Langflow vs n8n vs Dify: Which AI Workflow Tool Should Developers Choose?

Langflow, n8n, and Dify each have 36,000 to 50,000-plus GitHub stars and growing adoption, but they solve fundamentally different problems. Choosing the wrong one does not just slow you down — it forces a rewrite six months later when your requirements outgrow what the tool was designed to do. Langflow is a visual builder for LangChain and LangGraph pipelines; n8n is a general-purpose automation engine that added AI modules; Dify is a full LLM-app platform with backend, database, admin UI, API gateway, and prompt management baked in. None of them is universally best. The right answer depends entirely on what layer of the stack you need help with and who on your team will be owning it week to week.

Langflow vs n8n vs Dify 2026: Three Tools, Three Different Jobs

All three tools are open-source, self-hostable, and actively maintained, yet they compete in almost no real-world decision. Langflow has 36,000-plus GitHub stars and targets AI developers who already live inside the LangChain ecosystem and want to compose RAG pipelines or multi-agent graphs visually before exporting to Python. n8n, with 50,000-plus GitHub stars and a $180 million Series C behind it, is the dominant general-purpose workflow automation engine; AI is one node in a broader orchestration graph that might also talk to Slack, Postgres, Salesforce, and a cron schedule in the same flow. Dify, also at 50,000-plus GitHub stars, is the only one of the three that ships a complete production platform out of the box — including authentication, multi-tenant workspaces, role-based access control, prompt versioning, and an API gateway. If your team has never shipped an LLM app to real users before, these three tools represent three completely different bets on which layer of the problem is hardest: the AI pipeline itself (Langflow), the surrounding automation fabric (n8n), or the production LLM-app infrastructure (Dify).

Langflow: Visual LangChain Builder for RAG and Agent Prototyping

Langflow, with 36,000-plus GitHub stars and an Apache 2.0 license, is the most developer-native tool in this comparison. Every node in the visual canvas maps directly to a Python class from the LangChain or LangGraph ecosystem. You can build a full retrieval-augmented generation pipeline — document loader, text splitter, embedding model, vector store, retriever, reranker, and LLM — by connecting nodes, then export the entire graph as runnable Python code. That bidirectional relationship between the visual editor and actual code is Langflow’s defining feature and its clearest advantage over the other two. Prototyping with Langflow feels like writing code with a visual debugger, not like filling out forms. LangGraph integration is native, so stateful multi-agent systems with conditional edges, loops, and shared memory are first-class citizens in the canvas. Installation is a single pip install langflow && langflow run — there is no Docker Compose stack required for local development. The tradeoff is that Langflow does not include built-in production infrastructure: there is no authentication layer, no rate limiting, no multi-tenant workspace management, and no prompt versioning system. When your prototype graduates to production, you build or bolt on those capabilities yourself.

Langflow is the right choice when your team is already running LangChain in production and you want to accelerate iteration on complex RAG pipelines, when you need fine-grained control over chunking strategies and embedding models across multiple vector databases simultaneously, or when your development workflow involves rapid prototyping followed by clean handoff to a Python codebase. It is the wrong choice if your primary need is connecting business SaaS tools or if multiple non-engineering teams need to collaborate on prompt management and app deployment.

Use Langflow when:

• Your team already writes LangChain or LangGraph code
• You need complex RAG pipelines with custom chunking, embedding, and reranking strategies
• You want to prototype visually and export clean Python code to your existing backend
• You are designing stateful multi-agent architectures with LangGraph

Skip Langflow when:

• Your core need is connecting Slack, Gmail, Salesforce, or similar SaaS tools
• Non-technical teammates need to own and maintain workflows
• You need built-in team management, prompt versioning, or API gateway features

n8n: General-Purpose Automation with AI Modules

n8n has 50,000-plus GitHub stars, a $180 million Series C, and 200-plus pre-built integrations — making it the most mature general-purpose workflow automation platform in the open-source space. The core value proposition has nothing to do with AI: n8n excels at connecting systems, triggering on events, handling errors and retries, running on schedules, and maintaining a detailed execution history that makes debugging and audit straightforward. AI capabilities — OpenAI, Anthropic, and Google AI nodes — were layered on top of that solid automation foundation. That ordering matters. When you build a workflow in n8n, you get production-grade error handling, retry policies, and run logs for every step, including the AI steps. That level of operational maturity is something Langflow and Dify cannot match for automation-centric use cases. n8n is available as a cloud service starting at $20 per month or as a fully self-hosted deployment under its Sustainable Use License, which is free for internal automation but requires a commercial license if you resell n8n-powered workflows as a product.

The best n8n workflows look like this: a Zendesk ticket arrives via webhook, a GPT-4 node classifies the issue, a condition node routes it to the right Jira project, and a Slack node notifies the on-call team — all with retry logic, failure alerts, and a complete run log. The AI step is one node among many, not the centerpiece. That pattern — AI as a processing step inside a broader business process — is exactly where n8n wins. Where n8n falls short is in depth: building a sophisticated RAG pipeline with custom chunking strategies or a LangGraph-style stateful multi-agent system requires piecing together HTTP Request nodes manually, with none of the semantic scaffolding that Langflow provides. Similarly, n8n has no concept of prompt versioning, LLM model switching, or multi-tenant app management that Dify handles natively.

Use n8n when:

• AI work is really an automation and orchestration problem with AI as one step
• You need 200-plus SaaS integrations without writing custom API clients
• Non-technical team members need to build and maintain workflows
• Error handling, retries, scheduling, and run logs are non-negotiable operational requirements
• Your automation volume is high and you need reliable batch and scheduled processing

Skip n8n when:

• You need fine-grained control over RAG pipeline components
• Your primary output is a standalone LLM-powered application with its own UI and API
• You need multi-tenant workspace management and prompt versioning for a team of LLM engineers

Dify: The Full LLM-App Platform for Production Deployments

Dify has 50,000-plus GitHub stars and an MIT license, and it is the only tool in this comparison that ships a complete production environment for LLM applications. The platform bundles a backend API server, a PostgreSQL database, a vector database, a Redis cache, an admin UI, an API gateway, prompt management with versioning, and role-based access control into a single Docker Compose stack. When you self-host Dify, you are not just running a workflow builder — you are running a multi-tenant LLM-app platform that supports multiple workspaces, multiple teams, and multiple deployed applications from a single installation. Dify also natively supports over 100 LLM providers, including GPT-4o, Claude 3.x, Gemini, Mistral, and Llama, and switching between them requires no code changes — just a UI selection. Dify Cloud paid plans make the platform accessible without any infrastructure management. For teams that need to ship LLM-powered SaaS products or internal tools to real users with real access controls, Dify removes months of boilerplate infrastructure work that the other two tools leave entirely to you.

The tradeoff is customizability and depth at the pipeline level. Dify abstracts RAG into a “Knowledge Base” concept that handles document ingestion, chunking, and indexing automatically. That abstraction accelerates time-to-production but limits the fine-grained control that Langflow provides over chunking strategies, embedding models, and retrieval configurations. Dify also does not expose a code-export path — the platform is designed for teams that want to operate LLM apps, not for developers who want to extract a pipeline into their own codebase.

Use Dify when:

• You are building a multi-tenant LLM SaaS product or internal tool for multiple teams
• Prompt versioning, A/B testing, and collaborative prompt management are required
• You need a production API gateway and authentication layer without building them yourself
• Your team includes both engineers and non-engineers who need to collaborate on LLM app development
• You want to compare 100-plus LLM providers without touching code

Skip Dify when:

• You need LangGraph-style stateful multi-agent control or custom Python pipeline logic
• Business process automation with broad SaaS integration is your primary use case
• You want to export pipeline code to a separate backend codebase

Feature Comparison: RAG, Agents, Integrations, and Production Readiness

The clearest way to see where each tool fits is to look at five dimensions: RAG pipeline depth, agent orchestration, third-party integrations, production infrastructure, and error handling.

RAG pipelines: Langflow wins on depth. You can connect Chroma, Pinecone, Weaviate, and Qdrant side by side, swap chunking strategies, add a reranker, and export the whole pipeline to Python. Dify wins on speed — upload documents to a Knowledge Base and the pipeline is live in minutes. n8n requires manual HTTP node wiring and is not a practical choice for serious RAG work.

Agent orchestration: Langflow is the clear winner for LangGraph-based stateful agents. Dify has agent nodes that work well for simpler tool-calling patterns. n8n can chain API calls but does not have a native concept of agent memory or stateful execution loops.

Integrations: n8n dominates. 200-plus connectors covering every major SaaS tool, database, and communication platform. Langflow and Dify both trail significantly here.

Production infrastructure: Dify wins unambiguously. Authentication, RBAC, multi-tenant workspaces, prompt versioning, and an API gateway are included by default. Langflow provides none of these. n8n provides strong operational features for automation (scheduling, retries, run logs) but not for LLM-app lifecycle management.

Error handling: n8n has the best retry logic, error branching, and failure notification system of the three. This is core infrastructure for reliable business process automation.

Self-Hosting and Deployment: All Three Options Compared

All three tools are open-source and support full self-hosting. The operational complexity of each deployment is meaningfully different.

Langflow is the simplest to get running locally. A single pip install langflow && langflow run starts a local server on port 7860. For production, Langflow runs as a single Python process, which makes horizontal scaling via load balancer straightforward. The challenge is that Langflow does not ship with authentication, rate limiting, or database persistence configured out of the box — you add those layers yourself or integrate with a managed platform.

n8n runs locally with a single Docker command: docker run -p 5678:5678 n8nio/n8n. Production deployments support a queue mode that separates the main process from workers, enabling horizontal scaling of execution capacity. n8n uses SQLite for development and PostgreSQL for production, and the documentation covers this transition clearly. The Sustainable Use License allows free self-hosting for internal use.

Dify requires the most effort to deploy. The standard installation is a Docker Compose stack that brings up an API server, worker processes, PostgreSQL, a vector database (Weaviate or pgvector), and Redis simultaneously. This is also Dify’s strength: the full production stack is defined and reproducible, and each component can be scaled independently in a microservices architecture. For teams that have never operated a multi-service Docker deployment, the initial setup is the steepest part of the Dify learning curve. Once running, it is the most complete self-hosted LLM-app environment of the three.

Resource requirements at a glance:

Which Tool Should You Choose?

The answer is almost always determined by a single question: what is the primary job this tool needs to do for your team?

Choose Langflow if your team already uses LangChain or LangGraph in production code, your primary challenge is designing and iterating on complex RAG pipelines, or your development pattern involves rapid prototyping followed by exporting pipeline logic to a Python backend. Langflow is the only tool here with bidirectional code-graph parity — you can start in the visual editor and finish in code, or start in code and import into the canvas. That makes it uniquely suited to AI-focused engineering teams who want visual tooling without abandoning programmatic control.

Choose n8n if the problem you are solving is fundamentally an automation and orchestration problem, and AI is one step among many. If your workflow involves ingesting data from five different SaaS tools, running a classification or summarization step, writing results to a database, and triggering a Slack notification — that is n8n territory. The 200-plus built-in integrations, the battle-tested retry and error-handling system, and the clean execution history make n8n the most operationally mature tool for automation-centric workloads. n8n also has the lowest barrier to entry for non-technical users.

Choose Dify if you are building a production LLM application that will be used by real users, managed by multiple team members, and operated over months or years. Dify is the only tool here that includes prompt versioning, multi-tenant workspaces, RBAC, and an API gateway as first-class features. For a small team shipping an LLM-powered product, Dify eliminates months of infrastructure work. For an enterprise team deploying multiple LLM applications across departments, Dify’s workspace and access control model is the only one that scales without custom engineering.

Quick decision rules:

• Already writing LangChain code → Langflow
• AI work is one step inside a broader business automation → n8n
• Shipping an LLM-powered product to users, need prompt management and access control → Dify
• Need broadest SaaS integration catalog → n8n
• Need to export pipeline code to your own backend → Langflow
• Fastest path to a production-ready multi-tenant LLM app → Dify

Getting Started: Hello World in 30 Minutes

Here is the fastest path to a working prototype in each tool. All three can get you from zero to a functioning LLM workflow in under 30 minutes on a standard developer laptop.

Langflow in 30 minutes

Install and launch:

pip install langflow
langflow run

Open http://localhost:7860 in your browser. From the template gallery, select the “Basic RAG” template. This drops a pre-wired pipeline with a file loader, text splitter, OpenAI embedding, Chroma vector store, retriever, and ChatOpenAI node onto the canvas. Add your OPENAI_API_KEY in the component sidebar, upload a PDF document, and click Run. You now have a functional RAG pipeline. To export it as Python code, click the code export button in the top toolbar — the output is a self-contained Python script using LangChain and LangGraph that you can paste directly into an existing backend.

n8n in 30 minutes

Start with Docker:

docker run -p 5678:5678 -v ~/.n8n:/home/node/.n8n n8nio/n8n

Open http://localhost:5678. Create a new workflow and add three nodes: an HTTP Request trigger (to receive a POST request), an OpenAI node (set to chat completion mode), and a Respond to Webhook node to return the result. Configure the OpenAI node with your API key and a system prompt. Test with a curl -X POST http://localhost:5678/webhook/your-id -d '{"question":"What is LangChain?"}'. You now have an AI-powered API endpoint with built-in execution logging. Add a condition node to branch on the response and a Slack node to notify a channel — that is three more minutes of work.

Dify in 30 minutes

Clone and launch:

git clone https://github.com/langgenius/dify
cd dify/docker
docker compose up -d

Open http://localhost and complete the admin setup. Go to Knowledge Base, upload a PDF, and let Dify index it automatically. Then create a new Chatbot app, connect it to your Knowledge Base, and select your LLM provider. Dify generates a public chat URL and a REST API endpoint automatically. Share the chat URL with a colleague — they can query your RAG app in a browser without any setup on their end. Switch LLM providers by changing a dropdown; no code changes required. Total time from git clone to a shareable RAG chatbot with a working API: approximately 25 minutes, depending on Docker image pull speed.

FAQ

Q1. Can Langflow and n8n be used together in the same stack?

Yes, and this is a common production pattern. n8n handles external event triggers — incoming webhooks, email, schedules — and then calls Langflow’s RAG pipeline via an HTTP Request node. Langflow exposes its flows as REST API endpoints, so the integration requires no custom connector. The division of responsibilities is clean: n8n owns orchestration and integration, Langflow owns the AI pipeline logic.

Q2. Is Dify fully open-source?

Dify’s community edition is open-source under the MIT license and can be self-hosted at no cost. Dify Cloud offers paid plans with additional features and managed infrastructure. Some enterprise features — advanced SSO, enhanced RBAC, dedicated support — are available only on the Enterprise plan. For most self-hosted deployments, the MIT-licensed community edition covers the full feature set including multi-tenant workspaces, prompt versioning, and the API gateway.

Q3. What does n8n’s Sustainable Use License mean in practice?

The Sustainable Use License allows free self-hosting for internal automation — if your team is running n8n to automate your own business processes, there are no restrictions. The license restricts using n8n as the foundation for a commercial product that you sell to third-party customers. If you are building a SaaS product powered by n8n workflows that customers pay to use, you need a commercial license from n8n. Langflow (Apache 2.0) and Dify (MIT) do not carry this restriction.

Q4. Which tool is fastest for building a production RAG pipeline?

For the fastest path to a working RAG app that real users can access, Dify wins. Upload documents to a Knowledge Base, connect to an LLM, and Dify generates a shareable chat interface and REST API endpoint automatically. For the most technically sophisticated RAG pipeline with custom chunking, multiple vector databases, and reranking — and for teams who need to export that pipeline to their own Python codebase — Langflow is the better choice.

Q5. Which tool is best for a small team of three to five developers shipping an LLM product?

It depends on what the product is. If you are building an LLM-powered product with users, access control requirements, and multiple team members collaborating on prompts and app configuration, Dify is the strongest starting point — it eliminates the most infrastructure boilerplate for that specific use case. If your product is more accurately described as a business automation that uses AI as one step, n8n at $20 per month is the most cost-effective and feature-complete choice. If your team consists of Python engineers who want to iterate fast on complex AI pipelines and own the production infrastructure themselves, Langflow gives the most programmatic control.