Dify vs Langflow: Quick Verdict
Dify and Langflow are the two leading open-source, visual AI workflow builders — platforms that let you build LLM-powered applications through a graphical interface rather than writing orchestration code from scratch.
They overlap significantly, but they're designed for different primary users:
- Dify — An all-in-one AI application platform targeting product teams and non-engineers. Stronger on RAG pipelines, built-in deployment, and application management. Best when you want to ship a production-ready AI app without engineering overhead.
- Langflow — A visual builder for LangChain workflows, targeting developers who want a GUI for prototyping and debugging complex pipelines before coding them. Best as a development tool rather than a deployment platform.
If your question is "which one can I ship to users right now?", the answer is Dify. If your question is "which one helps me prototype faster before writing real code?", the answer is Langflow.
What Is Dify?
Dify is an open-source LLMOps platform that packages everything needed to build, test, deploy, and monitor AI applications in one interface. It ships with:
- A visual workflow builder for multi-step AI pipelines
- Built-in RAG engine (document ingestion, chunking, embedding, retrieval)
- Prompt engineering IDE with A/B testing
- Model provider management (OpenAI, Anthropic, Gemini, local models)
- A one-click deployment layer that turns your workflow into an API or embeddable chat widget
- Built-in analytics and conversation logging
Dify is particularly strong for teams building RAG-based chatbots and knowledge bases — the kind of internal Q&A tools, customer support bots, and document analysis applications that represent the majority of enterprise AI deployments.
What Is Langflow?
Langflow is an open-source visual interface for building LangChain applications. It represents LangChain components — LLMs, chains, agents, memory, retrievers, tools — as draggable nodes that connect via edges in a graph.
Langflow's primary value is rapid prototyping. You can assemble a multi-step LangChain pipeline visually, test it in the browser, and export the resulting flow as Python code for production use. It's much faster to debug a RAG pipeline by rearranging nodes than by editing code.
It ships with:
- Pre-built components for LangChain's ecosystem (chains, agents, retrievers, tools)
- A Python code component for custom logic
- Multi-model support (any LangChain-compatible model)
- Basic API export
- Docker deployment option
Feature Comparison
| Feature | Dify | Langflow | |---------|------|----------| | Primary use | Ship AI apps | Prototype LangChain flows | | Target user | Product teams + developers | Developers | | Built-in RAG engine | ✅ Full (ingestion → retrieval) | Partial (requires config) | | Document ingestion UI | ✅ | ❌ | | One-click deployment | ✅ API + chat widget | ❌ (export code, deploy yourself) | | Built-in analytics | ✅ | ❌ | | Prompt A/B testing | ✅ | ❌ | | Agent support | ✅ | ✅ (via LangGraph) | | MCP integration | In progress | Limited | | Model providers | 30+ (inc. local) | 20+ (LangChain-supported) | | Self-hostable | ✅ | ✅ | | Cloud hosted | Dify.ai (SaaS) | Langflow Cloud | | Open-source | ✅ (Apache 2.0) | ✅ (MIT) | | GitHub stars (2026) | ~50K | ~35K |
RAG Pipeline Comparison
RAG (Retrieval-Augmented Generation) is the most common use case for both platforms. Their approaches differ significantly.
Dify RAG
Dify has the most complete no-code RAG pipeline of any platform. You can:
- Upload documents (PDF, DOCX, CSV, web URLs, Notion, GitHub)
- Configure chunking strategy (fixed length, paragraph, custom delimiter)
- Choose your embedding model
- Set retrieval parameters (top-K, similarity threshold, re-ranking)
- Connect the knowledge base to your chatbot or API in one click
The entire pipeline is visual and requires zero code. The built-in embedding and vector database storage is handled by Dify — you don't need to provision a separate vector store for basic use cases.
Langflow RAG
Langflow's RAG support is more manual. You connect components: a document loader → text splitter → embedding model → vector store → retriever → LLM chain. Each component is configurable, but you're responsible for connecting the pieces and making sure the data flows correctly.
This gives experienced developers more control, but it's slower to set up and easier to misconfigure. Langflow doesn't handle document management or re-ingestion — that's your problem once you deploy.
Verdict: For RAG use cases, Dify is faster and more complete. Langflow is better if you need unusual retrieval architectures that Dify's opinionated pipeline doesn't support.
Agent Support
Both platforms support AI agents, but via different mechanisms.
Dify agents are configured through the workflow builder. You define tools (web search, custom API calls, code execution, database queries), and the agent runtime handles model-driven tool selection. Dify supports function-calling agents and ReAct-style agents out of the box.
Langflow agents leverage LangChain's agent ecosystem — LangGraph for stateful multi-step agents, ReAct agents, OpenAI tools agents. The visual interface lets you wire agent nodes together, but complex agentic flows still benefit from custom code components.
For AI agents that need complex, cyclical reasoning with persistent state, Langflow + LangGraph gives you more control. For agents that need to be deployed quickly and managed by non-engineers, Dify's agent workflow is more accessible.
Deployment and Production Readiness
This is where Dify has the clearest advantage.
Dify is built to be a deployment platform. Your workflow becomes an API endpoint or embeddable chat widget with one click. It ships with rate limiting, authentication, conversation history, usage analytics, and a logs dashboard. You can deploy Dify itself via Docker Compose or use Dify.ai's hosted SaaS.
Langflow is primarily a development and prototyping tool. It can export flows as Python code (runnable with LangChain). Langflow Cloud provides basic hosting, but production deployments typically involve exporting the flow and wrapping it in a FastAPI or Next.js application.
If your end state is custom application code, Langflow's visual prototyping → code export path is well-suited. If you want to ship an AI product without a separate deployment layer, Dify handles it.
Pricing
Both platforms are open-source and free to self-host.
Dify Cloud (SaaS):
- Free tier: 200 message credits/day
- Professional: ~$59/month (more credits, team features, priority support)
- Enterprise: Custom pricing (SSO, audit logs, dedicated support)
Langflow Cloud (SaaS):
- Free tier available
- Plus: ~$49/month
- Enterprise: Custom
Self-hosting both is free with your own infrastructure costs.
When to Choose Dify
- You want to ship a production AI app (chatbot, knowledge base, document QA) without writing deployment infrastructure
- Your team includes non-engineers who will manage the AI workflows
- You need built-in document management, conversation history, and analytics
- RAG over internal documents is your primary use case
- You're building on LLM basics like RAG and don't need unusual retrieval architectures
When to Choose Langflow
- You're prototyping LangChain workflows and want a visual debugger
- You'll ultimately export to production Python code rather than using the platform as a deployment target
- You need deep LangGraph agent capabilities that Dify doesn't expose
- Your team is developer-heavy and the visual interface is a productivity tool, not a no-code requirement
- You need unusual component configurations that Dify's opinionated UX restricts
The Custom Code Alternative
Both Dify and Langflow have limits. Complex enterprise AI products typically outgrow visual builders when they need custom business logic, advanced auth, multi-tenancy, fine-grained observability, or MCP-native agent architectures.
The decision isn't just "Dify vs Langflow" — it's also "visual builder vs custom code." For rapid prototyping and straightforward use cases, visual builders ship faster. For products that need to scale, customize, and integrate deeply, custom code with a framework like LangGraph or direct API calls gives you control that visual builders can't match.
See our guide on build vs buy for AI MVPs if you're weighing the broader tradeoff.
Related: LangChain vs LlamaIndex · What is RAG? · Build vs Buy AI MVP
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