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Dify vs Flowise vs Langflow

Three visual LLM orchestrators for building AI workflows without writing chain code.

Dify, Flowise, and Langflow are the three leading open-source platforms for visual AI workflow building. Compared on language stack, production posture, visual paradigm, RAG story, and where each one wins for teams building agents and automations.

The contenders

Each pick links through to its full Directories entry.

O OSS

Dify

by Community

Production-ready platform for agentic workflow development.

Best for: Teams shipping production agentic systems who want a unified platform for workflow design, RAG, and agent management.
Read the full entry
O OSS

Flowise

by Community

Build AI Agents, Visually

Best for: Engineers prototyping multi-step workflows fast with a familiar node-based visual editor and LangChain integrations.
Read the full entry
O OSS

Langflow

by Community

Langflow is a powerful tool for building and deploying AI-powered agents and workflows.

Best for: Python-first teams building experimental and production LLM workflows with code flexibility baked into the visual surface.
Read the full entry

Side by side

Same criteria, three answers. The verdict is opinionated and lives below the table.

Criterion DifyFlowiseLangflow
Language stack TypeScript fullstack (backend + frontend unified)TypeScript (Node.js backend, React frontend)Python backend, React frontend
Visual paradigm Canvas-based blocks with explicit connections and memory/context managementNode-based graph (LangChain community visual editor style)Flow-based graph inspired by React Flow
RAG support First-class: knowledge base integration, hybrid search, citation tracking built inVia LangChain integrations (supported, not native)Via LangChain integrations (supported, not native)
Multi-agent orchestration Native agent patterns with role definition and reasoning loopsVia manual tool chaining and LLM function callingVia manual agent composition and conversational patterns
Self-hosted complexity Medium: Docker compose or K8s, requires Postgres or MySQLMedium: Docker or npm, single Node process with SQLite defaultMedium: Python environment, requires database backend
Production readiness High: versioning, monitoring, API-first design, commercial SaaS availableMedium: open source stable but community-driven opsMedium: open source stable with large star count but minimal official support
Code extensibility Low friction: Python/JavaScript extensions via plugin systemMedium friction: fork and extend LangChain integrations directlyHigh friction: fork and extend backend, rebuild frontend
GitHub stars 143k (February 2026)53k (February 2026)149k (February 2026)

Verdict

Dify is the production platform. It built RAG as a first-class concept rather than bolting it on top of LangChain, which means knowledge bases, multi-step retrieval, and citation tracking are integrated into the workflow canvas from the start. The TypeScript fullstack simplifies operations because your orchestration runtime and your admin UI live in the same codebase. If you are shipping a customer-facing agent or workflow today and want commercial support available, Dify is the right bet.

Flowise and Langflow are the experimentation platforms. Flowise wins on time-to-first-prototype because the node editor is familiar to anyone who has used a visual programming tool. Langflow wins if your team lives in Python and wants to drop into code inside the visual flow without forking the entire runtime. Both are excellent for internal automation, research, and rapid iteration before committing to a production stack.

For most teams, the answer is not singular. Start with Flowise or Langflow to prove the workflow works, then migrate to Dify once you need RAG, multi-agent orchestration, versioning, or commercial reliability. The visual patterns are similar enough that the rework is light. Use Dify if you need those things from day one. Use Flowise or Langflow if you need to prove value before investing in production infrastructure.

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