Dify

Free tier

Build, deploy, and manage autonomous AI agents and RAG pipelines — effortlessly at any scale.

Free tier available·All audiences·Powered by Multi-model (OpenAI, Anthropic, open-source, and more)·API available·Open source

Visit

Key strengths

Visual drag-and-drop agentic workflow builderNative RAG pipeline support with vector DB indexingMulti-LLM support — switch and compare any global modelMCP (Model Context Protocol) native integrationProduction-ready with enterprise-grade security and scalability

Free tier + paid plans

San Francisco, USA

Founded 2023

Self-hostable

Get alerts when Dify changes

Free. We'll email you when pricing, the free tier, API access or open-source status changes.

No ratings yet

Developer Documentation

Dify is fully open-source (GitHub) and offers a rich developer experience for building production-grade AI systems.

Self-hosting: Deploy via Docker Compose. The repo includes full setup instructions for local and cloud environments.
REST API: Every Dify application exposes a fully documented REST API, enabling programmatic interaction, workflow triggering, and integration into existing services.
MCP Integration: Dify supports native HTTP-based MCP services (protocol 2025-03-26) in both pre-authorized and auth-free modes — connect external APIs, databases, and tools via standardized MCP protocols.
MCP Server Publishing: Export any Dify workflow or agent as a universal MCP server, making it accessible to any MCP-compatible client.
RAG Pipeline: Ingest data from diverse sources, apply transformation and chunking strategies, and index into vector databases for semantic retrieval at inference time.
Plugin/Tool Ecosystem: Extend agents with plugins from the Dify Marketplace or build custom tools to expand capabilities.
LLM Provider Support: Switch between and benchmark OpenAI, Anthropic, Mistral, open-source models, and other global LLM providers via a unified interface.
Observability: Built-in logging, tracing, and performance monitoring to track agent behavior and LLM output quality in production.