| Feature | Dify | LangChain |
|---|---|---|
| Free Plan | ✓ Yes | ✓ Yes |
| Pricing | Free / $59/mo | Free / Usage-based |
| Rating | ★★★★★ 4.6 | ★★★★★ 4.5 |
| Key Feature 1 | Visual workflow builder | Chains and pipelines |
| Key Feature 2 | RAG pipeline | RAG framework |
| Key Feature 3 | Multi-model support | LangGraph |
Reach buyers comparing Dify and LangChain. High-intent traffic, direct conversions.
Dify and LangChain are rated almost identically by users (4.6 vs 4.5), so the right pick comes down to feature fit rather than overall quality. Both Dify and LangChain offer free plans, so you can test both before committing. Dify tends to be favoured by agencies, while LangChain is more popular with enterprises.
Put Dify next to LangChain and the differences surface fast — Dify is built around coding tools while LangChain leans toward agents. Dify is best known for visual workflow builder, whereas LangChain stands out for chains and pipelines. On aggregate user ratings Dify holds a slight edge (4.6/5 vs 4.5/5), though that gap rarely decides the match on its own.
Where Dify pulls clearly ahead is building a customer-facing chatbot with RAG over your own documentation. A frequent plus in reviews: Open-source codebase — self-host for full data control, audit the code, or contribute to the community. LangChain, by contrast, is the stronger choice for building RAG (retrieval-augmented generation) pipelines over document collections. In its favour: The agents tool most professionals already know — reducing onboarding friction and enabling team collaboration from day one. Trying to force either tool outside its lane is where teams usually get frustrated.
Dify is the strongest open-source option for teams building production LLM applications who need more control than no-code tools but less overhead than building from scratch. LangChain is the industry-standard framework for LLM application development — the ecosystem of integrations (100+ LLMs, 50+ vector stores, dozens of tools) is unmatched. For most teams the deciding factor is existing workflow and budget, not a marginal feature gap.
Choose Dify if you are focused on developers and technical teams who want to build and deploy LLM-powered applications — chatbots, RAG pipelines, AI agents, and internal tools — without writing backend AI infrastructure from scratch, or if a big part of your week goes to creating internal AI tools that query your company knowledge base. Its free tier also lets you validate the fit before paying.
Choose LangChain if your priority is python and JavaScript developers building production LLM applications who need a structured framework for chaining AI calls, managing memory, integrating retrieval, and orchestrating agents, especially for creating LLM-powered agents that use tools and APIs autonomously. A free plan is available, so you can trial the workflow at zero cost first.
In day-to-day use, Dify feels strongest at building a customer-facing chatbot with RAG over your own documentation, while LangChain is more at home with building RAG (retrieval-augmented generation) pipelines over document collections.
Learning curve is worth weighing. Dify has a known trade-off — Steeper learning curve than no-code tools. On LangChain's side: Abstraction can obscure what's actually happening. Budget a week or two to get fluent in either before judging the output.
Both tools offer a free plan, so you can trial each side by side before spending anything. Paid plans start at $59/mo for Dify (Professional (Cloud)) and $39/mo for LangChain (LangSmith), making LangChain the cheaper entry point at $39/mo versus $59/mo. The extra spend on Dify only pays off if you need what its higher tier unlocks.
🚀 Ready to decide? Try both free and see which fits your workflow.
Dify is an open-source platform for building production-ready AI applications and agents without deep engineering expertise. Its visual work… Read the full Dify review →
LangChain is the most widely used open-source framework for building LLM-powered applications — providing composable building blocks for cha… Read the full LangChain review →
• Open-source codebase — self-host for full data control, audit the code, or contribute to the community
• Supports all major AI models
• Visual builder, no deep coding needed
• Strong RAG and agent capabilities
• Steeper learning curve than no-code tools
• Self-hosting requires server setup — worth evaluating before committing if this is central to your use case
• The agents tool most professionals already know — reducing onboarding friction and enabling team collaboration from day one
• 90,000+ GitHub stars, huge community
• Model-agnostic from day one — especially for chains and pipelines workflows where LangChain consistently outperforms manual approaches
• LangGraph excels at complex agent logic
• Abstraction can obscure what's actually happening
• Rapid iteration means breaking changes — worth evaluating before committing if this is central to your use case