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Dify vs LangGraph
Side-by-side comparison of Dify and LangGraph — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | Dify | LangGraph | Winner |
|---|---|---|---|
Ease of Use | 4/5 | 3/5 | Dify |
Scalability | 5/5 | 5/5 | Tie |
Documentation | 5/5 | 4/5 | Dify |
Community | 5/5 | 4/5 | Dify |
Performance | 4/5 | 5/5 | LangGraph |
Overall: Dify wins 3 categories, LangGraph wins 1, 1 tied
Feature Comparison
| Feature | Dify | LangGraph |
|---|---|---|
| Primary Language | Python | Python |
| License | Apache-2.0 | MIT |
| Pricing | Open Source + Cloud | Open Source + Commercial |
| GitHub Stars | 100,000 | 19,900 |
| Difficulty | Intermediate | Advanced |
| Enterprise Ready | ||
| Community Size | Very Large | Large |
| Category | Visual Development | Multi-Agent Systems |
Pros & Cons
Dify
Advantages
Comprehensive LLMOps platform
Visual agent building with code flexibility
Strong community (100k+ stars)
Apache 2.0 license
Production-ready with observability
Multi-agent orchestration
Cloud and self-host options
Limitations
Can be complex to set up initially
Docker-based deployment required
Learning curve for full platform
Cloud pricing for managed version
LangGraph
Advantages
Full control over agent behavior with low-level primitives
Excellent for complex non-linear workflows
Built-in state persistence and memory management
Production-proven by major companies (Klarna Uber LinkedIn)
Strong streaming and observability features
Human-in-the-loop support is first-class
Can be used standalone or with LangChain
MIT licensed with commercial platform option
Limitations
Steeper learning curve than LangChain
Requires understanding of graph theory concepts
May be overkill for simple linear workflows
Smaller community than LangChain (but growing)
Some advanced features require LangGraph Platform
Documentation still maturing compared to LangChain
More complex setup for basic use cases
Best Use Cases
Dify
LLMOps and model management
Visual agent building
RAG applications
Multi-agent orchestration
Internal AI tools and assistants
API-based AI services
LangGraph
Complex customer support workflows with escalation
Multi-agent research and analysis systems
Task management and orchestration
Long-running business process automation
Interactive assistants with memory
Decision support systems with conditional logic
Getting Started
Dify
Installation
docker-compose up or pip install
LangGraph
Installation
pip install langgraph
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