Dify is an open-source platform for building AI applications that combines visual workflow design, model management, and knowledge base integration in one tool.
An open-source platform for building AI apps — combine AI models, knowledge bases, and tools through a visual interface.
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.
Was this helpful?
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.
$0
Starts around $59/month
Starts around $159/month
Custom pricing
Free (open source)
Ready to get started with Dify?
View Pricing Options →Dify works with these platforms and services:
We believe in transparent reviews. Here's what Dify doesn't handle well:
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
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.
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.
Multi-Agent Builders
Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.
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.
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.
No reviews yet. Be the first to share your experience!
Get started with Dify and see if it's the right fit for your needs.
Get Started →* We may earn a commission at no cost to you
Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →A hands-on tutorial for building production AI apps with Dify — no coding required. Covers setup, three real use cases (customer support bot, document QA, content pipeline), pricing, and how it compares to LangChain and Flowise.
An honest comparison of the best no-code AI agent builders: n8n, Flowise, Dify, Langflow, Make, Zapier, and more. Features, pricing, agent capabilities, and recommendations by use case.
Learn to build AI agents with no-code tools like Lindy AI, low-code frameworks like CrewAI, or advanced systems with LangGraph. Real examples, cost breakdowns, and 30-day success plan included.
The 10 trends reshaping the AI agent tooling landscape in 2026 — from MCP adoption to memory-native architectures, voice agents, and the cost optimization wave. With real tools leading each trend and current market data.