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Dify

Open-source LLMOps platform with visual workflow builder, RAG engine, and model management for production AI apps.

View on GitHub Official site
Workflow Automation Apache 2.0 Low setup 60,000 stars

Overview

Plain English

Open-source LLMOps platform with visual workflow builder, RAG engine, and model management for production AI apps.

Technical

Open-source LLMOps platform with visual workflow builder, RAG engine, and model management for production AI apps.

Technical scorecard

License Apache 2.0
Commercial use Yes
OpenAI-compatible API No
REST API No
Fine-tuning support No
Quantization support No
Docker available No
GUI / no-code available No
Telemetry None
Offline after setup Yes

Data & Privacy

Does it send data online?

After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.

Does it store history?

Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.

License checks?

Commercial use is marked as allowed or likely allowed by the listed license.

Telemetry?

None

Last verified: May 16, 2026. Maintainer verification should be treated as directory guidance, not legal advice.

Setup & Installation

Low

GUI, low-resource, or simple install path likely available.

Prerequisites

Python, TypeScript / JavaScript, Docker, Kubernetes 

# Start with the official project documentation
# https://dify.ai/

Hardware Requirements

RAM8 GB minimum / 16 GB recommended
Hardware tagsCPU Only
Model formatsNot specified
Primary languagePython, TypeScript / JavaScript

Works Well With

AutoGPT AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters. Open WebUI User-friendly AI Interface (Supports Ollama, OpenAI API, ...). LangChain The agent engineering platform. RAGFlow RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs.
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