githuganswered from live GitHub & Hugging Face data
Dify vs LangChain — which platform is better for building agentic workflows?
When evaluating Dify and LangChain for building agentic workflows, a critical differentiation emerges. Dify is thriving with usability and rich tooling. However, LangChain leads in adaptability and integration capabilities across various AI components.
Here's a detailed comparison:
| Feature | Dify | LangChain |
|---|---|---|
| Stars | 152,149 | 144,038 |
| Latest Release | v1.16.1 (2026-07-28) | langchain-core 1.5.4 (2026-08-11) |
| Language | TypeScript | Python |
| Maintenance Status | ✅ Actively maintained (last updated 0 days ago) | ✅ Actively maintained (last updated 0 days ago) |
| Open Issues | 1004 | 431 |
| License | NOASSERTION (commercial use unclear) | MIT (commercial use allowed) |
| Key Features | Focus on workflows, RAG pipelines, intuitive for prototyping | Component interoperability, modular architecture, rapid prototyping |
Verdict
Dify shines for teams looking for a user-friendly platform to rapidly move from prototypes to production without complex setup. Its framework for building agent-based workflows is compelling, especially in collaborative contexts.
LangChain, on the other hand, offers more extensive flexibility for integration and detailed control over workflow components. Its robust community support and extensive documentation also bolster its position, allowing for more complex applications.
Recommendations
Choose Dify if you prioritize ease of use and quick deployment for agentic workflows. Opt for LangChain for more complex projects that require extensive integration capabilities and want to leverage a wide range of AI models and components.