Dify

Dify is an open-source LLM application development platform for production-grade Agentic workflows. It enables the building of Agents, knowledge pipelines (RAG) and workflows on a unified canvas, and supports cloud, VPC or self-hosted deployment.

AI Agent Platform#AI Agent#Open Source#LLM

Updated: 2026-10-26

What is Dify?

Dify's official positioning is "a platform for production-grade Agentic workflows". It places Agents, knowledge pipelines (RAG), models, and tools on the same collaborative canvas for construction, and supports deployment on the cloud, your own VPC, or full self-hosting. The official emphasis is that teams can move from prototype to production without rewriting their tech stack. For developers, it transforms prompt logic into visual execution paths, serving as the bridge between an LLM application's idea and its product. The official also highlights that it makes AI Agent development accessible to everyone-combining the no-code platform's features with an intuitive interface, even beginners can get started quickly. Developers and enterprises worldwide are using it. It is particularly suitable for team-based projects that require long-term maintenance and multi-person collaboration. It upgrades "prompt engineering" to "process engineering", turning the units for debugging and reuse from sentences into nodes, making applications more controllable and transferable.

Who is it for?

For development and product teams that truly want to implement LLM applications into production, especially enterprises with requirements that data must not leave the intranet and prefer self-hosting, as well as individual developers who want to quickly build RAG question-answering or Agent applications. Whether teams are conducting prototype verification, enterprises are carrying out private deployment, or individual developers are quickly building RAG applications, they can all find corresponding solutions among the three deployment paths.

Key features

Workflow Orchestration

Transforms prompt logic into visual execution paths, facilitating debugging, optimization, and team handovers.

Agent Construction

Configure skills, tools and knowledge for the Agent to build an intelligent agent that can perform real tasks. The three elements can be freely combined according to scenarios and become more and more aligned with the business with continuous use.

Knowledge Pipeline (RAG)

Prepares the context required for retrieval in AI applications and supports question answering based on private data and documents.

Plugin Ecosystem

Integrate different models and tools into a unified workspace to flexibly compose your own AI tech stack.

Unified Publishing

The same set of logic can be released as an application, API, or tool, built once and called multiple times.

Release and Monitoring

Supports runtime monitoring after application release, enabling continuous observation of effects and iterations, which aligns with the usage habits of production environments.

No-Code Friendly

The platform emphasizes its positioning of making AI Agent development accessible to everyone. Its intuitive interface and rapid deployment make it suitable for beginners, and it is also adopted by developers and enterprises worldwide.

Multiple Deployment Options

Three paths including Dify Cloud, Enterprise Edition, and Community Edition (self-hosted) can be selected according to team needs.

How to use Dify

First, use your own data to build a prototype of a knowledge Q&A or Agent, and confirm the effect and cost before deciding on the deployment form.
Newcomers can start with official examples and templates, familiarize themselves with the nodes on the canvas, and then process their own business data.
During team collaboration, unify the workflow and plug-in usage specifications as early as possible to avoid building independently and making it difficult to maintain, which would instead turn the tool into new technical debt.
Before going online, prepare monitoring and rollback plans.
When changing the model, first verify the effect on the canvas before switching to reduce the impact on online applications.

Things to know

The Community Edition is suitable for initial verification and internal use; wait until the workflow is stable and you require permission management, operational guarantees, and technical support before considering the Enterprise Edition.

The key to good RAG performance lies in the data quality and chunking strategy of the knowledge pipeline; don't expect to fix poor retrieval accuracy just by switching to a more powerful model.

Self-hosting requires you to maintain the infrastructure yourself; model call fees are charged separately based on the API of the model used, and you should calculate the total cost in advance.

The platform is evolving rapidly, with its interface and capabilities continuously updated. Pay attention to the timeliness of tutorial content and refer to the official documentation as the authoritative source; for the configuration differences between the Community Edition and the Cloud Edition, as well as version upgrade paths, consult the documentation first to avoid migration costs exceeding expectations.