Dify
Overview
An open-source LLM app development platform that streamlines the creation and management of AI applications with advanced orchestration and RAG capabilities.
Best For Professions:
Dify is an open-source platform designed to simplify the development of AI applications utilizing large language models (LLMs). It integrates Backend-as-a-Service (BaaS) and LLMOps capabilities, offering tools for visual prompt orchestration, retrieval-augmented generation (RAG) pipelines, agent capabilities, model management, and observability features. Dify supports a wide range of LLMs, including proprietary and open-source models, enabling developers to build, test, and deploy AI workflows efficiently. With its intuitive interface, Dify facilitates rapid prototyping and production deployment of generative AI applications, making it a comprehensive solution for AI app development.
Product overview
Capabilities, pricing and fit
Dify is best understood as an agent-building or deployment platform. Dify is the platform for production-ready agentic workflows. Build agents, knowledge pipelines, models, and tools on one canvas, deployable on Cloud, in your VPC, or self-hosted.
Information as of July 30, 2026
What it can do
- Agent building and deployment
Dify is the platform for production-ready agentic workflows. Build agents, knowledge pipelines, models, and tools on one canvas, deployable on Cloud, in your VPC, or self-hosted.[1]
Where it fits best
- Developers seeking to create AI applications with complex workflows and integrations.[1]
Sources (1)
- Dify - The Platform for Production-Ready Agentic WorkflowsOfficial site · checked 2026-07-30
Autonomy level
87/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
Custom Comparisons
Some of the use cases of Dify:
- Developers seeking to create AI applications with complex workflows and integrations.
- Teams aiming to build and deploy AI agents capable of handling sophisticated tasks.
- Organizations looking to implement retrieval-augmented generation (RAG) pipelines for enhanced AI performance.
- Managing and monitoring large language models within AI applications.
- Streamlining the development process from prototyping to production deployment of AI solutions.
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Popularity level: 74%
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