What Is Dify? Understanding Why AI Workflows Are Taking Off

If you think of Dify as a tool where you "drag a few blocks and AI does the work automatically," you've only got half the picture. What really sets it apart is that it brings AI models, knowledge bases, tools, workflows, and the final application into a single system.

Start with the most basic way of using AI: you enter a question, hand it to the model, the model generates an answer, and that's it. The whole process has just three nodes—input, model, output. But real-world work is rarely that simple.

Take receiving a customer email. Step one is to determine whether the customer is asking about after-sales service, a refund, or a product inquiry. Step two is to look up information in different knowledge bases based on the type. Step three is to hand that information to the AI to generate a reply. If a refund is involved, you enter another branch: small amounts are handled automatically, large amounts are escalated to a human for review, and after review an external tool is called to send out the result.

This is no longer "asking AI a question"—it's a complete chain: input comes in, goes through classification, retrieval, judgment, generation, and tool calls, and only then produces a result. This is the most important step in understanding AI workflows, and what Dify does is turn the steps that used to require writing a lot of code to connect into a flowchart you can see directly.

The Core of Dify: Visual AI Application Building

You can think of Dify as a visual building platform for AI applications. It brings Workflow, Agent, knowledge base, model management, plugins, and app publishing into one platform, and the apps you build can be published directly as web apps or integrated into other systems via API.

When you open Dify, what you see isn't an ordinary chat box but a canvas: a start node on the left, which can connect to a large model, then to knowledge retrieval, then to a conditional check—condition one goes left, condition two goes right—then a tool call, and finally the answer output. The logic that used to be buried in code is now laid out right in front of you.

This is also the first standout feature of Dify: it's not simply connecting a few pieces of software together. Much traditional automation is more like "receive email → save attachment → write to spreadsheet → send a notification"—good at connecting different software and fixed actions. AI workflows add a very special role in the middle—the model.

The model doesn't just execute commands; it can understand content, classify, extract information, generate text, and even decide where to go next based on the current result. So the process shifts from the relatively fixed "A to B, B to C" to "A comes in, AI understands first, then judges, then decides whether to go to B or C." That extra layer of understanding and decision-making in the middle is what makes AI workflows very different from ordinary automation, and many of Dify's design choices revolve around this layer.

Swappable Models: Not Locked to Any Single Provider

The second feature of Dify is that models aren't hard-coded. You can think of the model in a workflow as a swappable engine: today you connect GPT, tomorrow you switch to Claude, another node uses Gemini, or you even connect other models or your own self-hosted models.

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Back to Dify itself: its job is to organize these models, tools, and workflows together. The apps you build aren't necessarily locked to a single model provider, and this matters. Because within the same workflow, different tasks may suit different models: one node handles summarization, one handles classification, another handles more complex reasoning—you can even put different models into the same flowchart.

In real engineering, different models vary greatly in capability and cost. For example, tasks like classification and information extraction can be handled by lightweight models, which are low-cost and fast; while complex reasoning and long-document summarization may require stronger models. Dify lets you choose different models at different nodes of the same workflow, meaning you can flexibly mix and match based on task characteristics—controlling cost while ensuring quality. What's more, swappable models also reduce the risk of vendor lock-in: when a model service fails or changes its pricing, you can switch quickly without rebuilding the entire application.

Knowledge Base and RAG: Letting AI Answer Based on Materials

The third feature that's especially suited to AI scenarios is the knowledge base. Suppose you have a hundred internal company documents. The traditional approach isn't to let AI answer out of thin air, but to import the documents first, where they're processed, split, and indexed; when a user asks a question, the system first retrieves relevant content, then hands that content to the model to generate an answer. This is what everyone knows as RAG (Retrieval-Augmented Generation).

Dify puts the knowledge base and knowledge processing workflow directly into the AI application building layer. You no longer need to piece together a full set of document processing, retrieval, model calls, and a front-end interface from scratch just to build an enterprise knowledge assistant.

The core idea of RAG (Retrieval-Augmented Generation) is: rather than relying on knowledge stored in the model's parameters, first retrieve document fragments relevant to the question from an external knowledge base before generating an answer, then hand those fragments to the model as context. The benefit is that answers can be based on the latest, private, or domain-specific materials, significantly reducing the model's "making things up," and information sources can be traced. A typical RAG pipeline includes document loading, text splitting, vectorization, index building, similarity retrieval, and generation—among which the text-splitting strategy and retrieval quality directly affect the final result.

Hands-On Demo: What a Complete AI Workflow Looks Like

Suppose you run an e-commerce company and receive a large volume of user feedback every day. Step one, AI determines whether the feedback is a product issue, a logistics issue, or a refund issue; step two, it enters different branches based on the classification; step three, it looks up handling rules in the corresponding knowledge base; step four, the model generates suggestions according to the rules; step five, special cases are escalated to a human, and if all is well, a tool is called to pass the result to the next system.

Now look at the whole process: user feedback is on the far left, the final handling result is on the far right, and in the middle is not a single AI but an entire AI system.

This is exactly why AI workflows are getting more and more attention. People are gradually realizing that the AI that truly enters work is often not a smarter chat box, but multiple capabilities starting to connect: the model handles understanding, the knowledge base provides materials, tools execute actions, conditional checks choose routes, and the workflow ties everything together. Dify's value is putting these originally scattered modules into an intuitive interface. After a workflow is built, it can be packaged into an app and offered directly as a web interface, or connected via API to existing website backends and business systems. If you want to control your data and deployment environment yourself, Dify also supports self-hosting.

A Word of Caution: Visual Doesn't Mean No Technical Knowledge Needed

One misconception to avoid here: visual doesn't mean you don't need to understand technology at all. Why nodes are connected this way, where the model should go, when to query the knowledge base, when to let AI judge, and when a human must step in—Dify won't solve these questions for you automatically. What it lowers is the barrier to turning ideas into reality, but what really determines whether an AI workflow works well is still your understanding of the business process.

And not every task is worth turning into a workflow. If you just occasionally ask AI to summarize a piece of text, opening ChatGPT and asking directly may be faster; but if the same thing happens repeatedly every day and involves looking up materials, making judgments, calling tools, and taking different branches, that's when the value of a workflow truly emerges.

Conclusion

On the surface, Dify is a canvas made up of many nodes; one layer down, it's a connector for models, knowledge bases, agents, tools, and workflows; one more layer down, it solves a bigger problem—how to turn "I ask AI a question" into "AI completes a task following a process." From a chat box to a workflow, and from a workflow to a truly runnable AI application. Once you understand this, you basically understand why tools like Dify are gaining more and more attention alongside AI workflows.