Open-source LLMOps platform for building AI agents, RAG pipelines, and chatbots through a visual workflow builder. Supports all major LLM providers, MCP protocol, and self-hosting under Apache 2.0.
Visual platform for building AI agents and workflows. Drag-and-drop your way to production-ready AI apps with any LLM provider.
Dify is an open-source platform that lets you build AI agents and RAG applications through a visual drag-and-drop workflow builder. Instead of writing orchestration code by hand, you connect nodes representing LLM calls, tool invocations, conditional logic, and data transformations. The result is a production-ready application you can deploy as an API or web app.
The platform supports every major LLM provider out of the box: OpenAI, Anthropic, Google, Mistral, and self-hosted open-source models. You can swap between models without rewriting workflows, which makes cost optimization and A/B testing straightforward. The built-in knowledge base handles document ingestion, chunking, and embedding for RAG pipelines, so you don't need a separate vector database setup for most use cases.
MCP (Model Context Protocol) support sets Dify apart from most visual builders. Your agents can connect to external MCP servers for standardized tool access, which matters as the MCP ecosystem grows. This positions Dify as a bridge between no-code agent building and the protocol-driven interoperability that enterprise teams need.
The self-hosted option under Apache 2.0 is the biggest draw for privacy-conscious teams. You get the full feature set with no usage limits, running on your own infrastructure. The cloud version starts free with 200 message credits and scales to $159/month for the Team plan with 10,000 credits/month and 50 team members.
Where Dify struggles: the visual builder hits a ceiling with very complex custom logic that would be easier expressed in code. Cloud pricing per workspace adds up fast if you run multiple projects. And the 200-credit sandbox is barely enough to evaluate whether the platform fits your needs. For teams that outgrow the visual paradigm, LangChain or LlamaIndex give more programmatic control, though with steeper learning curves.
With over 50,000 GitHub stars and an active community, Dify has become the default recommendation for teams that want visual agent building with self-hosting flexibility. It fills the gap between fully no-code tools (which lack depth) and framework-level tools (which require significant engineering).
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Dify fills the gap between no-code AI builders and code-level frameworks. The visual workflow builder is genuinely useful for RAG apps and agent prototyping, and the self-hosted option under Apache 2.0 makes it the default choice for privacy-conscious teams. Cloud pricing per workspace adds up, and the visual paradigm hits limits with complex logic, but for most agent-building use cases it's the fastest path from idea to production.
Drag-and-drop interface for connecting LLM calls, tools, conditional logic, and data transformations into production workflows. Supports chatflow and standard workflow modes.
Use Case:
A product team builds a customer support agent that routes queries to different knowledge bases based on topic classification, all without writing orchestration code
Built-in document ingestion, chunking, embedding, and retrieval. Upload PDFs, web pages, or text files and Dify handles the vector storage and retrieval automatically.
Use Case:
A legal team uploads 500 contract PDFs and builds a Q&A agent that answers questions about specific clauses with source citations
Agents can connect to external MCP servers for standardized tool access, enabling interoperability with the growing ecosystem of MCP-compatible tools and data sources.
Use Case:
Connecting a Dify agent to an MCP server that exposes your company's internal APIs, letting the agent query databases and trigger workflows through a standard protocol
Support for all major LLM providers with the ability to swap models per node. Run expensive models for complex reasoning and cheaper models for classification in the same workflow.
Use Case:
Using GPT-4o for initial query understanding, then routing to Claude for long-form generation and a local model for PII detection, all in one pipeline
Free
month
$59.00/month
month
$159.00/month
month
Free
forever
Ready to get started with Dify?
View Pricing Options →Teams that need document Q&A, support bots, or internal knowledge search with built-in RAG capabilities and no separate vector database to manage.
Organizations where product managers or analysts need to build and iterate on AI workflows without waiting for engineering resources.
Companies in regulated industries (healthcare, finance, legal) that need full control over their AI infrastructure and data residency.
Developers exploring agent architectures who want to iterate quickly with visual tools before committing to a code-level framework.
We believe in transparent reviews. Here's what Dify doesn't handle well:
Yes. The self-hosted Community Edition runs under Apache 2.0 with the full feature set and no usage limits. You pay only for your own infrastructure (server, database, LLM API keys). There's no separate license fee or hidden enterprise gate on core features.
Dify is a visual platform. LangChain and LlamaIndex are code-level frameworks. Dify is faster for prototyping and accessible to non-engineers, but the visual builder limits flexibility for complex custom logic. Teams that need full programmatic control over every step should use LangChain or LlamaIndex. Teams that want faster iteration and broader team access should consider Dify.
Dify supports OpenAI (GPT-4o, o1), Anthropic (Claude 3.5/4), Google (Gemini), Mistral, Cohere, and self-hosted models via Ollama or compatible APIs. You can use different models for different nodes in the same workflow and switch providers without rebuilding.
Yes, with caveats. The cloud Professional plan supports up to 5,000 messages/month, which is enough for internal tools but tight for customer-facing applications. Self-hosted has no limits beyond your infrastructure. For high-volume production use, self-hosted is the recommended path.
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