What is n8n?

n8n is a workflow automation platform that connects your applications and automates repetitive tasks using a visual node-based builder. Founded in 2019 by Jan Oberhauser, the Berlin-based company has carved out a distinctive position in the automation space by offering something most competitors don’t: the ability to self-host on your own infrastructure whilst maintaining the simplicity of a visual workflow builder. The name itself, pronounced “n-eight-n” or “nodemation”, reflects the platform’s foundation built around Node.js and its node-based visual approach. What separates n8n from the crowded field of automation tools is its licensing model. Rather than being purely open source or entirely proprietary, it operates under what it calls a fair-code licence, specifically the Sustainable Use Licence that balances transparency with commercial viability. This means you can view, modify and self-host the code for internal business use without restriction, but reselling it as a commercial service requires a paid licence. The distinction matters because it affects not just how you can use the platform, but the entire ecosystem that’s grown around it.

The platform’s growth trajectory tells you something about demand for this particular combination of features. With over 156,000 GitHub stars, more than 100 million Docker pulls and a community exceeding 230,000 users, n8n has demonstrated that technical teams want control over their automation infrastructure without sacrificing the speed of visual development. The company raised a Series B in March 2025, reaching a valuation around €250 million, suggesting investors believe this model has legs. What’s particularly interesting about n8n’s positioning is how it targets technical teams specifically, rather than trying to serve every possible user type. The interface assumes a certain level of comfort with concepts like APIs, webhooks and data transformation, which means it can offer more power without oversimplifying to the point of uselessness. This is automation for people who know what they’re building, not a toy for creating simple email notifications.

The core architecture uses a visual canvas where you drag nodes representing different applications or functions and connect them to define your workflow logic. Each node performs a specific action: triggering on events, fetching data from services, transforming information, making decisions based on conditions, or executing code when you need custom behaviour. The platform supports over 400 integrations with services ranging from Google Workspace and Slack to databases, AI providers and niche business tools. Unlike some competitors that limit advanced features to premium tiers, n8n provides its full visual builder in the free self-hosted version, making capabilities like branching logic, loops and custom code accessible from the start. This architectural choice reflects a philosophy: the value isn’t in restricting features but in offering managed hosting and enterprise capabilities for organisations that need them.

How Fair-Code Licensing Creates Sustainable Development Without Sacrificing Transparency

The Sustainable Use Licence that n8n introduced in March 2022 represents a pragmatic response to a problem that’s plagued many open source projects: large cloud providers taking freely available code, wrapping it in managed services and capturing the commercial value without contributing meaningfully back to the original developers. The licence permits you to use, modify, create derivative works and redistribute n8n freely for internal business purposes or non-commercial use. You can provide consulting services around n8n, build custom nodes for integrations and support clients in implementing workflows. What you cannot do without a commercial licence is host n8n as a service that others pay to access, or embed it in a product whose value substantially depends on n8n’s functionality whilst charging for that product.

This licensing structure has proven controversial in some circles because it doesn’t meet the Open Source Initiative‘s definition of open source, which prohibits restrictions on commercial use. The n8n team acknowledges this directly, coining the term “fair-code” to describe their approach and explicitly stating they don’t call themselves open source. The transparency matters because users know exactly what they’re getting: source code they can inspect and modify, freedom to self-host and extend for their own purposes, but restrictions that prevent others from competing directly using the same codebase. Whether this represents a sustainable middle ground between pure open source and proprietary software remains an open question, but the model has attracted sufficient community contribution and commercial traction to validate it as viable.

The practical implications affect how organisations can use n8n. A company with 10,000 employees can run it internally to automate their processes without paying anything beyond infrastructure costs. Consultants can build workflows and custom integrations for clients. What crosses the line is taking n8n, hosting it and charging other companies for access, or building a product where n8n provides core functionality and selling that product. These restrictions aim to ensure that commercial value derived from n8n flows back to support its continued development. The model acknowledges reality: maintaining professional-grade software at scale requires consistent funding, and relying purely on donations or goodwill rarely generates sufficient resources. By preserving commercial restrictions whilst keeping source code visible and modifiable, n8n attempts to thread the needle between community benefit and financial sustainability.

Where AI Integration Moves Beyond Simple API Calls to Autonomous Agent Workflows

n8n’s approach to AI integration deserves attention because it reflects a deeper architectural commitment rather than superficial feature additions. Built on LangChain, the framework for developing applications powered by large language models, n8n provides native AI capabilities that let you construct sophisticated agent workflows directly in the visual canvas. The AI Agent node functions as a root node in workflows, giving the agent access to tools and enabling it to make decisions about which to invoke based on context. This isn’t just about generating text; it’s about creating systems that can reason about problems, access multiple data sources, determine appropriate actions and adapt their behaviour based on results.

The platform supports integration with multiple LLM providers including OpenAI, Anthropic Claude, Google Gemini and Azure OpenAI, allowing you to choose models based on capability requirements, cost considerations or data sovereignty needs. Beyond simple chat interfaces, n8n enables retrieval-augmented generation workflows where you can ingest documents from various sources, chunk them appropriately, create embeddings, store them in vector databases and build agents that retrieve relevant context before generating responses. This matters because it transforms generic AI capabilities into systems grounded in your specific data and business logic. An AI agent in n8n can pull information from your CRM, search through documentation stored in Confluence, retrieve data from internal databases and synthesise responses that reflect your organisation’s actual knowledge rather than generic training data.

The LangChain Code node takes this further by letting developers write custom JavaScript to implement agent behaviour that goes beyond what standard nodes provide. This becomes essential when you need multi-agent systems where specialised agents collaborate on complex tasks, or when you want to dynamically switch between different LLM providers based on response quality or cost. Because n8n’s AI nodes are built on LangChain, you can integrate with Langsmith, LangChain’s monitoring platform, to gain visibility into agent operations, debug decision-making processes and optimise performance. The combination of visual building for standard workflows and code for custom behaviour means you’re not forced to choose between simplicity and power. You can construct most of your automation visually whilst dropping into code precisely when you need capabilities that standard nodes don’t provide. This flexibility matters when building production AI systems that need to be both maintainable and sophisticated.

Why Self-Hosting Capabilities Matter When Data Control and Cost Economics Are Strategic Concerns

n8n offers two deployment paths: a managed cloud service and a self-hosted option, each serving different priorities. The cloud offering handles infrastructure, scaling, updates and security, making it the fastest path to implementation for teams that value operational simplicity. Pricing starts with a free tier for up to 2,500 executions monthly, then moves to paid plans based on execution volume. For organisations with modest automation needs or those wanting to test before committing to infrastructure, cloud hosting removes friction. You’re paying for convenience and letting n8n handle operational concerns whilst you focus on building workflows.

Self-hosting appeals to different calculations. When you run n8n on your own infrastructure, you pay only for servers, not per execution. This economic model becomes compelling at scale: if you’re running tens or hundreds of thousands of workflow executions monthly, the unit costs of cloud services compound quickly. Self-hosting transforms variable costs into predictable infrastructure expenses. Beyond economics, self-hosting addresses data sovereignty requirements that regulated industries face. When your workflows process customer information, financial records or health data, keeping that data within your own infrastructure isn’t optional. Many organisations operate under regulations that specify where data can reside and who can access it. Self-hosting lets you maintain compliance whilst still benefiting from sophisticated automation capabilities.

The installation process uses Docker, which has become standard for containerised deployments. You create a volume for persistent data storage, specify environment variables for encryption keys and configuration, then launch the container. This approach makes deployment straightforward for teams with basic DevOps capabilities whilst providing flexibility to integrate with existing infrastructure, monitoring systems and security policies. The self-hosted version includes the full workflow builder and all standard integrations. Enterprise features like single sign-on, advanced role-based access control, audit logs and log streaming to third-party services require a Business or Enterprise licence, but the core automation capabilities remain unrestricted. You’re not hitting artificial limits on workflow complexity or execution volume; the only constraints are the resources of your infrastructure.

For organisations evaluating which deployment model fits their needs, the decision hinges on several factors. Cloud hosting works well when execution volumes remain modest, operational simplicity matters more than per-execution costs, or when you lack dedicated infrastructure teams. Self-hosting becomes compelling when you’re running high volumes that make cloud pricing expensive, when regulatory requirements mandate data sovereignty, when you need to customise deeply beyond what managed services allow, or when you want independence from vendor pricing or feature decisions. The ability to choose between these models without being locked into one represents a strategic flexibility that proprietary platforms cannot match. You can start with cloud hosting to validate your workflows, then transition to self-hosting as your automation matures and execution volumes grow.

How Visual Node Architecture Balances Accessibility with Technical Depth

The node-based visual interface that n8n provides serves a specific purpose: making workflow logic visible and debuggable whilst maintaining enough sophistication for complex automation. Each node represents a discrete unit of functionality, whether that’s a trigger that starts the workflow, an action that performs an operation, or logic that makes decisions. You connect nodes to define the flow of data and control, creating a graph that represents your automation’s behaviour. This visualisation matters because it makes implicit logic explicit. When troubleshooting why a workflow behaves unexpectedly, you can see exactly which paths data takes, inspect outputs at each step and identify where transformations occur. Compare this to code-based automation where logic might be scattered across multiple files and understanding behaviour requires reading through functions.

The platform doesn’t treat visual building and code as mutually exclusive. When standard nodes provide the functionality you need, you use them. When you need custom behaviour, the Code node lets you write JavaScript or Python to transform data, implement complex logic or interact with APIs that don’t have dedicated integrations. Function nodes provide inline expressions for simple transformations without writing full code blocks. This layered approach means non-technical users can build straightforward workflows using only standard nodes, whilst developers can drop into code precisely when they need capabilities beyond what’s provided. The transition between visual and code is smooth rather than forcing an all-or-nothing choice.

n8n supports workflow patterns that matter for real automation: branching with IF nodes that route data down different paths based on conditions, loops that process arrays of items iteratively, merging that combines data from multiple sources, error handling that catches failures and implements retry logic, and webhook triggers that make workflows callable from external systems. The platform includes over 900 pre-built templates that demonstrate common patterns and provide starting points rather than requiring everything to be built from scratch. These templates cover use cases from syncing data between applications to automating customer support responses to processing data pipelines. The value isn’t just in the templates themselves but in seeing working examples that demonstrate how to combine nodes effectively.

The workflow execution model in n8n processes items through the graph, with each node receiving input, performing its operation and passing output to connected nodes. You can run workflows manually for testing, trigger them via webhooks for real-time responses, schedule them with cron expressions for recurring tasks, or start them based on events from connected applications. The execution view shows you exactly what data flowed through each node, making it straightforward to verify that transformations happen as expected and debug when they don’t. This transparency in execution is something that many competing platforms obscure, treating workflows as black boxes that either work or fail without visibility into intermediate states.

Where the Developer Experience Separates Platforms That Engineers Tolerate from Ones They Choose

The technical architecture underneath n8n reflects decisions that affect the developer experience profoundly. Built with Node.js and TypeScript, the codebase uses modern development practices that make it approachable for JavaScript developers wanting to contribute or build custom functionality. The choice of TypeScript provides type safety that catches errors during development rather than runtime, making the platform more reliable and the integration development process less error-prone. Custom nodes are npm packages, meaning you work with familiar tooling rather than learning proprietary systems. You can publish nodes to npm, making them available to the broader community, or keep them internal if they integrate with private systems.

The platform’s architecture as a monorepo managed with Nx keeps related code organised whilst sharing common elements efficiently. This structure matters for maintainability and contribution because it prevents the codebase from fragmenting into disconnected pieces that become difficult to reason about. The separation between the workflow engine, server, user interface and shared types means you can understand components in isolation whilst seeing how they compose into the complete system. For developers building custom integrations or extending functionality, this architectural clarity reduces the cognitive load of navigating the codebase and understanding where changes need to happen.

n8n’s community has contributed significantly to the integration catalogue, with community-built nodes expanding coverage beyond what the core team provides. The contribution process benefits from comprehensive documentation, responsive maintainers who review pull requests and provide feedback, and a genuine willingness to accept external contributions rather than maintaining tight control. The health of an open source ecosystem often correlates with how welcoming it is to outside developers. Too many projects claim to welcome contributions whilst making the process so onerous or opaque that few bother trying. n8n’s community activity suggests they’ve avoided this trap, though the fair-code licence means contributors understand their work becomes part of a commercially-restricted codebase rather than purely open source.

The developer experience extends to debugging and observability. Workflow execution logs show exactly what happened at each step, including data payloads and error messages when things fail. You can inspect node outputs directly in the editor, making it straightforward to verify that data transformations produce expected results. For self-hosted instances, you can integrate with external monitoring systems to track workflow health, execution times and error rates. The platform includes OpenTelemetry instrumentation, providing standardised telemetry data that works with various observability tools. These details might seem mundane, but they determine whether you can operate workflows reliably in production or end up with an undebuggable system that teams eventually abandon because troubleshooting takes longer than the automation saves.

What n8n’s Evolution Reveals About the Convergence of Automation and AI

The platform’s trajectory from basic workflow automation to AI-native architecture demonstrates how the category is evolving. Early automation tools focused on connecting applications through simple trigger-action pairs: when something happens in one app, do something in another. This worked for basic scenarios but broke down quickly when logic became complex or decisions needed context. The current generation incorporates AI not as an afterthought but as fundamental capability that changes what automation can accomplish. Rather than requiring every condition and action to be explicitly specified, AI agents can interpret intent, make contextual decisions and adapt to situations that weren’t explicitly programmed.

n8n’s adoption of this direction is thorough rather than superficial. The integration with LangChain provides access to a mature framework for building AI applications, rather than a proprietary system that locks you into specific approaches. The ability to use multiple LLM providers means you’re not dependent on any single AI vendor’s pricing, availability or capabilities. The visual node architecture extends to AI components, meaning you can see how agents are configured, what tools they have access to and how they make decisions. This transparency becomes critical as AI systems take on more responsibility in business workflows. When an agent makes an unexpected decision or produces an incorrect result, being able to inspect its configuration and execution trace makes troubleshooting feasible.

The economic model of fair-code licensing combined with self-hosting gives n8n interesting positioning as AI capabilities become more central to automation. Many AI platforms operate exclusively as cloud services where you have no visibility into how they work and no control over your data. Running AI agents that process sensitive business information on someone else’s infrastructure represents a compliance and security challenge that many organisations aren’t comfortable with. n8n’s self-hosting option means you can run AI-powered automation entirely within your own infrastructure, using your own LLM deployments if needed. This capability matters increasingly as AI becomes less experimental and more operational. When AI agents handle customer interactions, process financial data or make business decisions, the ability to control where they run and what data they access transitions from nice-to-have to mandatory.

The competitive positioning against established players like Zapier, Make and newer entrants like ActivePieces comes down to specific architectural choices and philosophical approaches. They all connect applications and trigger workflows. Differentiation emerges in how they handle complexity, whether they allow self-hosting, how they integrate AI capabilities, what their licensing and pricing models look like, and whether they create ecosystems that extend beyond what core teams build. n8n positions itself explicitly for technical teams who want power and control rather than casual users who need simplicity above all else. This focus means it can offer capabilities that would overwhelm non-technical users without apologising for complexity. The visual interface remains accessible, but the platform doesn’t sacrifice depth to achieve universal appeal.

For organisations evaluating automation platforms, n8n merits consideration particularly if you employ technical teams comfortable with concepts like APIs and data transformation, need self-hosting for data sovereignty or cost reasons, want to incorporate AI capabilities meaningfully rather than superficially, value the transparency of fair-code licensing over proprietary systems, or anticipate high automation volumes where per-execution pricing becomes expensive. It won’t suit every scenario. Teams without DevOps capabilities might find managed-only platforms simpler. Organisations requiring enterprise support contracts need to verify what n8n offers at different tiers. Non-technical teams might prefer interfaces that hide complexity even at the cost of capability. But for technical organisations building automation infrastructure they intend to own rather than rent, n8n presents a compelling combination of power, transparency and control that few alternatives match.

With extensive experience in designing and implementing digital automation systems, we understand the technical and strategic considerations that organisations face when building workflow infrastructure. Based in Horley, Surrey, with additional locations in Peckham and Hampstead, London, we help businesses evaluate, implement and optimise automation platforms that align with their operational requirements and growth objectives. Whether you need guidance on tool selection, assistance with implementation, or custom integration development, we can help you build automation infrastructure that delivers reliable value whilst maintaining the control and flexibility your organisation requires. Get in touch to discuss how we can support your automation initiatives.