Dify

Still writing code repeatedly for the development of AI applications? Enterprises need to customize the knowledge base but stuck in the threshold of getting started, Dify’s emergence has completely changed the development logic of LLM applications – as the benchmark of the domestic open source LLM development platform, it is “visual orchestration + full-process management” as the core, so that developers, product managers and even business people can quickly build chatbots, automated workflows and other AI applications through drag and drop. As the benchmark of domestic open source LLM development platform, it is centered on “visualization orchestration + full process management”, which allows developers, product managers, and even business people to quickly build chatbots, automated workflows, and other AI applications through drag-and-drop, without having to sink into the underlying code development. This article combines the latest version of the 2025 features and test cases to disassemble Dify’s core advantages, usage scenarios and deployment options, helping users with different needs to quickly unlock AI productivity.

First, the core positioning of Dify: LLM application development “omnipotent toolbox”.

Dify’s core mission is to “lower the threshold of AI application development”, it is not a simple model calling tool, but a full life cycle platform that integrates low-code development, RAG knowledge base, LLMOps operation and maintenance. Whether it’s rapid verification of product prototypes or building enterprise-level production applications, Dify provides complete support from design, development, deployment to monitoring, and its positioning can be summarized as “the common choice for three types of users”:

• Non-technical staff: rely on the visual interface zero code to build customer service robots, document Q&A tools;
• Developers: save 80% of the underlying coding time, focusing on the optimization of core business logic;
• Enterprise users: support for private deployment and multi-tenant rights management, taking into account data security and collaboration efficiency.

Different from LangChain’s code-driven and Flowise’s developer-oriented, Dify’s biggest advantage is “balancing ease of use and functionality” – not only retaining the low threshold of visualization, but also opening up custom code nodes, plug-in development, and other advanced features. Dify’s biggest advantage is “balancing ease of use and functionality” – not only retaining the low threshold for visualization, but also opening up custom code nodes, plug-in development, and other advanced features, covering the needs of all scenarios, from personal projects to financial applications. As of July 2025, its GitHub repository has gained 5 million downloads and 800+ contributors, and it has continuously won many awards such as the Most Influential LLMOps Platform of the Year in the Open Source Community, becoming the preferred AI application development tool for domestic developers.

Second, the core function of the actual test: 3 minutes to get started, complex scene full coverage

1. Visualized workflow scheduling: zero code to build complex AI logic.

This is the core function of Dify highlights, by dragging and dropping nodes can be linked to the complete AI processing process, without writing a line of code. The steps to build a “travel assistant” application are as follows:

1. Create a new workflow application, drag and drop the “Conditional Branching”, “Tool Call” and “LLM Generation” nodes from the left component bar to the canvas.
2. Configure the trigger rule: when the user asks for a travel plan within 7 days, the “Weather Forecast” tool will be called automatically;
3. Connecting data flow: Weather data is passed into LLM to generate trip plans with weather adaptation suggestions.
4. Click “Test” to debug in real time, no need to deploy to verify whether the logic is smooth.

The editor supports json variable passing, exception retry mechanism configuration, and even inserts Python/Node.js code nodes to extend the functionality, so that technical and non-technical staff can collaborate to complete complex requirements. For developers, this means no need to repeatedly write tool calls, process control code; for business people, you can also adjust the application logic through the visual interface, without relying on the technical team to iterate.

2. Powerful RAG engine: let AI “read” your proprietary knowledge.

Dify’s built-in Retrieval Augmented Generation (RAG) system can quickly transform documents into a knowledge base that can be called by AI, dramatically reducing the illusion of AI. After uploading a 50-page product manual, it only takes 3 steps to build a Q&A tool:

1. Enter the “Knowledge Base” module, upload PDF/Word/Markdown format files, the system automatically completes the text chunking and vector embedding.
2. Configure search strategy: support vector search, full-text search or mixed mode, adjust relevance threshold and number of returns;
3. Associate the knowledge base to the dialog application, the AI can accurately answer questions based on the content of the document, and can also mark the answer source page number.

Its RAG advantage is to support multi-vector database docking (Weaviate, Qdrant, etc.), and can automatically deal with the document format confusion, redundant information and other issues, a large bank using Dify to build an internal knowledge base, the efficiency of the staff to query the policy documents increased by 70%. As shown in Figure 2, the knowledge base management interface also provides version control and update logs to facilitate teamwork and maintenance.

3. full ecological compatibility: model, tools, deployment of full freedom

Dify’s compatibility covers the whole process of development, no need to worry about technology stack adaptation:

• Model compatibility: seamless integration of GPT series, Llama, Mistral and hundreds of other language models, support for OpenAI API, local Ollama deployment, Baidu Chifan and other domestic model access, switching models only need to modify the configuration;.
• Tools ecosystem: built-in 50+ common tools (Google search, web crawler, email sending, etc.), support for custom API tool development, can be docked to the internal system (such as ERP, CRM);.
• Deployment: Support cloud hosting (minute-level on-line) and private deployment (Docker Compose one-click start), to meet the needs of individual developers for rapid verification and enterprise data compliance.

The actual test of local deployment requires only 3 steps: cloning the warehouse, configure .env file, start the container cluster, within 30 minutes to complete the production-level environment setup, compared with other platforms of complex configuration to significantly save time.

4. 2025 New Feature: Embedded Components and Multi-Modal Support

The 2025 Dify v1.0.0 release brings two core upgrades to further broaden the application scenarios:

• Embedded website components: AI customer service and Q&A modules can be implanted into the official website by copying the code snippets, supporting CSS variables to customize the style, completing the intelligent transformation of the official website without secondary development, and increasing the efficiency by 300%.
• Multi-modal task scheduling: support image processing, voice interaction (TTS/ASR) and cross-platform message push tandem, can build “image recognition → text extraction → report generation → email sending” full chain automation tool.

Typical use scenarios: from personal tools to enterprise-level applications.

1. Enterprise intelligent customer service: low-cost construction of exclusive Q&A system

No technical team development, operations personnel can build customer service robots through Dify: upload product manuals and FAQ documents, configure the dialogue process and transfer manual rules, one key deployment as a web plug-in or docking WeChat public number. After an e-commerce platform, customer service response time was shortened from 10 minutes to 3 seconds, and the resolution rate of repeated questions reached 85%.

2. Developer efficiency tools: rapid construction of internal automation processes

Developers can build a personalized workflow through Dify, for example, “code error analysis → solution retrieval → email reminder” tool: access to GitHub WebHook, when there is an error report, automatically retrieve Stack Overflow, generate a solution and push it to the mailbox, which can save 40% of the time for problem troubleshooting. Troubleshooting time.

3. Financial-grade AI gateway: meeting compliance and regulatory requirements

Dify’s enterprise-level features (multi-tenant permissions, audit logs, sensitive data desensitization) have passed the requirements of Level 3 of the Equalization Guarantee, and a large bank has adopted it to build an LLM gateway system to achieve centralized supervision and security control of the bank’s AI applications, which not only safeguards data security, but also improves the efficiency of the AI applications on line.

Comparison with similar tools: why choose Dify?

Data source: real test experience and official document collation

V. Deployment and usage considerations

1. Deployment Requirements: 2-core 4GB RAM server is recommended for private deployment, supporting Linux kernel 3.10 or above, and Docker 20.10.x or above.
2. Performance Optimization: When dealing with large-scale knowledge base, it is recommended to connect to external vector databases (such as Milvus), and enable quantization acceleration technology to reduce resource consumption.
3. Open source agreement: using Apache License 2.0, allowing commercial and secondary development, only need to retain the original copyright notice, enterprises can rest assured that the use of ;……….
4. Avoid pit points: custom tools need to strictly configure the API authentication and response resolution rules to avoid data leakage; sensitive scenarios recommend enabling JWT token authentication and log retention.

Summary: AI development democratization of core tools

Dify’s emergence broke the “AI application development can only be completed by technical personnel” barrier, through the visualization of scheduling, full ecological compatibility and low-threshold deployment, so that users of different backgrounds can release AI productivity. Whether it’s for individual developers to quickly validate their ideas, small and medium-sized enterprises to build business tools, or large enterprises to build compliance-grade AI systems, Dify can provide solutions to meet their needs.

As the open source project with the first growth rate of GitHub LLM tools, Dify’s community ecosystem continues to grow, and will follow up with cross-repository comparisons, personalized templates, and other features to further lower the development threshold. Now visit the official website ( https://www.dify-china.com ) to experience the cloud version for free, or get the open source code to build a private environment through the GitHub repository, to start your low-code AI development journey.