Langflow vs Flowise

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

Langflow

🟡Low Code

Visual AI agent builder

An open-source visual framework for building and deploying AI agents, retrieval flows, and Model Context Protocol services.

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Starting Price

Free
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Flowise

🟡Low Code

Visual AI agent builder

An open-source visual platform for composing LLM workflows, retrieval pipelines, and AI agents from modular nodes.

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Starting Price

Free
Full ReviewVisit Site

Feature Comparison

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FeatureLangflowFlowise
CategoryVisual AI agent builderVisual AI agent builder
Pricing Plans2 listed plans2 listed plans
Starting PriceFreeFree
Key Features
  • • 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
  • • Visual node-based builder for AI agents and chatflows
  • • Agentflow multi-agent orchestration
  • • Chat assistants with RAG and tool calling

💡 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.

Langflow - Pros & Cons

Pros

  • ✓Open-source visual canvas supports inspection and self-hosted experimentation
  • ✓Python extensibility lets developers move beyond stock components
  • ✓MCP client and server capabilities fit modern agent-tool architectures

Cons

  • ✗Visual building does not remove the need for evaluation and production engineering
  • ✗Model, vector database, and hosting charges sit outside the free software price
  • ✗Large graphs require disciplined testing, version control, and observability

Flowise - Pros & Cons

Pros

  • ✓Open-source core supports self-hosting and source-level customization
  • ✓Visual nodes make agent and retrieval flow structure easier to inspect
  • ✓Broad model, vector-store, API, and deployment options support varied prototypes

Cons

  • ✗Production deployments still require engineering, monitoring, and security work
  • ✗Cloud plan prices and limits could not be verified from readable vendor HTML in this run
  • ✗Complex flows can become difficult to test and govern without naming and versioning discipline

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🔒 Security & Compliance Comparison

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Security FeatureLangflowFlowise
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 Residency—self-hosted deployments allow user-controlled data residency
Data Retentionconfigurableconfigurable
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Read the full reviews to make an informed decision

Review LangflowReview Flowise