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Basic Information

Dify is an open-source platform for developing and running LLM applications and agentic AI workflows. It provides a visual workflow canvas and a Prompt IDE to design, test, and compare prompt behavior and model outputs. The repository contains tooling, deployment configurations, and guides to run Dify locally or self-host it in production using Docker Compose, Kubernetes Helm charts, Terraform, or cloud-specific CDK/AMI options. Dify targets teams that need an integrated environment for RAG pipelines, agent definitions, model management, observability, and API-backed application runtime. It supports cloud-hosted usage via a managed offering as well as a community self-hosted edition. The project includes documentation, examples, and community channels for support and contribution.

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Features
Dify bundles a visual workflow builder, a Prompt IDE for crafting and comparing prompts, and extensive RAG capabilities covering document ingestion and retrieval with text extraction from common formats. It integrates with a wide range of LLMs and inference providers, including GPT, Mistral, and Llama3 and any OpenAI-compatible model. Agent features include LLM function-calling and ReAct style agents with more than 50 built-in agent tools such as web search and image generators. LLMOps features provide observability and logs to monitor application performance and improve prompts, datasets, and models. The platform exposes APIs for Backend-as-a-Service integration and supports enterprise features like access control for production use.
Use Cases
Dify helps teams move from prototype to production by combining workflow design, model selection, retrieval pipelines, agent tooling, and observability into a single platform. Developers can self-host with Docker Compose or deploy to cloud and Kubernetes using community Helm charts, Terraform modules, or AWS CDK patterns provided in the repository. Built-in tools and multi-model support reduce integration effort when experimenting with different providers and models. Observability and LLMOps capabilities enable continuous improvement of prompts and datasets based on real production data. The API-first approach allows integration of Dify capabilities into existing business logic and supports enterprise needs for SSO and access control.

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