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Dify

IntermediatePlatform1.6K learners

Dify is an open-source LLM application development platform that combines a visual workflow builder, backend-as-a-service, and tools for retrieval-augmented generation and agents into a single environment.

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Definition

Dify is an open-source LLM application development platform that combines a visual workflow builder, backend-as-a-service, and tools for retrieval-augmented generation and agents into a single environment.

Overview

Dify aims to cover more of the LLM application lifecycle than a purely visual flow builder, combining prompt orchestration, a knowledge base for RAG, agent capabilities, and backend infrastructure like API hosting and usage monitoring in one platform. Teams can define application logic visually, connect data sources for retrieval, and configure agents that use tools, then deploy the result as a hosted API or embeddable web app without assembling separate backend services. This positions Dify as both a development tool and an operational platform: it targets teams that want to move from prototype to production LLM application without stitching together a vector database, an orchestration framework like LangChain, and custom backend code themselves. Support for multiple LLM providers means teams are not locked into a single model vendor. Dify competes with other visual and low-code LLM application platforms such as Flowise and Botpress Studio, differentiating itself with a broader focus on the full application backend — including team collaboration, observability, and deployment — rather than just workflow construction.

Key Features

  • Visual workflow builder for orchestrating LLM application logic
  • Built-in knowledge base and retrieval-augmented generation support
  • Agent capabilities with tool integration
  • Backend-as-a-service features including API hosting and usage monitoring
  • Support for multiple LLM providers within one platform
  • Team collaboration features for building and managing applications together
  • Open-source with self-hosted and cloud-hosted options

Use Cases

Building and deploying production-grade LLM applications end-to-end
Creating retrieval-augmented generation applications with a built-in knowledge base
Developing agentic applications that use external tools
Standing up internal AI tools without building custom backend infrastructure
Managing and monitoring LLM application usage across a team

Frequently Asked Questions