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Dify

An open-source LLMOps platform for visually building, deploying, and managing AI-native applications like RAG systems, agents, and chatbots.

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Dify: Open-Source LLM Application Development Platform

Introduction

Dify (github.com/langgenius/dify) is an open-source LLM (Large Language Model) application development platform designed to simplify and accelerate the creation, deployment, and management of AI-native applications. It positions itself as an intuitive LLMOps platform, enabling developers, product managers, and AI teams to go from prototype to production quickly.

Developed by LangGenius, Dify combines a visual interface with powerful backend capabilities, supporting a wide range of functionalities including AI workflow orchestration, Retrieval Augmented Generation (RAG) pipelines, AI agent creation, prompt engineering, model management, and observability. It offers both a self-hostable community edition and a managed Dify Cloud service.

Key Features

Dify provides a comprehensive suite of tools for building and operating LLM-powered applications:

  • Visual Application Orchestration (Workflow Studio):
    • Design and build complex AI applications (chatbots, text generation tools, RAG systems, AI agents) using a drag-and-drop visual interface.
    • Visually define workflows, connect different nodes (LLMs, knowledge bases, tools, code execution), and manage data flow.
  • Prompt Engineering & Management (Prompt IDE):
    • Intuitive interface for crafting, testing, versioning, and comparing the performance of prompts with different models.
    • Supports various prompt variable types and orchestration modes (simple, assistant, flow).
  • Retrieval Augmented Generation (RAG) Engine:
    • Build and manage knowledge bases from diverse data sources (PDFs, PPTs, TXT, Markdown, CSV, HTML, Notion, web pages, APIs).
    • Supports document ingestion, cleaning, chunking, embedding, and retrieval.
    • Integrates with various vector databases (e.g., Weaviate, Qdrant, Milvus, TiDB Vector) for efficient semantic search.
    • Offers hybrid search, multi-path retrieval, and reranking capabilities.
  • AI Agent Capabilities:
    • Define agents based on LLM Function Calling or ReAct patterns.
    • Equip agents with pre-built (50+) or custom tools (e.g., Google Search, DALL·E, Stable Diffusion, WolframAlpha, API tools via OpenAPI Spec).
    • Visually orchestrate agent workflows and debug their execution.
  • Comprehensive LLM Support:
    • Seamless integration with hundreds of proprietary LLMs (OpenAI GPT series, Anthropic Claude series, Azure OpenAI) and open-source models (Llama series, Mistral, ChatGLM, etc.).
    • Supports models from various inference providers and self-hosted solutions like Ollama, Hugging Face, Replicate, AWS Bedrock, Google Vertex AI, NVIDIA NIM, and any OpenAI API-compatible models.
    • A detailed list of model providers is available in the Dify documentation.
  • Backend-as-a-Service (BaaS):
    • Automatically generates APIs for your deployed AI applications, allowing easy integration into existing systems or frontends.
  • Monitoring & Observability:
    • Track application logs, performance metrics, and user interactions over time.
    • Analyze token usage and costs associated with LLM calls.
  • Deployment Options:
    • Self-Hosting (Community Edition): Deploy Dify on your own infrastructure using Docker Compose or Kubernetes for full data control and customization.
    • Dify Cloud: A fully managed SaaS offering for quick setup and use without infrastructure management.
  • Data Management & Annotation:
    • Tools for managing datasets for knowledge bases.
    • Features for data annotation and labeling to improve quality.
  • Team Collaboration:
    • Workspaces designed for team collaboration, allowing multiple members to work on applications.
  • Open Source & Extensibility:
    • The core Dify platform is open-source, licensed under a modified Apache 2.0 license (Dify Open Source License).
    • Allows for community contributions and potential for extending functionalities.
  • Security:
    • Features for managing API keys securely. Dify Cloud encrypts sensitive information at rest.

Specific Use Cases

Dify's versatile platform can be used to build a wide array of AI-native applications:

  • Intelligent Chatbots & Customer Service Assistants: Create conversational AI that can understand user queries, access knowledge bases for contextual answers, and integrate with external tools.
  • Content Generation Tools: Develop applications for drafting articles, summaries, marketing copy, code, and other text-based content.
  • Internal Knowledge Base Search: Build powerful semantic search interfaces for internal documentation, wikis, and databases, making information easily accessible to employees.
  • AI Agents for Task Automation: Design agents that can perform specific tasks, interact with other services, and make decisions (e.g., research agents, data entry automation, e-commerce assistants).
  • Rapid Prototyping of LLM-Powered Features: Quickly build and test new AI features before full-scale development.
  • Backend for AI-Native Applications: Use Dify to manage the LLM logic, RAG, and agent capabilities, exposing them via APIs to custom frontends or other applications.
  • Educational Tools & Tutors: Create AI-powered learning assistants.
  • Research & Analysis Tools: Develop applications that can process and analyze large volumes of text data.

Usage Guide

Getting started with Dify involves setting up an instance (Cloud or self-hosted) and then using its visual interface to build your application.

  1. Accessing Dify:

    • Dify Cloud: Sign up at dify.ai. They offer a free Sandbox plan to get started, typically requiring a GitHub/Google account and an OpenAI API key for initial model use.
    • Self-Hosting (Docker Compose - Recommended):
      • Ensure Docker and Docker Compose are installed.
      • Clone the repository: git clone https://github.com/langgenius/dify.git
      • Navigate to the Docker directory: cd dify/docker
      • Copy the example environment file: cp .env.example .env (and customize if needed).
      • Start the containers: docker compose up -d
      • Access Dify at http://localhost/install for initial admin setup, then http://localhost.
      • Refer to Dify's official documentation for detailed self-hosting instructions, including minimum system requirements (CPU >= 2 Core, RAM >= 4 GiB, typically more for production) and configurations for different environments or vector databases.

  2. Creating an Application:

    • Log in to your Dify instance.
    • Create a new application, choosing a type (e.g., Chatbot, Text Generation, Agent).
    • Prompt Engineering: Design and refine your prompts in the Prompt IDE. Define variables, select models, and preview outputs.
    • Knowledge Base (for RAG):
      • Create a new knowledge base.
      • Upload documents (PDF, TXT, MD, etc.) or connect data sources. Dify will handle indexing and embedding.
      • Configure retrieval strategies (e.g., N-choose-1, multi-path, hybrid search).
    • Agent Configuration (if building an Agent):
      • Define the agent's role, prompt, and reasoning strategy (e.g., Function Calling, ReAct).
      • Add and configure tools the agent can use (built-in or custom).
    • Workflow Design (for more complex apps):
      • Use the visual workflow studio to connect different nodes (LLM calls, knowledge retrieval, conditional logic, code execution, tools).
    • Model Selection: Choose from a wide array of supported LLMs and configure their settings.
    • Test & Iterate: Use the debugging and preview features to test your application's responses and behavior.
    • Deploy: Once satisfied, you can deploy your application. Dify provides API endpoints and sometimes embeddable web apps.

Supported LLMs and Vector Stores

Dify supports a vast and growing list of LLMs and model providers, including:

  • Proprietary Models: OpenAI (GPT-4, GPT-3.5-Turbo, GPT-4o, etc.), Anthropic (Claude 2, Claude 3 series), Azure OpenAI Service, Google (Gemini models).
  • Open-Source Models (often via hosting providers or local inference): Llama series, Mistral models, ChatGLM, and many more.
  • Model Providers & Platforms: Hugging Face, Replicate, AWS Bedrock, NVIDIA NIM & API Catalog, OpenRouter, Cohere, Together.ai, Ollama, GroqCloud, Xinference, LocalAI, OpenLLM, and any OpenAI API-Compatible endpoints.
  • Vector Stores: Dify's RAG engine can integrate with various vector stores. While it might use a default or built-in option, users can often configure it to use external vector databases like Weaviate, Qdrant, Milvus, Pinecone, TiDB Serverless Vector Search, etc., especially in self-hosted deployments.

For the most up-to-date list, refer to the "List of Model Providers" and RAG configuration sections in the official Dify documentation (https://docs.dify.ai/).

Pricing & Plans

  • Dify Self-Hosted (Community Edition):
    • The software is free to use under its open-source license (Apache 2.0 with Dify Community Agreement conditions).
    • Users are responsible for the costs of their own infrastructure (servers for Dify, databases, and any local LLM inference) and any API costs for external LLMs (e.g., OpenAI, Anthropic).
  • Dify Cloud:
    • Offers a managed SaaS solution with various subscription tiers.
    • Sandbox (Free Trial): Typically includes a limited number of messages (e.g., 200 OpenAI calls initially), a small number of apps and knowledge documents, and basic features to get started.
    • Professional Plan: Aimed at independent developers or small teams, offering more messages (e.g., 5,000/month), more apps, larger knowledge base storage, higher rate limits, and more team members. Example pricing: around $59 per workspace/month.
    • Team Plan: For medium-sized teams, with further increases in limits and capacity. Example pricing: around $159 per workspace/month.
    • Enterprise Plan: Custom solutions for larger organizations.
    • Pricing details (message counts, storage, features per tier) are subject to change. Always check the official Dify pricing page (https://dify.ai/pricing) for the latest information.

License

Dify's community edition is open-source, primarily licensed under an Apache 2.0-based license with some additional conditions outlined in their LICENSE file (often referred to as the Dify Open Source License or Dify Community Agreement). This generally allows for free use, modification, and distribution, but it's important to review the specific terms for any restrictions, particularly around commercial use or redistribution if offering Dify as a competing SaaS product.

Frequently Asked Questions (FAQ)

Q1: What is Dify? A1: Dify is an open-source LLM application development platform that provides a visual interface and backend tools to build, deploy, and manage AI-native applications, including RAG systems, AI agents, and chatbots.

Q2: Is Dify a no-code platform? A2: Dify is more accurately described as a low-code platform. While its visual interface allows for building many applications with minimal to no direct coding, it also offers capabilities for developers to write custom code, integrate complex logic, and extend its functionalities, making it powerful for both technical and less technical users.

Q3: How does Dify compare to LangChain or LlamaIndex? A3: LangChain and LlamaIndex are primarily code-first frameworks (libraries) for building LLM applications. Dify provides a more visual, integrated platform (LLMOps) that often uses concepts or can integrate with logic similar to what these frameworks offer but presents it through a UI-driven experience with BaaS features. Dify aims to simplify the end-to-end lifecycle from development to deployment and monitoring.

Q4: Can I self-host Dify? A4: Yes, Dify offers a community edition that can be self-hosted using Docker. This provides full control over your data and infrastructure.

Q5: What LLMs does Dify support? A5: Dify supports a wide range of LLMs, including those from OpenAI (GPT series), Anthropic (Claude series), Google (Gemini), open-source models (Llama, Mistral), and models accessible via platforms like Hugging Face, Ollama, Replicate, and Azure OpenAI. It also supports any OpenAI API-compatible models.

Q6: How does Dify handle knowledge bases for RAG? A6: Dify has a built-in RAG engine. You can upload documents (PDF, TXT, MD, etc.) or connect data sources. Dify processes this data (chunking, embedding) and stores it in a vector database to enable semantic retrieval for providing context to LLMs.

Q7: Is Dify free? A7: The self-hosted community edition of Dify is free to use (software cost). You'll be responsible for infrastructure and LLM API costs. Dify Cloud offers a free sandbox tier and paid subscription plans for its managed service.

Related Blog Articles & Tutorials

Community & Support

Ethical Considerations & Responsible AI

  • Data Privacy: When self-hosting, data remains within your infrastructure. For Dify Cloud, review their data handling and privacy policies (https://docs.dify.ai/en/getting-started/cloud mentions data stored on AWS US-East, secrets encrypted).
  • Responsible Agent Behavior: If building AI agents, ensure they are designed with safety and ethical considerations in mind, especially if they can take actions.
  • Bias in LLMs: Be aware that the underlying LLMs used can carry biases from their training data.
  • Content Accuracy: Applications built with Dify, especially those using RAG, rely on the quality of the knowledge base and the LLM's ability to interpret it. Generated content should be reviewed for accuracy.

Last updated: May 16, 2025

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