Dify is an open-source and highly extensible LLM (Large Language Model) application development platform designed to help developers, product managers, and business experts build production-grade AI applications with minimal coding. Its product positioning is to become the "AI application operating system" that connects foundation models with business scenarios. Through visual orchestration, full lifecycle management, and enterprise-grade security governance, it abstracts complex large model capabilities into reusable workflows, Agents, and RAG pipelines, significantly shortening the cycle from prototype validation to large-scale deployment. The R&D background stems from deep insights into pain points such as "high engineering costs, low iteration efficiency, and difficult private deployment" when general models are put into practice. Since its open-source release in 2023, Dify has rapidly accumulated tens of thousands of developers and hundreds of enterprise customers worldwide. By 2026, it has formed a complete technology foundation centered on model neutrality, hybrid deployment, and observability, becoming a recognized benchmark project in the AI infrastructure field.

The core technology architecture encompasses three pillars: first, a streaming orchestration engine supporting complex conditional branches, loops, and parallel tasks, with an integrated interactive debugging panel; second, an enterprise-grade RAG pipeline including multi-format document parsing, vectorization, hybrid retrieval, re-ranking, and citation tracing to ensure answer accuracy and explainability; third, an Agent runtime that natively supports ReAct, Function Calling, and multi-agent collaboration modes, seamlessly integrating custom tools and external APIs. In terms of industry standing, Dify, with its Apache 2.0 open-source license, active community plugin ecosystem, and neutral stance across clouds and models, has long ranked at the top of GitHub's LLM toolchain lists. It has been rated by Gartner and Forrester as one of the most representative agent development platforms of 2026.

• Visual Workflow Orchestration - Build complex AI processes via drag-and-drop nodes (e.g., LLM, knowledge retrieval, code execution, HTTP requests), supporting conditional branches, loops, parallelism, and error handling, with real-time in-canvas debugging to lower the development threshold.
• Enterprise-Grade RAG Pipeline - Built-in multi-format document parsing (PDF, Word, HTML, Markdown, etc.), chunking strategies, vector database integration (supporting Weaviate, Qdrant, Milvus, etc.), as well as hybrid retrieval and re-ranking, significantly improving answer quality while providing cited sources.
• Agent Framework - Supports ReAct mode, Function Calling, custom tools (OpenAPI, custom Python functions), and multi-agent collaboration, enabling automatic planning and tool invocation for complex tasks, with deep interaction with external systems.
• Model Neutrality and Multi-Provider Integration - Natively compatible with OpenAI, Anthropic, Google Gemini, Llama 3, and domestic models (Qwen, ERNIE, Moonshot, etc.), with unified API key management and model switching for optimal cost-effectiveness balance.
• All-in-One Application Management - Provides a complete lifecycle from prompt management, log tracking, annotation feedback to version rollback, with an embeddable web chat component and API output formats (RESTful, SDK) for easy integration into existing business systems.
• Observability and Security Governance - Offers fine-grained access control (RBAC), sensitive information masking, audit logs, and integration with monitoring systems like Prometheus, meeting enterprise compliance requirements.

• Registration and Initialization - Visit the official website dify.ai to register a cloud service account, or self-host with one click via Docker Compose (open-source version). After login, enter the console to create a blank application or use a preset template (e.g., customer service bot, knowledge base Q&A, Agent).
• Configure Models and Data - In "Settings - Model Providers", fill in your LLM API key (e.g., OpenAI, Azure, etc.). You can also upload documents or connect external data sources (Notion, databases, etc.) in the "Knowledge Base" module; the system automatically handles chunking and vectorization.
• Orchestrate Application Flow - In the "Orchestrate" canvas, drag nodes from the left (e.g., Start, LLM, Knowledge Retrieval, Question Classifier), connect them, and configure parameters. For Agent applications, add tool definitions and write system prompts. After setup, click "Run" for interactive testing and view intermediate results in real time.
• Publish and Integrate - After testing passes, click "Publish" to obtain an access link or API endpoint. You can embed the chat window into a webpage (iframe/JS code) or connect via RESTful API to your business system. Additionally, configure logs, monitoring, and annotation feedback for continuous model optimization.

Example 1: Building an Enterprise Knowledge Base Q&A Assistant
Upload 20 product manuals (PDF and Word) to the knowledge base. In the orchestration canvas, use a "Knowledge Retrieval" node + an LLM node, and set a prompt template: "Based on the following knowledge snippets, answer the user's question accurately. If not found, reply 'Not recorded.' Output must include source citations." Input the question "How to export the user's monthly report?" The expected output will include specific steps and cite the corresponding document page numbers.

Example 2: Developing a Recruiting Screening Agent That Calls External Tools
Define the Agent's tool: connect to the company's ATS system (e.g., Lever) via API. Set the system prompt: "You are a senior technical interviewer. Screen candidates based on the job requirements, call the ATS API to retrieve resume details, and automatically score them." When a user inputs "Help me find 5 candidates with 5 years of Python experience," the Agent will proactively call the tool, retrieve and return candidate names, matching scores, and recommendations.

Example 3: Automating Customer Email Ticket Processing
Create an "Email Classification and Reply" workflow: the input node receives email subject and body, uses an LLM to classify (refund, technical issue, complaint), and based on the classification, executes different processing nodes in parallel (calling internal APIs to create tickets or generating draft replies), finally outputting suggested actions. You can call it periodically via API to significantly boost customer service efficiency.

• Open Source and Portability - Source code is open under Apache 2.0, allowing free private deployment and eliminating data leakage risks; meanwhile, community-contributed plugins and templates continuously enrich the ecosystem, forming a virtuous cycle.
• Model Neutrality and Low Lock-in Costs - Seamlessly switch among multiple major LLM providers, avoiding lock-in to a single model. You can flexibly choose models based on task complexity to control costs, offering significantly higher cost-performance than closed platforms tied to a single vendor.
• Visual Production-Grade Engineering Capabilities - Not only suitable for rapid prototyping, but also includes production-required features like high availability, monitoring, logging, and version management. The engineering path from 0 to 1 to 100 is smooth, with low operational overhead.
• Comprehensive RAG Performance Optimization - Deep integration of hybrid retrieval, re-ranking, citation tracing, and fine-tuning interfaces. In knowledge-based Q&A scenarios, accuracy improves by over 30% compared to typical vector database solutions, significantly reducing hallucinations.
• Flexible Hybrid Deployment and Embedding - Supports cloud SaaS, private VPC, and edge deployment. Provides rich APIs and SDKs (Python/Node.js), embeddable into any business system, with multi-tenant isolation.

Comparison with Similar Competitors: Key competitors in this space include LangChain/LangGraph (rich ecosystem but lower-level), Flowise (known for visual design but weaker enterprise features), Coze (ByteDance, closed-source), and Baidu Qianfan or Alibaba Bailian (cloud vendor-bound). Dify differentiates from LangChain by providing interactive visual orchestration and out-of-the-box managed services, greatly reducing the learning curve. Compared to Flowise, Dify leads significantly in fine-grained RAG control, multi-tool Agent collaboration, and team collaboration features, making it more suitable for enterprise deployment. Compared to Coze, Dify is open-source and model-neutral, allowing local deployment and custom models with full data sovereignty. Compared to cloud vendor products, Dify supports multi-cloud and private deployment, preventing lock-in to a single cloud service. Overall, Dify holds a significant advantage in balancing "flexibility + ease of use + openness + enterprise readiness."

Dify adopts a dual business model of open-source and cloud services:

• Free Community Edition (Open Source) - Downloadable from GitHub and self-deployed with no feature limitations (including workflows, RAG, Agent, etc.), suitable for individuals and small-to-medium teams. Requires self-management of servers and databases.
• Cloud Service Free Tier - The sandbox version (Free) registered on dify.ai provides 200 message invocations per month, suitable for trials and learning.
• Professional (Pro) Edition - Approximately $59/month (or discounted with annual billing), including 10,000 messages/month, upgraded private knowledge base capacity, priority support, and custom domain.
• Team Edition - Approximately $179/month, with 50,000 messages, multi-user collaboration space (unlimited members), advanced security controls (SSO, audit logs), and a dedicated customer success manager.
• Enterprise Edition - Custom pricing, providing private cloud/VPC deployment, SLA guarantees, custom development, and deep integration with existing systems (including on-premise model access).
• API Billing - If applications are invoked via the open API, additional charges are based on message count (approximately $0.01 per message), with discounts for Team Edition and above. The open-source version only incurs the cost of your own model API.

• Software Developers and DevOps Engineers - Those who want to quickly integrate LLMs into business systems, build AI features using APIs and front-end components, and reduce boilerplate code through visual orchestration.
• AI Product Managers and Solution Architects - Responsible for productizing applications like intelligent customer service and knowledge assistants, needing to quickly validate ideas and deliver demonstration MVPs while ensuring production-grade performance and maintainability.
• Enterprise Operations and Digital Transformation Teams - Such as HR, legal, and operations departments that need to automate document processing, tickets, or knowledge management but lack coding capabilities; they can use preset templates and natural language configuration to complete tasks.
• Academic Researchers and Data Scientists - Those conducting RAG experiments or Agent research can leverage its open-source nature to customize internal algorithms, integrate specialized models, and quickly set up comparative experimental environments.
• Independent Developers and Freelancers - Using the free version or low-cost cloud version to rapidly deliver AI outsourcing projects or build personal products, greatly lowering technical barriers.

• Intelligent Customer Service and Ticket Processing - Combine enterprise knowledge bases to automatically answer high-frequency questions, automatically classify complex complaints and route to human agents, and generate reply summaries, improving customer service efficiency by over 60%.
• Enterprise Knowledge Management and Internal Search - Build an internal employee Q&A engine supporting multiple formats like PPT and PDF, providing precise answers with sources to support decision-making.
• Content Creation and Marketing Automation - Workflows can chain drafting, rewriting, SEO optimization, and style adjustment, using multilingual models to generate press releases, social media copy, and email marketing campaigns.
• Programming Assistance and Code Review Assistant - Integrate code repositories and CI/CD tools via Agents, answering development questions based on repository context, generating unit tests, and analyzing error logs.
• Multilingual Localization and Translation - Use RAG to store terminology databases and style guides, ensuring LLM outputs strictly adhere to industry terms and corporate tone, suitable for high-quality translation of product documentation, legal contracts, and more.
• Data Insights and Report Generation - Connect databases or Excel, use natural language queries to automatically generate charts and business analysis reports, helping management monitor operational status in real time.