
n8n’s Gmail node can send emails using OAuth credentials. By inserting expressions that reference the Gemini output, you can dynamically fill the email subject and HTML/text body, achieving fully automated personalized messages.
A workflow remains inactive until it is published; publishing activates the polling interval for triggers like Airtable. Once live, n8n will repeatedly check the source and execute downstream nodes automatically.
The best 'where do I even start' video. Watch before opening n8n so you learn the fundamentals in the right order instead of jumping straight to agents.
The architecture question you'll face in your own projects. Cole gives a decision framework, not hype — useful for anyone planning to move beyond the course into real research automation.
The reference course. Don't watch end-to-end — use it as a lookup when you hit a specific node or pattern you haven't seen before. Bookmark and return.
Nick's guide is the one to watch when 'it works in test but breaks in prod'. Heavy on error handling, retries, and the un-glamorous parts of automation engineering.
The canonical 'RAG in n8n' build from one of the most respected AI-agent educators. Watch when you want your agent to answer from your own documents, not just general knowledge.
8FAQ 9
What is n8n and what can I use it for?
n8n is a visual workflow automation tool that connects different apps and services so they can pass information between each other automatically, without you writing code. You build automations by placing 'nodes' — each representing one app or action — on a canvas and drawing connections between them. A researcher could, for example, set up n8n to automatically collect papers from PubMed, summarize them with AI, and write the results to a Google Sheet — all triggered on a schedule.
Can I run a workflow on a schedule — for example, every day at 9 AM?
Yes. Use the Schedule Trigger node as the first node in your workflow. You can choose simple intervals (every X minutes, hours, or days) without any technical knowledge, or use a cron expression for precise schedules like 'every weekday at 9 AM'. The node also has a timezone setting so your schedule reflects your local time rather than the server's timezone.
How do I connect n8n to an app like Google Sheets or Gmail?
Each app integration in n8n requires a 'credential' — the login or access key that lets n8n talk to that service on your behalf. When you add a node for a service, n8n prompts you to add a credential. For Google services you click 'Connect my account' and log in via OAuth (the familiar 'Sign in with Google' popup). For other services you paste in an API key copied from that service's settings page. Credentials are stored securely and reused across all your workflows.
Are there ready-made workflows I can start from instead of building from scratch?
Yes — n8n has a public template library with thousands of community-built workflows covering a huge range of use cases. You can browse by category, preview what each workflow does, and load one directly into your editor with a single click. Templates are an excellent way to learn how experienced users structure their automations and a much faster starting point than building from scratch.
Do I need to know how to code to use n8n?
No — most workflows can be built entirely by clicking and dragging without writing a single line of code. However, n8n does have a steeper learning curve than some alternatives like Zapier: features such as expressions, error handling, and connecting AI tools require some patience and self-study. Non-coders who are willing to practice consistently report becoming comfortable with the interface after building a few simple workflows.
What is a node in n8n?
A node is a single building block in your workflow — it represents one action, service, or piece of logic. For example, a 'Gmail' node can send an email, a 'Google Sheets' node can write a row of data, and a 'Code' node lets advanced users add custom logic. Trigger nodes are a special type that start the whole workflow when a specific event happens (like a new file being uploaded or a schedule being reached).
How does n8n compare to Zapier or Make — which is easier for a complete beginner?
For someone with no technical background, Zapier is the easiest of the three: it uses a step-by-step wizard and requires no technical decisions. Make sits in the middle with a visual canvas. n8n has the steepest learning curve because it uses expressions, webhooks, and a more flexible but complex interface. That said, n8n is the most powerful and cheapest at scale, and its growing library of AI tools and templates is closing the gap.
What is a webhook and why does n8n use it?
A webhook is like a doorbell: instead of your workflow constantly checking whether something new has happened, an external app rings the bell (sends a message) the instant an event occurs. In n8n, the Webhook node gives your workflow a unique URL address; when another service sends data to that address, your workflow starts immediately. This is more efficient than scheduled polling and enables real-time responses — for example, triggering a workflow the moment someone submits a form.
How do I get my workflow to actually run automatically — it only works when I click 'Test'?
You need to activate the workflow. In the top-right corner of the editor there is an 'Inactive / Active' toggle switch. While it is set to Inactive, the workflow will not respond to real triggers — it only runs when you manually click Test. Switch the toggle to Active (it turns orange) and save, and your workflow will begin listening for events and running on its own.
9Glossary 20 terms
Show the 20 terms
n8n
Workflow
A saved sequence of connected steps (nodes) that n8n runs automatically to move or transform data between apps.
Node
A single building block in a workflow — each node performs one action, such as sending an email, filtering data, or calling an API.
Trigger node
A special node that sits at the start of a workflow and decides when it runs — for example, on a schedule, when a form is submitted, or when another app sends a signal.
Action node
A node that does something in an external service — such as creating a row in Google Sheets, sending a Slack message, or reading an email.
Core node
A built-in utility node that handles data processing or flow control without connecting to an external service — examples include IF, Filter, Merge, and Code.
Connection
The arrow drawn between two nodes on the canvas that tells n8n to pass data from one node to the next when the workflow runs.
Canvas
The visual drag-and-drop workspace inside n8n where you build a workflow by placing and connecting nodes.
Execution
One complete run of a workflow — n8n records what happened at each node so you can inspect inputs, outputs, and any errors afterward.
Credentials
Securely stored login details (such as API keys or passwords) that let n8n connect to an external service on your behalf without exposing secrets inside the workflow.
Webhook
A URL that n8n creates for you so that an outside app can instantly start your workflow by sending data to that address.
Expression
A small piece of JavaScript written inside double curly braces ({{ }}) that lets you pull in data from a previous node or do a quick calculation instead of typing a fixed value.
Item
A single unit of data travelling through a workflow — for example, one email, one spreadsheet row, or one API result.
Schedule trigger
A trigger node that starts a workflow automatically at a set time or repeating interval, similar to a calendar alarm.
IF node
A core node that checks a condition and sends each data item down one of two paths — True or False — so different actions can happen depending on the data.
Sub-workflow
A separate workflow that another workflow calls like a reusable function, helping you keep complex automations organised and avoid repeating the same steps.
Error handling
A set of features in n8n — including dedicated error workflows and the Stop And Error node — that let you define what should happen if a node fails instead of silently stopping.
Data mapping
The act of telling a node where to find its input by dragging a field from a previous node's output onto the current node's input — no code required.
Sticky note
A text annotation you can place anywhere on the canvas to explain what part of a workflow does, without affecting how it runs.
Template
A pre-built workflow shared by the n8n community that you can import and adapt instead of building from scratch.
Manual execution
Running a workflow by clicking the Execute button yourself, used for testing before you switch the workflow on for automatic production runs.
n8n
