AnythingLLM vs Dify vs Flowise
Three powerful platforms for building RAG applications and LLM workflows. Whether you want to chat with your documents, build an AI assistant, or create multi-step pipelines — here's how they compare.
AnythingLLM
All-in-one desktop + web app for chatting with documents using any LLM
Dify
LLM application development platform with workflows, RAG, and agents
Flowise
Visual drag-and-drop builder for LangChain-powered AI flows
Feature Comparison
| Feature | AnythingLLM | Dify | Flowise |
|---|---|---|---|
| Open source | |||
| Free to use | |||
| Desktop app | |||
| Docker/self-hosted | |||
| Works fully offline | Partial | ||
| Drag-and-drop builder | |||
| Document upload (RAG) | |||
| Multi-user workspaces | |||
| Agent support | |||
| Workflow automation | |||
| API for integrations | |||
| Vector DB support | 5+ | 10+ | 10+ |
| LLM backends | 15+ | 20+ | 15+ |
| Ollama support | |||
| Min RAM | 4 GB | 8 GB | 4 GB |
Deep Dives
AnythingLLM
AnythingLLM is designed for simplicity. Its desktop app (Windows, Mac, Linux) lets you create workspaces, upload documents (PDF, DOCX, TXT, CSV, YouTube links), and chat with them using any LLM backend — Ollama, OpenAI, Anthropic, or dozens of others. No coding required. The workspace concept keeps conversations and documents organized, with built-in user management for shared deployments.
AnythingLLM supports agents that can browse the web, generate charts, and run code. It has its own built-in vector store and integrates with ChromaDB, Qdrant, and others. The cloud version adds team collaboration features. Its biggest strength is accessibility — a non-technical user can be chatting with their PDF in under 5 minutes.
Pros
- ✓ Desktop app — no Docker needed
- ✓ Easiest setup of the three
- ✓ Great document chat UX
- ✓ Built-in agent capabilities
- ✓ Multi-user workspaces
Cons
- ✗ No visual workflow builder
- ✗ Less extensible than Dify/Flowise
- ✗ Fewer enterprise features
Dify
Dify is the most feature-rich of the three. It's a full LLM application development platform with a workflow builder, RAG pipeline configuration, agent orchestration, prompt management, and analytics. Dify's workflow editor lets you build complex multi-step pipelines visually — think If/Else branching, loops, HTTP calls, and multiple LLM nodes connected together.
Dify supports 20+ LLM providers, 10+ vector databases, and has robust API access for embedding chatbots into web apps. The annotation and QA features help improve model outputs over time. Dify is developer-oriented but has a polished UI. Self-hosting via Docker Compose is well-documented.
Pros
- ✓ Most complete feature set
- ✓ Advanced workflow builder
- ✓ 20+ LLM providers, 10+ vector DBs
- ✓ Analytics and annotation tools
- ✓ Embed chatbots in apps
- ✓ 130k GitHub stars
Cons
- ✗ Docker required for self-hosting
- ✗ More complex setup
- ✗ Steeper learning curve
- ✗ Partial offline support only
Flowise
Flowise takes a visual-first approach, wrapping LangChain in a drag-and-drop interface. You build "chatflows" and "agentflows" by connecting nodes: document loaders, text splitters, embeddings, vector stores, LLMs, and tools. The result is a visual representation of a LangChain pipeline that's much easier to debug and understand than Python code.
Flowise is lightweight (single npm package, starts in seconds) and has a large library of pre-built templates. The marketplace lets you share and discover flows. It supports 15+ LLM backends and all major vector databases. Flowise is the bridge between no-code accessibility and full LangChain power.
Pros
- ✓ Easiest to install (npm/Docker)
- ✓ Visual drag-and-drop flows
- ✓ Large template marketplace
- ✓ Full LangChain under the hood
- ✓ Lightweight resource usage
Cons
- ✗ Tied to LangChain abstractions
- ✗ Less suited for complex team workflows
- ✗ No desktop app
- ✗ Multi-user support limited
Choose Based on Your Needs
Upload files and start chatting in minutes. No Docker, no coding — the simplest document RAG experience.
Full application platform with workflows, agents, analytics, and embed capabilities for production-grade apps.
Visual drag-and-drop LangChain pipelines. Perfect for prototyping and understanding AI workflows visually.
Our Recommendation
Dify wins overall for its comprehensive feature set and massive adoption. AnythingLLM is the runner-up for non-developers who just want to chat with documents. Flowise is the specialist pick for visual pipeline builders who want LangChain power without writing Python.
Frequently Asked Questions
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) lets you ask an LLM questions about your own documents, databases, or knowledge base. The system retrieves relevant context before generating a response, dramatically improving accuracy.
Can I run all three offline?
AnythingLLM and Flowise run fully offline with local LLMs. Dify can be self-hosted but requires internet for cloud model APIs unless you configure Ollama as the backend.
Which supports the most vector databases?
Flowise and Dify support the most vector databases (Pinecone, Qdrant, ChromaDB, Weaviate, Milvus). AnythingLLM has its own built-in vector store plus integrations with major providers.
Is Dify harder to set up than the others?
Dify requires Docker Compose for self-hosting, making it slightly more complex than AnythingLLM's one-click installers. Flowise is the easiest to get running with a single npm command.
Which is best for non-developers?
AnythingLLM targets non-developers with its desktop app and simple document upload UI. Flowise's drag-and-drop flow builder is also approachable. Dify is more developer-oriented but has a clean web UI.