Flowise vs Langflow

Detailed side-by-side comparison to help you choose the right tool

Flowise

🟡Low Code

AI Agent Framework

Open-source visual LLM and agent builder — drag-and-drop canvas on a Node.js/TypeScript stack, with MCP nodes and a managed Flowise Cloud option.

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Starting Price

Free

Langflow

🟡Low Code

AI Agents

Open-source visual editor (acquired by DataStax/IBM) for building, prototyping, and deploying agentic LLM workflows with hundreds of pre-built components.

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Starting Price

Free

Feature Comparison

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FeatureFlowiseLangflow
CategoryAI Agent FrameworkAI Agents
Pricing Plans22 tiers22 tiers
Starting PriceFreeFree
Key Features
  • Visual node-based builder for AI agents and chatflows
  • Agentflow multi-agent orchestration
  • Chat assistants with RAG and tool calling
  • Low-code visual builder for agentic and RAG applications
  • Build and deploy AI agents and MCP servers
  • Supports major LLMs, vector databases, and AI tools

💡 Our Take

Choose Langflow if your team works primarily in Python and wants custom components as standard Python classes, or if you need built-in MCP server generation for Claude Desktop and Cursor integration. Choose Flowise if you prefer Node.js/TypeScript, want a larger library of pre-built templates, or value a more mature community marketplace.

Flowise - Pros & Cons

Pros

  • Truly self-hostable and free — no usage meter on the open-source build
  • Node.js stack means JS/TS teams can extend components without context-switching to Python
  • MCP integration lets you compose existing MCP servers into agent tools visually

Cons

  • Large flows (50+ nodes) get visually unwieldy and slow to navigate
  • Cloud pricing tiers are not openly listed on the marketing site
  • Lags pure-code frameworks on advanced agent patterns (graph state, conditional planning)

Langflow - Pros & Cons

Pros

  • Lowest-friction path to functional LLM agents for non-engineers
  • MIT-licensed core with no artificial feature gating versus the cloud version
  • Bi-directional MCP support is rare — most builders are MCP clients only
  • Inline custom Python escape hatch means you're not stuck inside the visual paradigm
  • Backed by IBM/DataStax means long-term maintenance is well funded

Cons

  • Visual flows become unwieldy past ~30 nodes; refactoring is awkward
  • Component quality varies — community contributions can be uneven
  • Self-hosted observability is limited; you'll want LangSmith or Langfuse alongside
  • Versioning of flows is JSON-export based, not git-native
  • Performance overhead versus hand-written code is non-trivial at scale

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🔒 Security & Compliance Comparison

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Security FeatureFlowiseLangflow
SOC2
GDPR
HIPAA
SSO
Self-Hosted✅ Yes✅ Yes
On-Prem✅ Yes✅ Yes
RBAC✅ Yes
Audit Log
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest
Encryption in Transit✅ Yes
Data Residencyself-hosted deployments allow user-controlled data residency
Data Retentionconfigurableconfigurable
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