Skip to content
aicoolies logo

LangFlow vs Flowise vs Dify — Visual AI Builder Comparison

Three visual builders for creating AI applications without extensive coding. LangFlow is LangChain's official visual builder with 146K+ stars, Flowise provides a lightweight drag-and-drop LLM flow builder, and Dify offers a complete LLMOps platform combining visual orchestration with model management and RAG.

analyzed by Raşit Akyol March 29, 2026

ShareShare on XShare on LinkedInShare on Reddit
LangFlowFlowiseDifywinner

Flowise reviewDify review

Verdict

Dify is the superior comprehensive platform for building production-grade LLM applications, integrating visual workflow orchestration, prompt management, dataset RAG pipelines, and enterprise-ready backend APIs. While Langflow and Flowise offer valuable drag-and-drop canvas experiments for LangChain and LlamaIndex components, Dify excels by delivering a complete operational stack with built-in monitoring, web-app publishing, and role-based access control for technical teams. Our pick: Dify.

Quick Comparison

LangFlow

Pricing
Langflow is a 100% open-source visual multi-agent and RAG orchestration IDE (MIT license, 45k+ GitHub stars) that is completely free ($0) to self-host via pip or Docker. For fully managed cloud deployment, DataStax Astra Cloud provides a managed Langflow tier with generous free monthly credits, pay-as-you-go serverless scaling, and enterprise governance.
Pricing Model
Freemium
Platforms
Web, Self-hosted, Docker, Python
Open Source
Yes
Telemetry
Clean
Status
Active
Editorial Pick
—
Last Verified
Sep 6, 2026
Description
LangFlow is an open-source visual framework for building multi-agent AI apps with drag-and-drop. Built on LangChain, it lets developers compose chains, agents, and RAG pipelines by connecting modular components visually. Features real-time interaction, Python customization, one-click deployment, and export to LangChain code. Supports all major LLM providers, vector stores, and tools. With 146K+ GitHub stars, it bridges visual prototyping and production deployment.

Flowise

Pricing
Flowise is a 100% open-source low-code LLM orchestration platform (Apache-2.0 / MIT license, 35k+ GitHub stars) that is completely free ($0) to self-host on local machines, Docker, or Kubernetes with unlimited flows and predictions. For managed hosting, Flowise Cloud offers tiered plans starting with Starter at $35/month (2 hosted flows, 10k predictions/month), Pro at $99/month (10 hosted flows, 50k predictions/month), and custom Enterprise plans for dedicated VPC deployments, SAML SSO, and enterprise SLAs (users supply their own LLM API keys via BYOK).
Pricing Model
Freemium
Platforms
Web, Self-hosted (Docker, Node.js)
Open Source
Yes
Telemetry
Clean
Status
Active
Editorial Pick
—
Last Verified
Sep 4, 2026
Description
Flowise is an open-source, low-code UI and API platform for building customized LLM orchestration flows, multi-agent systems, and autonomous AI applications using drag-and-drop node graphs.

Difywinner

Pricing
Dify.AI is free and open-source for self-hosting. Its managed cloud plans include a Free Sandbox tier, a Professional plan at $59/month for small teams, a Team plan at $159/month for growing organizations, and custom Enterprise deployments.
Pricing Model
Freemium
Platforms
Web, Self-hosted (Docker)
Open Source
No
Telemetry
Clean
Status
Active
Editorial Pick
—
Last Verified
Aug 26, 2026
Description
Source-available LLM application development platform combining a visual no-code canvas with backend capabilities for building AI workflows, RAG pipelines, and agent systems from prototype to production. Integrates hundreds of models from dozens of providers, with PDF/PPT ingestion, ReAct agents with 50+ tool integrations, and multi-step orchestration. Used by both technical and non-technical teams to ship GenAI apps like chatbots and Q&A systems.

What Sets Them Apart

Langflow, Flowise, and Dify address visual AI development across distinct stages of the application lifecycle. Langflow and Flowise are low-code node-and-wire canvas builders designed primarily to visually compose and experiment with LangChain components, prompts, and tools. Dify is an all-in-one AI application development platform and enterprise LLMOps suite integrating visual workflow orchestration, native multi-modal RAG, multi-agent routing, prompt studios, and production API serving.

Langflow targets Python AI engineers with FastAPI and LangGraph; Flowise targets JavaScript/TypeScript developers with embeddable chat widgets; Dify provides enterprise-grade microservice architecture with team RBAC and traffic isolation.

Langflow, Flowise, and Dify at a Glance

Dify supports Chatflows and deterministic Workflows, advanced built-in RAG with hybrid search, Prompt Studio, and auto-generated production REST APIs.

Langflow features a custom Python component editor inside visual nodes, with direct export to FastAPI microservices.

Flowise offers frictionless npm/Docker deployment for creating embeddable React/iframe chat widgets.

Technical Architecture and LLMOps

Dify decouples visual design from execution using Celery task queues, PostgreSQL, Redis, and pluggable vector databases with built-in token/cost analytics.

Langflow executes DAG graphs dynamically in FastAPI, supporting cyclic agent execution via LangGraph integration.

Flowise serializes nodes into LangChain.js execution chains on Express.js and Node.js.

Developer Experience and Production Scaling

Dify bridges visual design and enterprise production, enabling non-technical teams to manage knowledge bases while engineers integrate secure REST APIs.

Langflow provides excellent ergonomics for Python developers experimenting with custom code blocks.

Flowise gives frontend developers an instant path to embedding chatbots into websites.

The Bottom Line

Dify is the definitive winner for production AI application development and LLMOps, turning visual workflows into governed, scalable enterprise software.

Langflow is ideal for Python/LangGraph prototyping, and Flowise is best for quick embeddable JavaScript chat widgets.

FAQ

What are the core architectural runtime differences between LangFlow, Flowise, and Dify?

LangFlow is built in Python (FastAPI/React) executing natively inside the Python interpreter with direct access to PyTorch and async loops. Flowise is built on TypeScript/Node.js using LangChain.js optimized for low-footprint event-loop execution and embeddable JS widgets. Dify is an enterprise-grade GenAI application platform featuring a decoupled backend (Flask/Celery, Postgres, Redis, Vector DBs) with proprietary workflow orchestration.

How do their retrieval-augmented generation (RAG) engines and knowledge base pipelines compare?

LangFlow and Flowise treat RAG as modular canvas nodes where users manually wire document loaders, text splitters, embedding models, and vector stores. Dify provides an integrated production-ready RAG pipeline with automatic document ETL (PDF/DOCX/Markdown parsing), dynamic chunking, hybrid search (vector + BM25), reranking, and knowledge curation interfaces.

How do the three platforms compare regarding multi-tenancy, enterprise security, and production scaling?

LangFlow and Flowise are predominantly single-tenant developer canvas tools requiring custom containerization to scale. Dify is engineered for multi-tenant enterprise deployments featuring granular RBAC, team workspaces, API key management with rate limiting, audit logging, model budget limits, and asynchronous Celery/Redis worker queues.

How do they handle custom logic execution, external integrations, and code extensibility?

LangFlow allows writing arbitrary Python inside Custom Component nodes with real-time dependency introspection. Flowise supports custom JS functions inside code nodes and webhook triggers. Dify provides a secure sandboxed Code Execution Node (Docker/Wasm) supporting Python/JS alongside native OpenAPI/Swagger tool importing.

Sources & verification

Sources checked
Content verified

Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.