n8n vs LangGraph: Which AI Workflow Tool Wins in 2025?

In today’s AI-powered world, orchestrating complex workflows is no longer a luxury—it’s a necessity. As organisations and developers seek to automate, scale, and manage intricate processes, two platforms often pop up in conversations: n8n and LangGraph. Both are celebrated for their ability to streamline automation and AI agent workflows, but they serve different audiences and use cases. 

This head-to-head comparison will unpack the technical nitty-gritty, unique strengths, and real-world scenarios of n8n and LangGraph, helping you decide which is the better fit for your next AI project.

Spoiler: n8n edges ahead for most practical, production-grade automation needs.

🤖 Quick Intro: What Are n8n and LangGraph?

n8n is an open-source workflow automation tool that lets you connect apps, APIs, and services—think of it as a developer-friendly alternative to Zapier or Make, but with far more flexibility. It’s built for automating everything from simple tasks to complex, multi-step business processes, and it shines with its visual editor, extensibility, and robust integrations.

LangGraph, on the other hand, is a graph-based orchestration framework designed for building, managing, and deploying stateful AI agents and LLM-powered workflows. It’s part of the LangChain ecosystem and is tailored for advanced AI applications—think multi-agent reasoning, dynamic branching, and human-in-the-loop AI.

🏗️ Core Philosophy and Architecture

Feature	n8n	LangGraph
Design Focus	General workflow automation, API integration, ETL	AI agent orchestration, LLM workflows, stateful AI
UI/UX	Visual, drag-and-drop, no-code/low-code	Code-first, graph-based, visual prototyping tools
Extensibility	Custom nodes, scripts, community integrations	Python modules, custom nodes, tool integration
Deployment	Self-hosted, cloud, Docker, Kubernetes	Self-hosted, cloud SaaS, hybrid, LangGraph Studio
Open Source	Yes (fair-code, source-available)	Yes (MIT, fully open-source)

🧠⚙️ Technical Deep-Dive: How Each Platform Handles Workflows

Workflow Modelling

Parameter	n8n	LangGraph
Workflow Structure	Directed acyclic graphs (DAGs)	Directed graphs (support cycles/loops)
Node Types	Triggers, actions, transforms, custom	LLM calls, tools, custom Python functions
Branching	If/Else, Switch, Merge, Split	Dynamic, multi-conditional, cyclical
Memory/State	Per-execution context, env vars	Persistent state, long/short-term memory
Debugging	Step-by-step execution, logs, snapshots	Visualisation, time-travel, state editing

🔗 Integration and Extensibility

Parameter	n8n	LangGraph
Integrations	350+ built-in, custom via HTTP, scripts	Python ecosystem, LangChain, custom tools
API Support	REST, GraphQL, SOAP, Webhooks	Python APIs, LLM APIs, external tools
Custom Logic	JS/TS function nodes, custom modules	Python functions, tool integration
Plugin Ecosystem	Large, active community, npm packages	Growing, Python/PyPI, LangChain plugins
Third-party AI	OpenAI, HuggingFace, Google, AWS, etc.	Any LLM/AI model via LangChain

🏆 n8n Wins on Breadth

n8n’s biggest flex is its sheer number of ready-to-use integrations. From Slack to Salesforce, MySQL to Notion, you can automate almost any SaaS or database out-of-the-box. For anything else, HTTP Request nodes or custom scripts fill the gaps.

🏆 LangGraph Wins on Depth

LangGraph is deeply integrated with the Python and LangChain ecosystem. It’s made for AI agents, so you can plug in any LLM, tool, or custom Python logic, and orchestrate them in sophisticated, stateful workflows. If you’re building advanced AI, LangGraph is your playground.

State Management and Memory

Parameter	n8n	LangGraph
State Handling	Stateless per execution, context vars	Persistent, global state containers
Memory	Per-run, no long-term memory	Short-term and long-term memory
Session Handling	Per workflow run	Cross-session, persistent memory
Human-in-the-Loop	Manual intervention, basic approvals	Built-in, review, approval, moderation

n8n is stateless by design—each workflow run is independent. You can pass context and variables, but there’s no built-in long-term memory. For persistent data, you’d use an external database.

LangGraph, by contrast, is all about state. Each node can update a global state object, and memory is first-class: you can persist context, track conversation history, and even “time-travel” to previous states for debugging or correction.

🧠🤖 AI Agent Orchestration and LLM Workflows

Parameter	n8n	LangGraph
LLM Integration	Via OpenAI, HuggingFace, plugins	Native, multi-agent, multi-step orchestration
Multi-Agent Support	Basic (parallel nodes, webhooks)	Yes, designed for multi-agent systems
Conditional Logic	If/Else, Switch, custom code	Dynamic, graph-based, O(n²) branching
Streaming	Limited	Native token-by-token, intermediate steps
Human Collaboration	Manual, basic approval nodes	Built-in, review, approve, edit, rollback

LangGraph is purpose-built for orchestrating AI agents and LLMs. It supports complex, adaptive workflows where multiple agents (or tools) can collaborate, branch, and iterate based on real-time context. Streaming lets users see agent reasoning unfold in real-time, and human-in-the-loop controls ensure safety and reliability.

n8n can integrate with LLMs via plugins or HTTP requests, but it lacks the deep, native support for agentic reasoning and stateful AI that LangGraph provides.

💻 Visualisation, Debugging, and Developer Experience

Parameter	n8n	LangGraph
Visual Editor	Yes, drag-and-drop	LangGraph Studio (visual prototyping)
Debugging	Step-by-step, logs, error handling	State inspection, time-travel, edit/resume
Monitoring	Workflow logs, execution history	Visualise agent actions, state transitions
Testing	Manual, built-in test runs	Visual prototyping, live debugging

Both platforms offer strong visualisation and debugging, but n8n’s visual editor is more approachable for non-developers and rapid prototyping. LangGraph Studio is powerful for AI agent workflows, letting you trace agent decisions, edit state, and roll back as needed.

🚀 Deployment, Scalability, and Production Readiness

Parameter	n8n	LangGraph
Deployment	Self-hosted, cloud, Docker, K8s	Cloud SaaS, hybrid, self-hosted
Scaling	Horizontal scaling, clustering	Production-grade, scalable infra
Reliability	Durable, retries, error handling	Durable, resumable, retries, cost-efficient
Observability	Logs, metrics, external monitoring	LangSmith integration, deep tracing
Security	Role-based access, credential vaults	Role-based, data isolation, VPC support

n8n is proven in production across thousands of companies. It’s easy to deploy on any infrastructure, scales horizontally, and offers robust error handling and retry mechanisms.

LangGraph is newer but designed for production-grade AI agent systems. It supports cloud, hybrid, and self-hosted deployments, with features for observability, debugging, and secure, scalable execution.

🌐🤝Community, Documentation, and Ecosystem

Parameter	n8n	LangGraph
Community	Large, active, global	Growing, strong AI/LLM focus
Docs	Extensive, tutorials, examples	Detailed, code-first, AI agent focus
Support	Forums, Discord, GitHub, paid plans	GitHub, Discord, LangChain Academy
Ecosystem	350+ integrations, plugins, templates	LangChain, Python, AI/LLM tools

n8n’s community is massive, with tons of tutorials, templates, and ready-made integrations. LangGraph’s community is smaller but highly technical and focused on AI agent workflows, with strong support from LangChain and the Python ecosystem.

Cost and Licensing

Parameter	n8n	LangGraph
Open Source	Yes (source-available, fair-code)	Yes (MIT, fully open-source)
Free Tier	Yes, unlimited self-hosted	Yes, unlimited self-hosted
Cloud Pricing	Paid plans for cloud, enterprise	Paid plans for cloud, enterprise

Both platforms are open-source and free to self-host. Cloud and enterprise plans offer additional features, support, and scalability.

✨ Real-World Use Cases: Where Each Shines

Head-to-Head Summary Table

Feature/Parameter	n8n (Winner)	LangGraph
General Automation	✅
AI Agent Orchestration		✅
Visual Workflow Builder	✅	Partial (LangGraph Studio)
Integrations	✅ 350+	Python/AI ecosystem
State Management		✅ Persistent, global
Human-in-the-Loop	Partial	✅ Built-in
Debugging	✅	✅ Advanced, state editing
Production-Ready	✅	✅
Open Source	✅	✅
Community	✅	Growing

✅🤖 Final Verdict: Why n8n Has the Edge

While LangGraph is a powerhouse for advanced AI agent orchestration, n8n wins for most practical, production-grade automation scenarios. Its visual builder, massive integration library, and proven reliability make it the go-to for businesses automating real-world workflows—AI-powered or not.

LangGraph is the tool of choice if your project demands complex, stateful, multi-agent LLM workflows with advanced reasoning, memory, and human-in-the-loop controls. For everything else—especially when you need to connect dozens of services, automate business logic, and scale with confidence—n8n is the clear winner.

Bottom line

If you want to automate everything from SaaS to AI, n8n is your Swiss Army knife. If you’re building the next-gen AI agent with memory and reasoning, LangGraph is your secret weapon. Use both, and you’re unstoppable.