Dify Getting Started Guide: A Complete Walkthrough of Low-Code AI App Development

What Is Dify? A Low-Code AI Application Platform Explained

Dify is a low-code platform purpose-built for AI application development. "Low-code" means developers can build fully functional AI applications through a visual drag-and-drop interface with little to no actual coding. This dramatically lowers the barrier to entry, enabling users without deep programming backgrounds to get started quickly.

The concept of Low-Code platforms was formally introduced by Forrester Research in 2014. Its core idea is to compress functionality that would traditionally require hundreds of lines of code into a few configuration steps — achieved through visual modeling, pre-built components, and drag-and-drop interfaces. It's worth noting that low-code is not a new invention born from the AI era. Its roots trace back to "Fourth-Generation Languages" (4GL) in the 1990s and Rapid Application Development (RAD) tools aimed at enterprises in the 2000s. Platforms like Salesforce, OutSystems, and Mendix have been established players in the enterprise market for years. In the AI era, the value of low-code has been amplified further — because AI applications don't just involve frontend and backend development; they also require calling LLM APIs, managing prompts, handling vector databases, and other highly specialized operations that far exceed the complexity of standard web development. Dify emerged in this context, packaging complex AI engineering capabilities into visual nodes so that non-technical users can accomplish work that would otherwise require a dedicated AI engineer.

One interesting detail: although Dify's interface is primarily in English, it is actually a product developed by a Chinese company and marketed internationally. As a result, the official website features a predominantly English interface while also supporting Chinese. This "domestic product going global" positioning gives Dify strong adaptability and a solid user base both in China and abroad.

Five Types of AI Applications Supported by Dify

The core value of the Dify platform lies in its ability to help users build multiple types of AI applications. The platform supports five main application categories, covering everything from simple conversations to complex task automation.

Chatbot and Text Generation

Chatbots are the most basic application type. Users can quickly build a conversational bot through Dify and share it directly with others after publishing. Text generation applications are similar in functionality but are primarily designed for one-off interaction scenarios — better suited for handling single-instance text generation tasks.

Agent

Agents are one of Dify's most valuable application types. Agents can autonomously invoke tools, execute multi-step tasks, and perform more sophisticated automated reasoning — making them a central direction in AI application development today.

The core architecture of an Agent is commonly known as the "ReAct" (Reasoning + Acting) pattern, first proposed jointly by Princeton University and Google Research in 2022. The model first performs reasoning, then decides which tool to call to take action, iterating in a loop until the task is complete. The key breakthrough of this design is that it deeply integrates the language model's chain-of-thought with external tool invocations, allowing the model to go beyond language generation and genuinely affect external systems. The biggest difference from ordinary chatbots is that Agents have "tool-calling" capabilities — they can actively search the internet, read and write files, call APIs, execute code, and more. After OpenAI introduced Function Calling in 2023, Agent development entered an explosive growth phase. Dify builds on this with a graphical tool integration interface, so users don't need to hand-code tool invocation logic; they can simply equip their Agent with capabilities like search, computation, and database querying — significantly reducing the cost of building Agent applications.

Workflow: ChatFlow and Workflow

Under the "Workflow" category, there are actually two distinct modes: ChatFlow and Workflow. The key difference between them is the interaction model — ChatFlow focuses on dialogue-driven process orchestration, while Workflow leans toward automated batch processing of tasks.

From a technical standpoint, ChatFlow maintains conversational history context (Memory), so each process node is aware of prior conversation content. This makes it well-suited for scenarios requiring multi-turn interaction and dynamic path adjustment based on user feedback — such as intelligent customer service or guided questionnaires. Workflow, on the other hand, follows a classic Directed Acyclic Graph (DAG) execution model — the same data structure at the core of workflow engines like Apache Airflow and Prefect. This ensures tasks execute in topological order without circular dependencies, enabling AI tasks to be scheduled and processed in parallel like data pipelines. Tasks flow from start to finish along predefined paths without relying on real-time conversational input, making Workflow better suited for background automation tasks like batch data processing, scheduled report generation, and automated content review. The design inspiration for these two modes comes from conversational AI frameworks (such as LangChain) and classic workflow engines (such as Apache Airflow) respectively — and Dify unifies both within the same visual platform, covering the mainstream scenarios in AI application development.

Among the five application types, Agents and Workflows offer the most practical value for most users, representing Dify's capability boundary from simple conversation all the way to complex task automation.

Broad Large Model Integration Support

As a low-code AI development platform, Dify performs exceptionally well in terms of LLM compatibility. The platform supports virtually all major domestic Chinese models, including DeepSeek, Qianfan, and Zhipu; it also fully supports leading international models such as ChatGPT and Anthropic Claude. This extensive model support allows users to flexibly choose the underlying model based on their actual needs and budget.

Beyond comprehensive model support, Dify also offers the following notable advantages:

• Low barrier to entry: The visual interface enables rapid AI application building with a gentle learning curve, suitable for users without a technical background
• Built-in LLMOps capabilities: The platform includes built-in monitoring that tracks invocations and execution of workflows and applications in real time, giving it complete operational attributes

LLMOps (Large Language Model Operations) is an emerging field that evolved from MLOps (Machine Learning Operations), specifically addressing the management challenges of large language models in production environments. Unlike traditional MLOps, which focuses on model training pipelines and inference service deployment, LLMOps centers on the core challenge of versioning prompt engineering — because the same input can produce different outputs, and model version updates affect all downstream applications. Core LLMOps capabilities include: prompt version management, call chain tracing, cost tracking, output quality evaluation, and anomaly alerting. Notable standalone LLMOps tools in the industry include LangSmith and Helicone. Dify integrates these capabilities directly into the development platform itself, so developers don't need to introduce additional toolchains to achieve full lifecycle management from development to monitoring — delivering especially strong cost-efficiency value for small and mid-sized teams.

With built-in LLMOps capabilities, Dify is not just a development tool — it also provides observability support after applications go live, which is particularly critical for managing AI applications in production environments.

Two Ways to Use Dify: Cloud vs. Local Deployment

Dify offers two primary usage modes. Users can choose flexibly based on their network environment and data requirements.

Online Cloud Usage (Cloud)

The first option is to use Dify online via the official cloud service at cloud.dify.ai. After registering and logging in, you can experience Dify's full feature set with no environment setup required.

It's worth noting that the cloud service may have limited accessibility within China — the site can be slow or unreachable. If you don't have a VPN or proxy tool, it's recommended to consider local deployment instead.

Local Offline Deployment (Recommended for Users in China)

The second option is local offline deployment, which is the primary focus of this guide. Since Dify is an open-source project (open-sourced on GitHub under the Apache 2.0 license), users can download the source code directly to their machine and complete a self-contained deployment in a local environment.

Dify's local deployment is based on the Docker Compose technology stack. Docker Compose is Docker's official multi-container orchestration tool. It uses a single YAML configuration file to define multiple services — their images, port mappings, dependencies, and volume mounts — enabling a "one-command startup" of complex multi-service applications. This is its core advantage over manually installing each dependency. Dify leverages this mechanism to package multiple service components — including the web frontend, API service, worker processes, PostgreSQL database, Redis cache, and Weaviate vector database — into a containerized application. Users simply need to install Docker and run a few commands to complete the entire deployment. Another major advantage of this containerized architecture is environment consistency — whether you're on Windows, macOS, or Linux, the deployment process and runtime behavior are nearly identical, significantly reducing the complexity of environment configuration.

Key advantages of local deployment include:

• No network access restrictions — more stable usage
• Data stays local for better privacy control (all business data, conversation logs, and model invocation records are stored on your own device, meeting data compliance requirements)
• Easier for secondary development and custom modifications

For users in China, local deployment of Dify is the more stable and practical choice for getting started.

Summary

As a domestically developed low-code AI application platform, Dify has become a popular tool for building AI applications thanks to its visual drag-and-drop development, comprehensive LLM integration, and built-in LLMOps monitoring. Whether you're building a chatbot, an Agent, or a complex automated workflow, Dify provides a relatively complete solution. For users looking to quickly get started with AI application development, choosing the local deployment approach to learn Dify is an ideal starting point — low barrier, high controllability.

Key Takeaways