The N8N Glossary

Navigating the world of workflow automation can sometimes feel like learning a new language, especially with powerful platforms like N8N. Understanding the specific terminology used is crucial for unlocking the full potential of automating your tasks, integrating applications, and even leveraging artificial intelligence. Whether you’re just starting out or looking to deepen your expertise, getting comfortable with the core concepts and vocabulary is the first step towards building efficient and sophisticated automations. This guide aims to demystify the key terms you’ll encounter, providing a comprehensive reference – think of it as your essential N8N Glossary – to help you build, manage, and optimize your workflows with confidence. Let’s dive into the building blocks and advanced features that make N8N a versatile tool for connecting your digital world.

Core N8N Concepts: The Building Blocks of Automation

At the heart of N8N are fundamental concepts that define how automations are structured and executed. Understanding these is key to designing effective processes.

Workflow (n8n)

An n8n workflow is the complete automated process you build. It’s essentially a sequence or collection of interconnected nodes designed to perform a specific task or series of tasks. Workflows are the core construct in n8n, representing everything from simple data transfers between two apps to complex, multi-step processes involving conditional logic, data transformation, and interactions with numerous services. They typically start with a trigger and then proceed through a series of action nodes. Think of a workflow as the entire automated recipe you create.

Node (n8n)

Nodes are the individual steps or building blocks within an n8n workflow. Each node performs a specific function. There are different types of nodes: some trigger the workflow’s execution, others fetch data from services, manipulate data, send data to other services, implement logical conditions (like IF statements), or connect to external applications via APIs. You visually connect nodes on the canvas to define the sequence of operations in your workflow. Every action, trigger, or logical step in your automation is represented by a node.

Trigger Node (n8n)

A trigger node is a special type of node that initiates the execution of a workflow. It defines the starting condition. This condition could be a schedule (e.g., run every hour), an incoming webhook (data sent from another application), a manual start, or an event occurring in a specific application (like a new email arriving or a form submission). Every workflow intended for automatic execution needs at least one trigger node to tell it when to begin its process. Without a trigger, the workflow would need to be started manually each time.

N8N Interface and Development Tools

These terms relate to the environment where you build and test your N8N workflows, along with features designed to streamline the development process.

Editor (n8n)

The n8n editor UI (User Interface) is the web-based environment where you create, modify, and manage your workflows. It’s the primary interface you interact with. The main part of the editor is the canvas, but it also includes side panels and top menus for accessing credentials, browsing workflow templates, managing variables, viewing execution history, and configuring instance settings. It provides a visual way to build automations without necessarily writing extensive code.

Canvas (n8n)

The canvas is the central workspace within the n8n editor UI. It’s the visual area where you actually build your workflows. You drag and drop nodes onto the canvas, configure their settings, and draw connections between them to define the flow of data and execution logic. It provides a graphical representation of your entire automated process, making it easier to understand and modify the workflow’s structure.

Data Pinning (n8n)

Data pinning is a highly useful development feature in n8n. It allows you to “freeze” or save the output data of a specific node during the workflow building phase. When data is pinned, subsequent nodes will use this saved data instead of executing the pinned node again to fetch live data. This is extremely helpful because it lets you develop and test the later parts of your workflow using consistent, predictable data without repeatedly making requests to external services (which might have rate limits or costs). When the workflow runs in production (i.e., when activated), pinned data is ignored, and fresh data is fetched on each execution.

Expression (n8n)

In n8n, expressions are snippets of JavaScript code used to dynamically set the values of node parameters. Instead of typing a fixed, static value into a node’s configuration field (like a specific email address or message text), you can use an expression to calculate or retrieve that value based on data from previous nodes, environment variables, or other dynamic information. Expressions provide immense flexibility, allowing workflows to adapt based on the data they process. They use a specific syntax (often involving `{{ }}`) to access data items from preceding nodes.

Template (n8n)

n8n templates are pre-built, ready-to-use workflows created either by the n8n team or the user community. They offer a starting point for common automation tasks. You can browse a library of templates directly within n8n and import them into your instance. While templates provide a functional structure, you’ll typically need to configure them with your own credentials and potentially adjust some settings to perfectly match your specific requirements and applications.

Connectivity and Authentication

Connecting N8N to other applications and services securely is fundamental. These terms cover how N8N interacts with external systems.

API

API stands for Application Programming Interface. In essence, an API is a set of rules and protocols that allows different software applications to communicate with each other. Services expose APIs to grant programmatic access to their features and data, bypassing the need for manual interaction through a user interface (like a website or app). N8N heavily relies on APIs to connect with various services (like Google Sheets, Slack, CRMs, etc.), enabling it to fetch data, send commands, and trigger actions within those external systems as part of an automated workflow.

Credential (n8n)

In n8n, credentials are used to securely store the authentication information needed to connect to external apps and services. This information could be a username and password combination, an API key, OAuth2 client secrets, or other authentication tokens. Once you create and save credentials for a specific service (e.g., your Google account or your Mailchimp API key), you can easily select and use these credentials within the corresponding n8n nodes without having to re-enter the sensitive details each time. This centralizes and secures your connection details.

Advanced Node Structures

Some N8N nodes are more complex than simple action nodes, grouping functionality together.

Cluster Node (n8n)

In n8n, a cluster node isn’t just a single node but a structured group of nodes designed to work together to provide a specific set of related functionalities within a workflow. They simplify the canvas by encapsulating complex operations. A cluster node always consists of a central ‘root node’ and one or more ‘sub nodes’ attached to it.

Root Node (n8n)

Within an n8n cluster node structure, the root node serves as the main entry point and defines the core purpose or primary functionality of the cluster. It’s the central piece that other related nodes (sub nodes) connect to. For example, in an AI Agent cluster, the root node might handle the main agent logic.

Sub Node (n8n)

Sub nodes are the components that attach to a root node within an n8n cluster node. They extend the functionality of the root node by providing access to specific services, resources, or dedicated processing capabilities needed by the cluster. For instance, in an AI Agent cluster, sub nodes might represent specific tools the agent can use or the memory system it accesses.

Artificial Intelligence (AI) Concepts in N8N

N8N integrates powerful AI capabilities. Understanding these terms is vital for leveraging AI within your automations.

Large Language Model (LLM)

Large Language Models, commonly known as LLMs, are a type of artificial intelligence model specifically trained on vast amounts of text and code data. This extensive training allows them to understand, generate, and process human language (natural language processing or NLP) with remarkable proficiency. Examples include models like OpenAI’s GPT series or Google’s Gemini. In n8n, LLMs are the engines behind AI nodes, enabling tasks like text summarization, translation, content generation, classification, and powering conversational AI agents.

AI Agent

AI agents represent a sophisticated use of AI within n8n. They are AI systems designed not just to respond to a single prompt, but to potentially make decisions, interact with tools, and perform multi-step tasks to fulfill a user’s request. They utilize LLMs to understand the request and then intelligently decide how to use available resources (like specific nodes configured as ‘tools’ or internal/external data) to achieve the goal. Critically, AI agents can often maintain context across multiple interactions using AI memory.

AI Chain

AI chains in n8n provide a way to structure interactions with LLMs and other resources in a defined sequence of calls. You might use a chain to first summarize a document using an LLM, then translate the summary with another LLM call, and finally format the output. Unlike AI agents, AI chains in n8n typically operate without persistent memory; each step in the chain processes its input independently without recalling the context from previous steps within that specific execution chain (though they can use the output of the previous step as input for the next).

AI Tool

In the context of AI agents within n8n, a tool is an external resource or specific functionality that the AI agent is explicitly given permission and instructions to use when processing a request. This could be another n8n node (like one that searches a database, sends an email, or fetches web content) or a connection to an external API. The LLM powering the agent analyzes the user’s request and determines if using one of its available tools would help in formulating the best response or completing the required action.

AI Memory

AI memory refers to the capability of an AI system, particularly AI agents in n8n, to retain context and information across multiple interactions or messages within a single conversation or session. This allows for more natural, ongoing dialogues where the AI can refer back to what was previously discussed, rather than treating each message as a completely new and isolated request. In n8n, AI agent nodes can be configured to use memory, while AI chains currently do not have this built-in persistence.

AI Embedding

Embeddings are a fundamental concept in modern AI, particularly for processing complex data like text or images. An embedding is a numerical representation of data, typically in the form of a high-dimensional vector (a list of numbers). AI models, especially LLMs, create embeddings to capture the semantic meaning and relationships within the data. For instance, words or sentences with similar meanings will have mathematically similar embedding vectors. These numerical representations are much easier for AI algorithms to process than raw text.

AI Vector Store

Vector stores, also known as vector databases, are specialized databases optimized for storing and efficiently searching through large quantities of embeddings (the numerical vector representations of data). Because embeddings capture semantic meaning, searching a vector store allows you to find data based on conceptual similarity rather than just exact keyword matches. In n8n, you might use a vector store to provide relevant context or knowledge to an AI agent by searching for information similar to the user’s query.

LangChain

LangChain is a popular open-source framework designed to simplify the development of applications powered by large language models (LLMs). It provides standardized components and structures for interacting with various LLMs, managing prompts, connecting to data sources (like vector stores), and chaining together different AI operations to build complex applications like chatbots, question-answering systems, and agents. N8N incorporates concepts and nodes inspired by or directly compatible with LangChain principles, making it easier to build sophisticated AI-driven workflows.

N8N Instance and Workflow Management

These terms relate to managing your N8N setup, organizing resources, and evaluating performance.

Project (n8n)

Introduced in newer versions of n8n, projects provide a way to organize your work by grouping related workflows, credentials, and variables together. This is particularly useful for teams or individuals managing multiple distinct automation initiatives. Projects help keep resources separate and compartmentalized, making it easier to manage access, collaborate effectively, and maintain clarity across different sets of automations.

Evaluation (n8n)

Evaluation in n8n is a feature primarily focused on understanding and improving workflow performance, especially for AI-driven workflows where output quality can be subjective. It allows you to tag specific workflow executions, store their history (inputs and outputs), and compare them against new executions or defined benchmarks. This helps you track how changes to your prompts, models, or workflow logic affect the results over time, enabling iterative improvement and quality assurance.

Entitlement (n8n)

In the context of n8n’s commercial offerings or cloud plans, entitlements are what grant an n8n instance access to specific plan-restricted features for a set duration. They essentially unlock premium capabilities.
Floating entitlements are a flexible type of entitlement where you have a pool of licenses that can be assigned, unassigned, and reassigned among different n8n instances you manage. This allows you to dynamically allocate premium features to the instances that need them most at any given time.

Conclusion: Mastering the Language of Automation

Understanding this N8N Glossary is your key to truly harnessing the power of workflow automation. From the basic building blocks like nodes and workflows to the advanced capabilities offered by AI agents, vector stores, and expressions, each term represents a concept or tool that can help you connect applications, manipulate data, and automate complex processes more effectively. As you continue to build and explore within the n8n editor, refer back to these definitions whenever you encounter an unfamiliar term. The more fluent you become in this vocabulary, the more intricate and valuable the automations you’ll be able to create, saving you time and unlocking new possibilities.