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AI Agent & FrameworkOpen Source / Cloud Freemium
4.5(4,200)

Dify

Dify is an open-source LLM application development platform that bridges the gap between no-code simplicity and developer flexibility. It features a visual workflow orchestrator for building complex AI pipelines, a built-in RAG (Retrieval-Augmented Generation) engine for knowledge-based applications, and a comprehensive agent framework with tool integration. Dify supports multiple LLM backends (OpenAI, Claude, Llama, Qwen, and more), offers API publishing for embedding AI capabilities into existing products, and provides enterprise features including user management, logging, and analytics. It has gained massive adoption in both Chinese and international markets, particularly for building customer service bots, knowledge assistants, and internal productivity tools.

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Pricing

Open Source / Cloud Freemium

Rating

4.5 / 5

Reviews

4,200

Category

AI Agent & Framework

Performance Score

4.6

Features

4.3

Reviews

4.7

Momentum

4.4

Popularity

Pros

  • Visual workflow orchestration with drag-and-drop builder
  • Built-in RAG engine with multiple retrieval strategies
  • Multi-model support (OpenAI, Claude, Llama, Qwen, etc.)
  • Open source with self-hosting option and cloud service
  • Excellent Chinese and English documentation
  • Enterprise-grade features (RBAC, logging, analytics)

Cons

  • Advanced features require self-hosting technical expertise
  • Cloud version has usage limits on free tier
  • Workflow builder can feel complex for very simple use cases
  • Community plugins vary in quality and maintenance

Pricing

Self-hosted version free (Apache 2.0 License). Cloud: Free (200 messages), Pro at $59/month, Team at $159/month, Enterprise at custom pricing.

Features

Visual AI workflow builder
Built-in RAG engine and knowledge base management
Multi-LLM backend support
Agent framework with tool calling
API publishing for application embedding
Conversation logging and analytics
Role-based access control
Plugin and extension ecosystem

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Use Case

Best for developers and teams who want to build production-ready AI applications with a balance of visual development ease and code-level customization, especially for knowledge-intensive use cases.

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