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Automation & Workflows🟡Low Code🏆Editor's Choice
D

Dify

Dify is an open-source platform for building AI applications that combines visual workflow design, model management, and knowledge base integration in one tool.

Starting atFree
Visit Dify →
💡

In Plain English

An open-source platform for building AI apps — combine AI models, knowledge bases, and tools through a visual interface.

OverviewFeaturesPricingGetting StartedUse CasesIntegrationsLimitationsFAQSecurityAlternatives

Overview

Dify is an open-source LLM application development platform that positions itself as a leading agentic workflow builder, combining Backend-as-a-Service (BaaS) capabilities with LLMOps tooling in a single deployable stack. Rather than forcing teams to assemble brittle pipelines from disparate libraries, Dify provides a unified canvas where developers and non-technical builders alike can design AI applications—chatbots, copilots, multi-step agents, RAG systems, and document workflows—through a visual node-based editor that compiles to production-ready APIs.

The platform's architecture revolves around four pillars. First, a visual Workflow Studio lets users drag and connect nodes for LLM calls, knowledge retrieval, conditional branching, code execution, HTTP requests, and tool invocation, making complex orchestrations inspectable and debuggable. Second, a model-agnostic gateway supports hundreds of proprietary and open-source models—OpenAI, Anthropic Claude, Google Gemini, Mistral, Llama, Qwen, DeepSeek, and locally hosted models via Ollama, vLLM, or Xinference—so teams can swap providers without rewriting application logic. Third, a built-in RAG engine handles document ingestion, chunking, embedding, vector storage, hybrid retrieval, and reranking, eliminating the need to glue together separate vector databases and parsing services. Fourth, an agent framework with native tool use, function calling, and an extensible plugin marketplace enables autonomous task execution against APIs, databases, and SaaS systems.

Dify is distributed under a permissive open-source license and can be self-hosted via Docker Compose or Kubernetes for full data sovereignty, or consumed as a managed cloud service for teams that prefer not to operate infrastructure. Observability features—prompt versioning, response logging, annotation queues, and usage analytics—are built in, giving operators visibility into token spend, latency, and answer quality across deployments. The platform exposes every application as a REST API and ships with embeddable web chat widgets, making it straightforward to plug Dify-built agents into existing products, internal tools, or customer-facing channels. With tens of thousands of GitHub stars and adoption across enterprises and indie developers, Dify has become one of the most widely used open alternatives to closed LLM application platforms, particularly for teams that need a complete, opinionated stack without committing to a single model vendor.

🦞

Using with OpenClaw

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Integrate Dify with OpenClaw through available APIs or create custom skills for specific workflows and automation tasks.

Use Case Example:

Extend OpenClaw's capabilities by connecting to Dify for specialized functionality and data processing.

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🎨

Vibe Coding Friendly?

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Difficulty:beginner
No-Code Friendly ✨

Standard web service with documented APIs suitable for vibe coding approaches.

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Editorial Review

Dify is the most feature-complete open-source LLM application platform, combining visual workflow building, RAG, agent capabilities, and observability. Impressive breadth for a self-hosted solution but can be complex to operate at scale.

Key Features

Visual Workflow Studio: drag-and-drop canvas with nodes for LLM calls, knowledge retrieval, conditional branching, code execution, HTTP requests, and tool use, with live debugging and step-level inspection+
Multi-Model Gateway: unified abstraction over hundreds of proprietary and open-source models including OpenAI, Anthropic, Gemini, Mistral, DeepSeek, Qwen, Llama, plus local runtimes (Ollama, vLLM, Xinference)+
Built-in RAG Engine: end-to-end document ingestion, automatic and manual chunking, multiple embedding models, hybrid (vector + keyword) retrieval, and reranking with attachable knowledge bases+
Agent Framework with Tool Use: native function calling, ReAct-style agents, and a plugin marketplace for connecting to external APIs, databases, code interpreters, and SaaS platforms+
Prompt IDE and Versioning: side-by-side prompt comparison, A/B testing, version history, and template management to iterate on prompts as first-class artifacts+
LLMOps Observability: full request and response logging, token usage analytics, latency tracking, user feedback capture, and annotation queues for continuous quality improvement+
Application API and Embeds: every Dify app is auto-exposed as a REST API with SDKs and embeddable web chat widgets, plus iframe and JS snippet integration for product surfaces+
Deployment Flexibility: managed cloud, single-node Docker Compose, and production-grade Kubernetes Helm charts for self-hosted environments with full data sovereignty+

Pricing Plans

Sandbox (Free)

$0

    Professional

    Starts around $59/month

      Team

      Starts around $159/month

        Enterprise

        Custom pricing

          Self-Hosted (Community)

          Free (open source)

            See Full Pricing →Free vs Paid →Is it worth it? →

            Ready to get started with Dify?

            View Pricing Options →

            Getting Started with Dify

            1. 1Define your first Dify use case and success metric.
            2. 2Connect a foundation model and configure credentials.
            3. 3Attach retrieval/tools and set guardrails for execution.
            4. 4Run evaluation datasets to benchmark quality and latency.
            5. 5Deploy with monitoring, alerts, and iterative improvement loops.
            Ready to start? Try Dify →

            Best Use Cases

            🎯

            Building internal knowledge-base chatbots that answer employee questions over company documentation, wikis, and policy PDFs

            ⚡

            Prototyping and shipping customer-facing support copilots with embedded chat widgets backed by RAG over product manuals and help center content

            🔧

            Designing multi-step agentic workflows that combine LLM reasoning with API calls, database lookups, and conditional branching for back-office automation

            🚀

            Standardizing LLMOps across an organization—centralizing prompts, model routing, logs, and evaluations so multiple teams share one governed platform

            💡

            Self-hosting AI applications in regulated environments (finance, healthcare, government) where data must remain within a private VPC or on-premise cluster

            🔄

            Replacing fragmented LangChain plus vector DB plus custom UI stacks with a single open-source platform that non-engineers can also operate

            Integration Ecosystem

            25 integrations

            Dify works with these platforms and services:

            🧠 LLM Providers
            OpenAIAnthropicGoogleCohereMistralOllama
            📊 Vector Databases
            PineconeWeaviateQdrantChromaMilvuspgvector
            ☁️ Cloud Platforms
            AWSGCPAzure
            💬 Communication
            SlackEmail
            🗄️ Databases
            PostgreSQLMySQL
            📈 Monitoring
            Langfuse
            💾 Storage
            S3
            ⚡ Code Execution
            Docker
            🔗 Other
            GitHubNotionZapier
            View full Integration Matrix →

            Limitations & What It Can't Do

            We believe in transparent reviews. Here's what Dify doesn't handle well:

            • ⚠Dify's visual builder, while powerful, can struggle to express the most complex stateful agent loops cleanly—teams pushing the frontier of multi-agent orchestration often hit ceilings that pure-code frameworks like LangGraph or AutoGen handle more naturally. Self-hosting demands real DevOps capability: running Postgres, Redis, a vector store, and the Dify services in production requires Kubernetes or similar expertise. The cloud tier's request and document quotas can become limiting for high-volume RAG workloads, and certain enterprise-grade features (SSO, granular RBAC, audit trails, dedicated support) are reserved for paid or enterprise plans. Finally, because Dify evolves rapidly, breaking changes between minor versions occasionally surface, so production deployments should pin versions and test upgrades carefully.

            Pros & Cons

            ✓ Pros

            • ✓Open-source under a permissive license with full self-hosting support via Docker and Kubernetes, giving teams complete control over data, models, and infrastructure
            • ✓Visual workflow builder dramatically lowers the barrier for non-engineers to design multi-step agents, RAG pipelines, and chatbots without writing orchestration code
            • ✓Model-agnostic gateway supports hundreds of providers including OpenAI, Anthropic, Gemini, Mistral, and local models via Ollama or vLLM, enabling provider switching without rewrites
            • ✓Integrated RAG engine handles ingestion, chunking, embedding, hybrid retrieval, and reranking out of the box, removing the need to stitch together a separate vector stack
            • ✓Built-in LLMOps features—prompt versioning, logging, annotation, and analytics—provide production observability that most open-source frameworks omit
            • ✓Extensible plugin and tool marketplace lets agents call external APIs, databases, and SaaS systems with minimal custom code

            ✗ Cons

            • ✗Self-hosted deployments can be resource-intensive and require Docker, Kubernetes, and database operational expertise to run reliably at scale
            • ✗Visual workflow abstraction can become unwieldy for very complex agent logic, where pure code (LangGraph, custom Python) offers finer control and better version diffing
            • ✗Cloud pricing tiers can escalate quickly for high-volume teams, pushing larger workloads toward self-hosting which adds operational overhead
            • ✗Documentation and community support, while active, occasionally lag behind rapid feature releases, leaving edge-case behavior under-documented
            • ✗Some advanced enterprise features such as SSO, fine-grained RBAC, and audit logs are gated behind paid or enterprise plans

            Frequently Asked Questions

            Is Dify free and open source?+

            Yes. Dify is released under an open-source license and can be self-hosted at no cost using Docker Compose or Kubernetes. The team also offers a managed cloud service with paid tiers for users who prefer not to manage infrastructure, plus enterprise plans with SSO, advanced RBAC, and SLA support.

            Which LLMs and model providers does Dify support?+

            Dify is model-agnostic and supports hundreds of providers including OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, DeepSeek, Qwen, and Llama. It also integrates with locally hosted runtimes such as Ollama, vLLM, LocalAI, and Xinference, allowing fully on-premise deployments.

            How does Dify compare to LangChain or LangGraph?+

            LangChain and LangGraph are code-first Python libraries for building LLM applications, while Dify is a complete platform that wraps similar capabilities behind a visual builder, hosted UI, RAG engine, and observability layer. Teams that want full programmatic control may prefer LangGraph; teams that want a deployable product with less boilerplate typically prefer Dify.

            Can Dify handle Retrieval-Augmented Generation (RAG)?+

            Yes. Dify includes a built-in knowledge base feature that ingests PDFs, Word documents, web pages, and structured data, then handles chunking, embedding, vector storage, hybrid search, and reranking. Knowledge bases can be attached to any chatbot, agent, or workflow without external infrastructure.

            Is Dify suitable for production deployments?+

            Yes. Dify exposes every application as a REST API, supports horizontal scaling on Kubernetes, and includes logging, prompt versioning, and analytics for production monitoring. Many companies run customer-facing chatbots and internal copilots on Dify, though teams with strict compliance needs typically choose self-hosted or enterprise tiers.

            🔒 Security & Compliance

            —
            SOC2
            Unknown
            —
            GDPR
            Unknown
            —
            HIPAA
            Unknown
            ✅
            SSO
            Yes
            ✅
            Self-Hosted
            Yes
            ✅
            On-Prem
            Yes
            ✅
            RBAC
            Yes
            ✅
            Audit Log
            Yes
            ✅
            API Key Auth
            Yes
            ✅
            Open Source
            Yes
            ✅
            Encryption at Rest
            Yes
            ✅
            Encryption in Transit
            Yes
            Data Retention: configurable
            📋 Privacy Policy →
            🦞

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            What's New in 2026

            Through late 2025 and into 2026, Dify has expanded its agent capabilities with deeper multi-agent orchestration, parallel branch execution in workflows, and an enlarged plugin marketplace covering more SaaS connectors and code-execution sandboxes. The platform has added support for the latest reasoning models from major providers (including Claude 4 family, GPT-5-class models, Gemini 2.x, and DeepSeek V3/R1), improved structured output and JSON-mode handling, and introduced richer evaluation and dataset tooling for systematic prompt and agent testing. RAG has been upgraded with stronger hybrid retrieval, parent-child chunking strategies, and broader file-format support. Deployment ergonomics have also improved with cleaner Helm charts and more granular role-based access control on Team and Enterprise tiers.

            Alternatives to Dify

            CrewAI

            AI Agent Builders

            Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.

            Microsoft AutoGen

            Multi-Agent Builders

            Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

            LangGraph

            AI Agent Builders

            Graph-based workflow orchestration framework for building reliable, production-ready AI agents with deterministic state machines, human-in-the-loop controls, and durable execution.

            Microsoft Semantic Kernel

            AI Agent Builders

            SDK for building AI agents with planners, memory, and connectors. - Enhanced AI-powered platform providing advanced capabilities for modern development and business workflows. Features comprehensive tooling, integrations, and scalable architecture designed for professional teams and enterprise environments.

            View All Alternatives & Detailed Comparison →

            User Reviews

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            Quick Info

            Category

            Automation & Workflows

            Website

            dify.ai
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