Introduction
In the past two years, large language models (LLMs) have advanced rapidly, and AI application development has entered a stage of explosive growth. From intelligent assistants to knowledge management and enterprise automation, nearly every tech team is experimenting with building next-generation applications powered by LLMs. In other words, LLM application development is gradually becoming a mainstream direction in software engineering.
However, developers often face a crucial choice: should we use a low-level framework or a full-fledged platform?
- Frameworks provide maximum flexibility, allowing developers to build complex systems from scratch.
- Platforms, on the other hand, focus on usability and speed, enabling teams to bring ideas to life quickly.
Today, two tools have become iconic representatives of these two paths: LangChain and Dify.
LangChain vs Dify
LangChain
- Positioning: An open-source Python/JS library
- Core: A toolkit for LLM orchestration and modular components, including Chains, Agents, Memory, and Tools
- Tendency: A developer-centric SDK with high flexibility
LangChain is best known as “LEGO for LLM apps.” Developers can freely combine modules to meet different needs, but they must also handle engineering complexity themselves—such as prompt management, data storage, monitoring, and logging.
Dify
- Positioning: An all-in-one AI application development platform (supporting both SaaS hosting and self-deployment)
- Core: Visual app builder, prompt studio, RAG workflows, and unified API
- Tendency: Product-oriented delivery, designed to lower barriers for teams
Compared to LangChain, Dify is more like a full package. It is not just a library but a complete platform, combining both UI and APIs. This allows even non-engineers to participate in app development while still offering enough flexibility for technical teams.
Architecture and Ecosystem
From an architecture perspective:
- LangChain focuses on low-level capabilities. To form a complete application, it typically needs to be combined with external components such as LlamaIndex, vector databases, and API gateways.
- Dify, by contrast, integrates many of these elements at the platform level. It comes with built-in support for RAG and prompt management, reducing repetitive work.
From an ecosystem perspective:
- LangChain has a massive and mature community, with hundreds of thousands of GitHub stars and widespread adoption in tutorials and demos.
- Dify is relatively new but growing quickly, especially among small teams and enterprise innovation groups.
Feature Comparison
Dimension | LangChain | Dify |
Application orchestration | Extremely flexible, but code-intensive | Visual workflows, low-code or even no-code |
Prompt management | Not built-in; requires external tools | Built-in prompt studio |
Data integration (RAG) | Depends on external components | Built-in vector database and retrieval |
Deployment | Fully self-hosted, maximum control | SaaS hosting + self-deployment options |
Community ecosystem | Mature and large | Emerging but rapidly growing |
Typical scenarios | AI Agents, complex workflows | Enterprise apps, rapid prototyping, internal tools |
Developer Experience
- LangChain feels more “engineering-oriented.” If you want to deeply control application logic or build multi-agent collaboration systems, it provides the flexibility you need. But you’ll also spend more time handling debugging, logging, deployment, and scalability.
- Dify, by contrast, feels like a “ready-to-use stage.” With drag-and-drop interfaces, document upload, and simple configurations, you can create an app quickly. For enterprises, its admin console, access control, and deployment options make it even more practical.
Cost and Maintenance
- LangChain is completely open-source and free, but it requires strong engineering capability within your team to maintain infrastructure.
- Dify is also open-source, but many teams may choose its SaaS hosting to reduce operational overhead. This introduces additional costs and some level of vendor lock-in.
In short, the cost of LangChain lies in human resources, while the cost of Dify lies in services.
Who Should Choose Which?
- Engineering-driven teams / tech-focused companies: If you need highly customized workflows or want to experiment with advanced logic at the LLM layer, LangChain is the better choice. It excels at building complex AI agents, cross-system workflows, and even research prototypes.
- Product managers / small teams / enterprise innovation groups: If your priority is quick iteration, validation, and delivery, Dify is more suitable. It frees you from most engineering hassles, allowing you to focus on business logic and user experience.
Conclusion
LangChain and Dify are not in a zero-sum competition; rather, they exist on different layers of the stack:
- LangChain is a “framework for LLM apps,” offering endless possibilities but requiring significant engineering effort.
- Dify is an “all-in-one platform,” helping teams move quickly from ideas to production.
In practice, the two can even complement each other:
- Start with Dify to validate ideas and launch a working demo fast.
- Then refine the underlying logic with LangChain to achieve scalability and advanced customization.
In today’s flourishing ecosystem, choosing tools is not the end goal. The real question is: What problem are you solving, and what strengths does your team have?