Dify

Visually build and operate LLM apps, agents, and RAG pipelines

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assistant workflow #llm-app-builder#rag-workflows#ai-agents

About

Design LLM apps in a visual canvas with built-in deployment and observability. Engineers and product teams use it to build agents, RAG pipelines, and multi-agent workflows, then run them in the cloud or self-hosted. Open-source with a visual agent builder plus optional code for flexibility.

Editor's Take

We recommend Dify if your team wants a visual, deployable LLM app workflow that can run in the cloud or on-prem. Best suited for engineers and product teams standardizing RAG, agents, and LLMOps where observability and repeatable deployments matter.

Key Features

Drag and connect workflow blocks → a runnable LLM app with visual orchestration
Add retrieval steps to a flow → a working RAG pipeline without custom scaffolding
Define multiple agents in one project → coordinated multi-agent workflows with observability
Toggle deployment target → run in Dify Cloud or self-host the same project
Run docker-compose up or pip install → a local instance ready for development in minutes

Use Cases

A product manager building an internal support assistant that answers from company knowledge via RAG
An ML engineer setting up multi-agent workflows to automate document processing across teams
A platform engineer standardizing LLMOps with monitoring and managed deployments for several apps

Try It Like This

1
Build an internal support assistant
Sign up or self-host and create a new project → drag and connect workflow blocks to define a user query path and add a retrieval step pointing at company docs → deploy to Dify Cloud or your server and test conversations that answer from your knowledge base.
2
Create a RAG pipeline for docs
Start a flow and add a retrieval block that indexes your PDF/knowledge store → connect an LLM block to generate answers using retrieved context → run and validate results, then adjust retriever settings without writing custom scaffolding.
3
Design multi-agent processing
Define multiple agents in one project canvas and connect them with message or task blocks → assign each agent a role (extract, summarize, classify) and wire inputs/outputs visually → deploy and watch agents coordinate via built-in observability to debug and optimize.
4
Local dev: run a full instance quickly
Clone the repo or pull the image, then run docker-compose up (or pip install) → open the visual canvas, build a simple flow, and iterate locally → once satisfied, toggle deployment target to push the same project to Dify Cloud or a self-hosted environment.
5
Standardize LLMOps across teams
Create templates of common workflows (support bot, ingestion pipeline, agent orchestration) in the visual builder → share and reuse those templates across projects, attaching monitoring/observability hooks → use cloud or self-hosted deployments to enforce consistent runtime and monitoring.

Pros & Cons

Pros

Visual canvas converts drag-and-drop blocks into a runnable LLM app with orchestration, reducing boilerplate.
Same project can target Dify Cloud or self-hosted runtimes, making deployments portable.
Local development is quick: run docker-compose up or pip install to get a working instance in minutes.

Cons

Full platform has a learning curve—expect some setup and exploration to use advanced features effectively.
Managed Cloud tiers are paid (sandbox free, Professional $59/mo, Team $159/mo), which may matter for larger teams.

Getting Started

1 Sign up for Dify Cloud or install locally via docker-compose up.
2 Create a new project and use the visual agent builder to sketch your workflow or RAG pipeline.
3 Deploy to the Sandbox and run a test conversation to see end‑to‑end outputs in minutes.