Dify
Open-source platform for building LLM apps and AI agents with visual workflows, RAG, and 100+ model providers
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About Dify
Dify is an open-source platform for building LLM applications and AI agents, bringing visual workflows, retrieval-augmented generation, and broad model support together on one canvas. It is built for developers, citizen developers, and teams who want to ship internal assistants, chatbots, and agentic tools without stitching several separate products together. Startups use it to validate ideas quickly, while larger teams lean on its production-oriented features.
What stands out is how much lives in one place. The same builder handles drag-and-drop workflows, RAG pipelines grounded in your own documents, and agents that call tools and APIs, with native Model Context Protocol support and a large plugin marketplace. It connects to many model providers including OpenAI-compatible APIs and local models through Ollama, so you are not locked to a single vendor. Built-in observability helps you watch how those apps behave once they are running.
Licensed on terms based on Apache 2.0, it can be self-hosted for free, which matters when you need to keep data inside your own infrastructure, and a managed cloud option exists on a freemium basis. With a very active community and frequent releases, it is a solid foundation for a private, multi-model LLM stack.
Key Features
- Visual drag-and-drop workflow builder with branching, iteration, and parallel steps
- Built-in RAG pipeline covering ingestion, chunking, embedding, retrieval, and reranking
- Agents using LLM Function Calling or ReAct with 50+ built-in tools
- Integrates hundreds of models across 100+ proprietary and open-source providers
- Self-hostable via Docker Compose alongside a managed cloud option
- Observability, prompt testing, and logging baked into the app stack
Pros & Cons
What we like
- Genuinely open source under a license based on Apache 2.0, so you can self-host for free
- One canvas covers workflows, RAG, and agents instead of stitching several tools together
- Broad model support means you are not locked to a single LLM vendor
- Large active community with 140k-plus GitHub stars and frequent releases
Room for improvement
- Self-hosting needs Docker plus your own model API keys and ongoing maintenance
- Embedding Dify inside a SaaS you sell requires a separate commercial license
- Cloud message credits are capped per tier and can run out on heavy workloads
- Complex multi-branch workflows still take real effort to design and debug
Frequently Asked Questions
What is Dify?
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Who is Dify best for?
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Reviews (11)
Recommended without reservation
Dify solves a real problem for me without making a fuss about it. The output quality holds up better than I expected.
Worth the price of admission
Dify has quietly become part of my daily flow. Their take on broad model support means you are not locked to a single llm vendor is genuinely good. Nothing flashy, it simply pulls its weight every day. Mostly using it for prototyping and deploying agentic workflows that call tools and apis. Would sign up again without thinking twice.
Recommended without reservation
Almost a year on Dify now, no plans to leave. Where it really wins is integrates hundreds of models across 100+ proprietary and open-source providers. Would sign up again without thinking twice.
Best decision this quarter
Hadn't planned on switching, but Dify was hard to ignore. Real selling point for me was self-hostable via docker compose alongside a managed cloud option. It handles the boring parts so I can focus on the work that matters. Mostly using it for building rag search over pdfs, slides, and knowledge bases. Would sign up again without thinking twice.
Powerful once it clicks
Tried Dify on a side project first, then rolled it out everywhere. Where it really wins is agents using llm function calling or react with 50+ built-in tools. It has shaved real time off my week. My only gripe is cloud message credits are capped per tier and can run out on heavy workloads. Worth the price for what I get out of it.
Worth the price of admission
Three months of Dify later, here is what holds up. Real selling point for me was self-hostable via docker compose alongside a managed cloud option. It is the rare tool that got better the more I used it. It has been a fit for self-hosting a private llm app stack to keep data in your own infrastructure. Hard to imagine going back to my old setup.
Genuinely impressed
Tried Dify on a side project first, then rolled it out everywhere. It just works, day after day, without surprises. It handles the boring parts so I can focus on the work that matters. Glad I made the switch.
Pulled its weight from week one
Dify solves a real problem for me without making a fuss about it. Their take on self-hostable via docker compose alongside a managed cloud option is genuinely good. Performance has been steady even when I lean on it hard.
Best decision this quarter
Onboarded the whole team to Dify in an afternoon. The interface stays out of my way, which I appreciate. It has been a fit for building rag search over pdfs, slides, and knowledge bases. Hard to imagine going back to my old setup.
It just works
Have been running Dify for a while, here is where I land. The thing I keep coming back to is how reliable it is. Mostly using it for shipping internal ai assistants and chatbots grounded in company documents. Hard to imagine going back to my old setup.
Quietly excellent
Three months of Dify later, here is what holds up. What stands out is how it handles observability, prompt testing, and logging baked into the app stack. It earns its place in my stack.
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