Dify is a platform for production-grade agentic workflows, with the tagline "The Platform for Production-Ready Agentic Workflows." It lets users build agents and RAG pipelines in the same collaborative workspace, with rich support for AI models and tools; it can be deployed in the cloud, in a VPC, or in a self-hosted environment, enabling teams to move from prototype to production without rebuilding their tech stack.
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Purpose and Positioning Dify is used to build and manage production-oriented agentic workflows. Officially, it is said to be able to "democratize AI agent development." By combining powerful AI/ML capabilities with a no-code platform, plus rapid deployment and an intuitive interface, even beginners can get started relatively easily, thereby accelerating citizen development. It is also aimed at enterprise teams, integrating business teams, data, and workflows onto a unified foundation to shorten the cycle from idea to execution.
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Main Capabilities
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Workflow Studio: For visual construction of agentic workflows. Officially described as "Turn Prompt Logic Into a Visible Execution Path," that is, using visual building blocks and AI prompts to define how an application thinks, retrieves data, makes decisions, uses tools, requests human input, and completes tasks, keeping the logic visible for easy testing, debugging, deployment, and handoff.
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Agent Building: Agents can be built through chat or manual configuration, including skills, tools, and knowledge. After building, they can be used as applications, embedded in websites, or reused as agent nodes in workflows.
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Knowledge Pipeline: Create repeatable pipelines for files, websites, online documents, and cloud drives to extract, clean, chunk, and index, and test retrieval performance before the knowledge base is connected to agents or workflows.
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Marketplace: Explore Dify tools, models, and integrations; install model providers, tools, data sources, and MCP integrations from the marketplace, or customize them; teams can reuse approved plugins without wiring each project from scratch.
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Publishing and Monitoring: Dify applications can be published as hosted experiences, API endpoints, embedded components, or MCP-compatible tools; understand operating conditions and continuously improve through logs, feedback, annotations, latency, and usage data.
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Deployment Methods and Versions
- Dify Cloud: A SaaS hosted by Dify, with no need to manage infrastructure, build, testTry and publish AI apps; it provides a hosted workspace for apps, workflows, agents, and knowledge bases, can be published as a web app or API, and scales plans as the team, storage, logs, and usage grow.
- Dify Enterprise: Self-hosted or running in a VPC, aimed at procurement, security, and auditing; provides SSO/SAML, RBAC, audit logs, SOC 2 Type II + ISO 27001, Kubernetes deployment Helm charts, and a dedicated CSM with 24/7 support.
- Community Edition: Open-source community edition, self-hostable, deployed with Docker; GitHub 158K+ stars, uses an Apache-2.0 derivative license, single-command Docker deployment, includes the full agent runtime and workflow editor, and has community Discord and forum support.
IV. Applicable Scenarios The official list includes scenarios such as evaluation and testing (multi-stage review, scaled question generation), manufacturing and industry (a unified private platform across business units), consumer goods and FMCG (enabling business teams to build their own Agents), professional services and auditing (using Agents and RPA to drive audit automation), logistics and supply chain (scaling business volume without proportionally increasing costs), the public sector (local deployment and secure sharing across agencies), banking and financial services (unifying business teams onto one AI platform), pharmaceuticals and life sciences (governed expert-level AI assistants), semiconductors (orchestrating AI on top of proprietary LLMs), education (multiple campuses sharing one knowledge base for services), and internet and media (moving from pilot projects to everyday productivity).
V. Usage and Limitations Users can directly use the hosted version through Cloud, or self-host and deploy in a VPC in enterprise environments; the open-source community edition is self-deployed via Docker. The main construction methods are a visual canvas, prompt orchestration, knowledge pipeline configuration, and plugin integration. The official materials also mention that logic can be published as a web app, API, embed, or MCP-compatible tool. Details such as specific charges, pricing, feature differences between versions, model calling costs, and usage steps are not provided in the materials and are subject to official channels.
