Dify Getting Started: A Complete Guide to Building AI Workflows Without Code

What Is Dify? Build AI Applications Without Writing a Single Line of Code

A common question from those new to AI development: do you really need to code to build AI applications? The answer is — not necessarily. Dify (pronounced "dee-fai") is a platform built specifically to lower the barrier to AI application development.

In simple terms, Dify is a visual AI application building platform, falling under the category of LLMOps (Large Language Model Operations). What sets Dify apart is its open-source-first approach — the core codebase is hosted on GitHub and supports private enterprise deployment, which clearly differentiates it from purely cloud-based SaaS products like Coze. Its business model mirrors GitLab: the community edition is completely free and open source, while the enterprise edition offers advanced organizational features such as SSO single sign-on, granular permission management, and audit logging. This "Open Core" strategy lets Dify attract individual developers and small teams with low friction, while also meeting large enterprises' demands for data sovereignty and private deployment.

LLMOps evolved from MLOps. MLOps (Machine Learning Operations) emerged around 2018, aiming to bring DevOps principles into machine learning engineering to solve the challenge of moving models from research into production. The rise of large language models introduced a new set of engineering concerns: prompt engineering replaced traditional feature engineering, inference cost replaced training cost, and hallucination management and context window handling became central problems.

It's worth understanding the fundamental shift in engineering focus between LLMOps and traditional MLOps. Traditional MLOps centers on model accuracy, training duration, and feature importance; LLMOps centers on cost per thousand tokens, Time to First Token (TTFT), prompt cache hit rate, and hallucination frequency. This means LLMOps platforms must have built-in token usage dashboards, prompt version comparison tools, and output quality evaluation frameworks — Dify's "Logs & Annotations" module is designed precisely to address these challenges. Tokens are the basic unit LLMs use to process text: roughly one English word equals 1–1.5 tokens, and one Chinese character equals approximately 1.5–2 tokens. Since models like GPT-4 are billed per token, accurate token usage monitoring directly impacts enterprise AI cost control — one of the core functions that distinguishes LLMOps platforms from traditional DevOps tooling.

LLMOps platforms are purpose-built for these challenges, providing tooling across the full lifecycle of developing, deploying, and operating large language models. Unlike traditional software development, LLM application development requires handling prompt engineering, context management, model switching, and output quality evaluation. Dify wraps these complex operations into drag-and-drop nodes through a visual interface, enabling users without technical backgrounds to participate in building AI applications without writing complex backend logic from scratch.

If you've used Coze before, picking up Dify will be even easier. Whereas Coze supports only two application types — Agent and Workflow-powered agents — Dify supports five application types: three simpler types for beginners, plus two more advanced forms: Workflow and Chatflow.

It's worth noting that these five types represent two fundamentally different AI orchestration philosophies. Workflow is deterministic: developers pre-define node order and data flow paths, making execution predictable and traceable — ideal for fixed business processes. Agent mode is autonomous: the large model independently decides which tools to call and how many steps to take based on the user's goal, offering greater flexibility but less predictable outputs.

The underlying implementation of Agent is typically based on the ReAct (Reasoning + Acting) framework — at each step, the model first reasons (outputting its thought process), then decides which action to take, forming a loop of "think → act → observe → think again" until the goal is reached. The ReAct framework was jointly proposed by researchers at Google and Princeton in 2022. Its core innovation lies in fusing Chain-of-Thought reasoning with external tool invocation into a unified reasoning-action loop, giving models the ability to self-correct: when a tool call returns an error, the model can detect the anomaly during the "observe" phase and adjust its strategy in the next "reasoning" round, rather than blindly continuing. This mechanism makes Agents more robust when facing missing information or tool failures, and is the underlying engine behind Dify's Agent mode completing complex multi-step tasks.

It's important to note that ReAct is not the only Agent implementation paradigm. Others include the Plan-and-Execute architecture (plan all steps first, then execute them sequentially) and the Reflexion architecture (deep retrospection on historical errors via a linguistic reflection mechanism). Dify currently adopts the ReAct paradigm, whose advantages are step-by-step visibility and ease of debugging — though it can fall into local loops for complex tasks requiring long-horizon planning. Understanding this background helps developers set appropriate maximum iteration steps for Agents in Dify, avoiding unnecessary token consumption from infinite loops.

Chatflow combines both worlds: it supports workflow node orchestration within a multi-turn conversational framework, balancing conversational continuity with process determinism — particularly suited for complex business scenarios requiring sustained interaction.

Both Workflow and Chatflow are fundamentally "workflow" types. Dify separates them to distinguish different use cases — more on this later.

The Real Competitiveness of Chinese-Built AI Tools

In enterprise AI adoption, choosing the right workflow tool matters. When evaluating ease of use, feature completeness, and user-friendliness, Dify ranks among the top in its category, followed by Coze. Compared to international tools like N8N and RagFlow, the Chinese-developed tools represented by Dify are actually more mature in terms of functionality and usability.

Chinese AI tools have advanced rapidly in recent years, now fully capable of competing head-to-head with leading international tools on both feature breadth and interface quality — which is the core reason Dify is worth prioritizing.

Deploying Dify 1.8.0: Cleaner Than Ever

This guide uses Dify's latest version 1.8.0. Compared to earlier versions, deployment configuration has been significantly simplified, making it easy for newcomers to get up and running quickly.

Deployment Steps Walkthrough

Dify uses Docker Compose for containerized deployment. Docker is a lightweight container technology that packages an application and all its dependencies into a standardized image, ensuring consistent behavior across any environment — solving the classic "it works on my machine" problem. Unlike virtual machines (VMs), Docker containers share the host OS kernel, reducing startup time from minutes to seconds and dramatically lowering resource overhead. This is why Dify can run multiple concurrent services smoothly on an ordinary development machine.

Dify's full operation depends on multiple microservices working together: Nginx as a reverse proxy for frontend requests, an API service for core business logic, a Worker service for async tasks, PostgreSQL for structured data storage, Redis for caching and message queuing, and Weaviate or Qdrant as vector databases for embedding data. Docker Compose defines all service images, network connections, mounted volumes, and startup order in a single YAML configuration file, compressing what would otherwise require manually installing and configuring a dozen components into a single command — which is exactly why docker compose up -d achieves one-command startup.

Understanding this microservice architecture is equally important for ongoing operations. When Dify responds slowly, you can use docker stats to observe CPU and memory usage across containers, quickly identifying whether it's API service overload or a vector database retrieval bottleneck. For data migration, simply back up the PostgreSQL data volume and the vector database's persistence directory to fully restore the entire application state. As two mainstream vector databases, Qdrant and Weaviate each have their strengths: Qdrant is written in Rust and performs better in high-concurrency, low-latency scenarios; Weaviate has a built-in GraphQL query interface and multi-modal data support, making it better suited for complex knowledge base scenarios requiring hybrid search. Dify 1.8.0 defaults to built-in vector storage, which is fully sufficient for mid-small scale applications handling under 100,000 daily retrievals; production environments can switch to an independently deployed vector database instance as needed.

The entire deployment process can be summarized in a few key steps:

1. Extract the package: Use cd in the command line to navigate into the Dify directory and extract the files.
2. Enter the Docker directory: After extraction, switch to the docker subfolder.
3. Configure the environment variables file: This is the critical step — inside the Docker directory there is a .env.example file that needs to be renamed to .env. This file contains all required environment variables, including database connection info, key configurations, and other system-level parameters. The most important field in .env is SECRET_KEY, used to encrypt user sessions and sensitive data. In production, always replace it with a randomly generated strong key rather than using the default value from the example file, as leaving it unchanged poses a security risk.

Note: Older versions required extensive additional environment configuration. In version 1.8.0, these tedious steps are no longer necessary — once you rename the .env file, you're ready to start.

4. One-command startup: Run docker compose up -d. Dify will automatically pull the images and complete startup. The -d flag runs the services in detached (daemon) mode, so they continue running after you close the terminal.

Image Size and Download Speed

Many people worry about large image sizes. In practice, the total size of all 1.8.0 images is approximately 5–6 GB — a reasonable range.

With an appropriate mirror source configured, download speeds are noticeably faster than in earlier versions, and the pull-and-launch process typically completes in a short time. The new version also fixes several known bugs that previously frustrated users, with significantly improved overall stability — though you may still occasionally encounter minor issues to watch out for.

The Interface After Logging In

Once started, open your browser and navigate to the deployment address to access the Dify login screen. The overall layout includes the following core modules:

• Explore: Browse and experience ready-made application templates.

• Studio: Create and manage your AI applications — the primary workspace.

• Knowledge: Manage document data used for RAG retrieval. RAG (Retrieval-Augmented Generation) was formally introduced by Meta AI in 2020 and is the mainstream solution for addressing large language model "hallucination" problems. Its technical implementation has two phases: an offline phase that splits documents into chunks, converts them into high-dimensional vectors via an embedding model, and stores them in a vector database; and an online phase that vectorizes the user's query, retrieves the most relevant document chunks via cosine similarity, and injects those chunks into the prompt context so the model can generate answers based on real source material.

  The choice of document chunking strategy (Chunk Size) is critical: chunks that are too large introduce noise that hurts retrieval precision, while chunks that are too small fragment semantic continuity. Dify provides both automatic segmentation and custom segmentation modes — beginners can start with default parameters and tune based on actual retrieval results. Advanced users can also enable the Reranking feature in Dify: the coarse retrieval stage quickly recalls Top-K candidate chunks via vector similarity, and the reranking stage uses a Cross-Encoder reranking model to score candidate chunks against the original query for semantic match, filtering out the truly relevant content to inject into context. This two-stage retrieval strategy can improve RAG system answer accuracy by 15–30%, and is the recommended configuration for enterprise-grade knowledge base applications.

  It's also worth noting that RAG retrieval quality is deeply influenced by the choice of embedding model. Embedding models convert text into semantic vectors, and different models vary significantly in Chinese language performance. OpenAI's text-embedding-3-large excels in general English scenarios, while BAAI's bge-large-zh-v1.5 and Alibaba's GTE series have a clear edge in Chinese knowledge base scenarios. Dify supports freely switching embedding model providers, allowing enterprise users to run A/B tests to select the best vectorization approach for their knowledge base's language composition and domain — fundamentally improving semantic matching quality in knowledge retrieval. Compared to fine-tuning models, RAG offers lower knowledge update costs and better private data security, making it the core technical foundation for enterprise AI customer service and internal knowledge management.

• Tools: Configure and invoke various external tools, including search engines, code executors, HTTP requests, and other extended capabilities.

Start by Creating Your First Application

Dify's core value lies in the Studio module. Inside Studio, click "Create Blank App" to start building your AI application.

For beginners, it's recommended to start with the simplest application types, gradually understanding what each node does, before moving on to the more powerful Workflow and Chatflow orchestration forms. With a step-by-step approach, most people can fully master the complete flow — from environment deployment to workflow building — within about a week.

Conclusion: Dify Is the Ideal Starting Point for AI Development

Dify's greatest strength is visualizing and modularizing the complex AI application development process — you don't need to be a programming expert to build useful AI workflow applications.

• On the deployment side: Based on a Docker Compose containerized architecture, version 1.8.0 has greatly simplified the configuration process and is beginner-friendly.
• On the feature side: Five application types cover everything from simple Q&A to complex workflow orchestration, and RAG knowledge base support lets AI applications draw on private data.
• On competitiveness: Dify leads its category in head-to-head comparisons of LLMOps tools; its open-source private deployment capability gives it a unique advantage in enterprise scenarios where data security is sensitive.

For developers and product professionals looking to enter the AI application development space, Dify is an ideal starting point — low barrier to entry, high ceiling for capability. From here, you can explore each application type hands-on and truly learn AI workflow development from the ground up.