n8n vs LangGraph: How Our Team Approaches AI Workflow Development Across Different Tools
Over the past year and a half, our team has built a wide range of AI workflows and agents using tools such as LangChain and LangGraph. These frameworks have given us a high level of control when designing more advanced or customised systems. While our typical approach leans toward code-first development, a recent client request led us to explore n8n for a lightweight solution.
That exploration provided useful insights into where each tool can fit into different types of projects.
Initial Thoughts on n8n
n8n initially presents itself as a visual workflow builder, and at a glance it resembles many other automation platforms. However, after spending time with it, we found that it offers a broader range of integrations and capabilities than expected. The interface is intuitive, and assembling functional workflows required relatively little setup time.
These early experiments showed that n8n can be helpful when a project benefits more from rapid assembly than from extensive custom logic.
Creating an AI agent in n8n
Creating a simple AI agent in n8n mainly involved connecting pre-built components. Using an OpenRouter node, a webhook, and some branching logic, we were able to design a basic workflow that accepted a user input, processed it with an LLM, and returned a formatted response.
While this approach doesn’t provide the level of orchestration or fine-grained control that more advanced frameworks can offer, it proved suitable for straightforward tasks such as summarisation, basic routing, or simple automated replies.
How It Stacks Up Against LangGraph
It’s important to note that n8n and LangGraph serve different purposes. LangGraph is designed for complex workflows that may require state management, multi-step reasoning, or long-running processes. It offers deeper control and customisation, which can be valuable for production-grade systems.
n8n, by contrast, focuses on visual configuration and ease of use. It may be a practical option when timelines are short, requirements are well-defined, or a prototype is needed quickly.
Both tools have strengths, and our evaluations are based solely on internal use cases rather than broad conclusions about either platform.
When We Choose One Tool Over the Other
Within our team:
n8n is considered when a project involves a narrow, clearly scoped workflow, especially for internal demos or early prototypes.
LangGraph is typically selected when a solution requires maintainability, versioning, more detailed orchestration, or long-term scalability.
These choices depend on the specific goals and constraints of each project rather than a general preference for one platform over another.
Closing Reflections
Our exploration of n8n has shown that it can complement our existing tools in scenarios where rapid assembly and visual workflows offer practical advantages. While code-first frameworks remain essential for more complex or long-term applications, n8n provides a useful option for simpler cases.
As with any technology decision, selecting the right tool depends on project requirements, expected complexity, and long-term objectives. For the appropriate tasks, n8n has become another resource in our toolkit.
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Legal Disclaimer: The views expressed in this article reflect internal observations based on our team’s limited use of the referenced tools. They are not intended as definitive evaluations, endorsements, or comparisons. All product names, logos, and trademarks mentioned belong to their respective owners. Any decisions regarding tool selection should be based on independent research and specific project requirements. The information provided is for general informational purposes only and may change as the technologies evolve.