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Dify

What for: an open-source platform for LLM apps and agents — RAG, workflows, multi-agents and MCP on a visual canvas, with self-hosting.

open-source бесплатно локально API профи

★131k GitHub — один из самых быстрорастущих open-source LLM-фреймворков checked 2026-06-01

Updated: 02.07.2026

$ git clone https://github.com/langgenius/dify && cd dify/docker cp .env.exampl…

Open source ↗

Dify

What it is and who it's for

Dify is an open-source platform for building LLM apps and agents: a visual pipeline builder, RAG (questions over your own documents), multi-agent orchestration and MCP support — all with the option to self-host. For developers and teams who want to spin up a chatbot/assistant over their knowledge base fast and take it to production without locking into a single cloud. What sets it apart: a mature "chat-first RAG" out of the box and a huge community (★131k on GitHub).

Key features

  • Visual workflow and agent builder (drag-and-drop).
  • RAG pipeline: document upload, indexing, questions over your own base.
  • Multi-agent orchestration and tools; MCP support.
  • Connects to many models (cloud and local).
  • Built-in prompt IDE, logging, response evaluation.
  • Self-host via Docker or use the cloud; an API for embedding.

Getting started in 5 minutes

  1. Spin it up locally with Docker Compose (or sign up for Dify cloud):
git clone https://github.com/langgenius/dify && cd dify/docker
cp .env.example .env && docker compose up -d
  1. Open the panel and connect a model API key (OpenAI/Anthropic/local).
  2. Create an app, upload documents for RAG and test the chat; publish it as an API/widget.

When to take it and when not to

  • ✅ Take it if: you need a self-hosted RAG assistant/agent over your own knowledge base with control over your data.
  • ✅ Take it if: you value open source, visual assembly and MCP support.
  • ❌ Skip it → go with "n8n"-style automation if AI is only one step in a larger ops process.
  • ❌ Skip it → go with "Flowise" if what you want is LangChain-style prototyping on a canvas.

The honest price

Dify itself is free (open source, self-hosted); you pay for model tokens and your own infrastructure. There's a Dify cloud plan with limits/subscription. Exact amounts are on the provider's side and change.

Gotchas

  • Self-hosting needs DevOps hands: updates, backups, scaling — all on you.
  • RAG quality depends on how you clean data and tune chunks/retriever.
  • Model tokens still cost money — only the platform itself is "free".
  • Fast releases: keep an eye on migrations between versions.

🤖 Prompt accelerator

"I'm building a RAG assistant in Dify over my knowledge base <describe: documents/content type> for <audience/task>. Help me design the pipeline: how to prepare and chunk the documents, which retriever/embeddings to pick, the assistant's system prompt and the rules that keep it answering only from sources with citations. Give me a checklist for verifying answer quality before publishing."

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