Complete comparison of Dify, Flowise, and LangFlow for building AI workflows. Features, architecture, and which open-source LLM platform to choose in 2025.
Dify vs Flowise vs LangFlow: Which Open Source AI Workflow Builder in 2025?
Building AI applications shouldn't require months of custom development. Open-source AI workflow builders let you create RAG systems, chatbots, and AI agents visually.
Three platforms dominate: Dify, Flowise, and LangFlow.
All are open source. All let you build AI apps without deep coding. But they serve different needs.
Here's how to choose.
The Quick Comparison
| Factor |
Dify |
Flowise |
LangFlow |
| GitHub Stars |
58,000+ |
30,000+ |
42,000+ |
| Best For |
App building, RAG |
Quick prototypes |
Complex graphs |
| Approach |
Polished platform |
Lightweight, simple |
LangChain visual |
| Control Flow |
If/else, loops |
Basic if/else |
Loops (beta) |
| Deployment |
Cloud + self-host |
Self-host focused |
Cloud (DataStax) |
| Enterprise Ready |
Most ready |
Basic |
Growing |
TL;DR:
- Choose Dify for polished app building with RAG
- Choose Flowise for simple, stable self-hosted workflows
- Choose LangFlow for complex, graph-based agent workflows
Understanding the Landscape
What These Tools Do
All three let you:
- Build AI workflows visually (drag-and-drop)
- Connect to various LLMs (OpenAI, Anthropic, local)
- Implement RAG (retrieval augmented generation)
- Create chatbots and AI assistants
- Deploy without extensive coding
Why Open Source Matters
- No vendor lock-in - Own your infrastructure
- Full customization - Modify anything
- Cost control - No per-seat licensing
- Data privacy - Everything stays on your servers
- Community - Active development and support
Dify: The Polished Platform
Dify has the most complete, polished experience. It feels like a commercial product that happens to be open source.
Key Features
App Studio
Build complete AI applications, not just workflows:
- Chatbots with UI
- Text generation apps
- Agent assistants
- API endpoints
RAG Engine
Best-in-class document processing:
- Multiple file types (PDF, Word, web pages)
- Automatic chunking
- Embedding generation
- Vector store management
Control Flow
Most advanced logic:
- If/else conditions
- For loops (sequential + parallel)
- Error handling
- Branching
Built-in UI Generation
Generate user-facing chat interfaces automatically.
Strengths
- Most polished UX - Clean, intuitive interface
- Fastest prototyping - Minutes to working app
- Best RAG - Document handling just works
- Debug tools - Execution logs, sandboxed testing
- Security - Closest to enterprise-ready
Limitations
- Opinionated architecture
- Less flexible for edge cases
- Smaller component library than LangFlow
Best For
- Rapid prototyping with citizen developers
- RAG applications
- Teams wanting polished, ready-to-use platform
- Non-technical users building AI apps
Flowise: The Simple Workhorse
Flowise prioritizes simplicity and stability. It's lightweight, easy to deploy, and predictable.
Key Features
Minimal Setup
One command to run:
npx flowise start
Visual Builder
Drag nodes, connect them, done. No complexity hiding.
Multi-Channel Deploy
Built-in integrations:
- Telegram
- WhatsApp
- Discord
- Slack
- Embed widget
Predictable Behavior
What you build is what you get. No magic, no surprises.
Strengths
- Easiest to deploy - Single command
- Most stable - Mature, predictable
- Best channel integrations - Deploy to chat platforms easily
- Lightweight - Minimal resource requirements
- Simple mental model - Easy to understand
Limitations
- Basic control flow (if/else only)
- No loops or advanced logic
- Simpler UI than Dify
- Fewer governance features
Best For
- Quick prototypes
- Simple chatbots
- Multi-channel deployment
- Resource-constrained environments
- Teams wanting stability over features
LangFlow: The Graph Powerhouse
LangFlow (acquired by DataStax) provides the deepest LangChain integration and most flexible graph building.
Key Features
LangChain Native
Every LangChain component available as visual node:
- All LLMs
- All retrievers
- All tools
- All agents
Complex Graphs
Build sophisticated multi-step workflows:
- Multiple branches
- Loops (beta, February 2025)
- Conditional routing
- Agent orchestration
DataStax Backing
Acquisition means:
- Stable funding
- Enterprise focus
- Cloud hosting option
- Continued development
Strengths
- Most flexible - Build almost anything
- Deep customization - Access to all LangChain
- Complex agents - Multi-agent support
- Growing ecosystem - Active community
- Backed by DataStax - Financial stability
Limitations
- Steeper learning curve
- Active security vulnerabilities (improving)
- More complex than needed for simple use cases
- Loop support still maturing
Best For
- Complex agent workflows
- Developers comfortable with LangChain
- Teams needing maximum flexibility
- Multi-agent orchestration
Head-to-Head: Building a RAG Chatbot
Let's compare building the same thing on each platform.
Goal
Chatbot that answers questions from uploaded documents.
Dify Implementation
- Create new "Chat App"
- Upload documents to knowledge base
- Configure retrieval settings
- Set system prompt
- Publish
Time: ~15 minutes
Skill required: Minimal
Flowise Implementation
- Drag Document Loader node
- Connect to Text Splitter
- Connect to Embeddings
- Connect to Vector Store
- Add Retrieval Chain
- Connect Chat Model
- Test and deploy
Time: ~30 minutes
Skill required: Basic understanding of RAG
LangFlow Implementation
- Add document nodes
- Configure chunking
- Set up embeddings
- Create retriever
- Build agent graph
- Configure memory
- Test and iterate
Time: ~45 minutes
Skill required: Understanding of LangChain concepts
Verdict: Dify fastest for RAG. Flowise for simple cases. LangFlow when you need customization.
Head-to-Head: Control Flow
Dify
- If/else conditions: Yes
- For loops: Yes (sequential + parallel)
- Error handling: Yes
- Iteration: Yes
Most complete control flow.
Flowise
- If/else conditions: Yes
- For loops: No
- Error handling: Limited
- Iteration: No
Basic but reliable.
LangFlow
- If/else conditions: Yes
- For loops: Beta (February 2025)
- Error handling: Limited
- Iteration: In development
Catching up quickly.
Verdict: Dify leads. LangFlow improving. Flowise intentionally simple.
Head-to-Head: Enterprise Readiness
Security Posture
Dify: Most secure of the three. Approaching enterprise-ready.
Flowise: Stable but basic security features.
LangFlow: Active vulnerabilities being addressed. Improving with DataStax resources.
Compliance Features
Dify:
- Role-based access control
- Audit logging
- Team management
- SSO (enterprise)
Flowise:
- Basic access control
- Limited audit features
- No SSO
LangFlow:
- Growing enterprise features
- DataStax enterprise tier
- API keys and authentication
Verdict: Dify is most enterprise-ready. LangFlow closing gap.
Head-to-Head: Community and Development
GitHub Activity (December 2025)
| Metric |
Dify |
Flowise |
LangFlow |
| Stars |
58k |
30k |
42k |
| Growth Rate |
Very high |
Stable |
High |
| Contributors |
400+ |
200+ |
300+ |
| Issues Response |
Fast |
Moderate |
Fast |
Backing
- Dify: VC funded startup
- Flowise: Community driven
- LangFlow: DataStax acquired
Verdict: All actively developed. Dify and LangFlow have strongest backing.
Deployment Options
Dify
- Cloud: dify.ai hosted service
- Self-host: Docker, Kubernetes
- Enterprise: Private cloud, support
Flowise
- Self-host: Docker, npm
- Cloud: Various community options
- Lightweight: Single container
LangFlow
- Cloud: DataStax Astra
- Self-host: Docker, pip
- Enterprise: DataStax enterprise
Verdict: All support self-hosting. Flowise is lightest. Dify and LangFlow have commercial cloud options.
Pricing
Dify
| Tier |
Cost |
| Self-hosted |
Free (open source) |
| Cloud Sandbox |
Free tier |
| Cloud Pro |
From $59/month |
| Enterprise |
Custom |
Flowise
| Tier |
Cost |
| Self-hosted |
Free (open source) |
| Everything is self-host |
$0 |
LangFlow
| Tier |
Cost |
| Self-hosted |
Free (open source) |
| DataStax Cloud |
Usage-based |
| Enterprise |
Custom |
Verdict: All free to self-host. Commercial options vary.
When to Choose Each
Choose Dify If:
- You want the most polished experience without rough edges
- RAG is your primary use case - best document handling
- You're building apps, not just workflows
- Non-technical team members need to use it
- Enterprise security is a requirement
Choose Flowise If:
- Simplicity is paramount - you want predictable, stable
- Multi-channel deployment - Telegram, WhatsApp, etc.
- Minimal resources available for hosting
- Quick prototypes without learning curve
- You don't need complex logic like loops
Choose LangFlow If:
- Maximum flexibility is required
- You know LangChain and want visual building
- Complex agent orchestration is the goal
- DataStax ecosystem alignment helps
- You need features others don't have yet
The Practical Reality
For most teams starting with open-source AI platforms:
Start with Dify if:
- Building customer-facing AI apps
- RAG is involved
- Multiple team members need access
Start with Flowise if:
- Building simple chatbots
- Need multi-channel deployment
- Want minimal operational overhead
Start with LangFlow if:
- Building complex agent systems
- Already familiar with LangChain
- Need maximum customization
Migration Paths
Moving between platforms isn't trivial. Each has different:
- Node/component structures
- Configuration formats
- Deployment models
Recommendation: Invest time in choosing the right platform upfront. Migration costs exceed selection costs.
Frequently Asked Questions
Which open-source AI platform has the most features?
Dify has the most complete feature set with if/else conditions, loops, error handling, and polished RAG support. LangFlow has the most components via LangChain integration. Flowise is intentionally simpler.
Is Dify really enterprise-ready?
Dify is the closest of the three to enterprise readiness with role-based access control, audit logging, and team management. However, evaluate your specific security and compliance requirements before deploying to production.
Can I build multi-agent systems with these platforms?
Yes. LangFlow is strongest for multi-agent orchestration with its graph-based approach. Dify supports agents with tools. Flowise handles simpler agent patterns.
What's the easiest platform to self-host?
Flowise. Single command (npx flowise start) and you're running. Dify requires Docker Compose with multiple containers. LangFlow is somewhere between.
Do I need to know LangChain to use LangFlow?
Understanding LangChain concepts helps significantly with LangFlow since it exposes LangChain components directly. For Dify and Flowise, LangChain knowledge is helpful but not required.
Which platform is best for RAG applications?
Dify has the best RAG experience with built-in document processing, chunking, and retrieval. It handles the complexity automatically. Flowise and LangFlow require more manual configuration for RAG.
The Bottom Line
The open-source AI platform space is maturing rapidly. All three options are viable for production use.
Dify for polished, RAG-focused applications.
Flowise for simple, stable, multi-channel deployment.
LangFlow for complex, graph-based agent systems.
For most teams, Dify offers the best balance of capability and usability. Start there unless you have specific needs that push you to alternatives.
The future of AI development is visual, collaborative, and open source. These platforms are leading that future.
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