Flowise

What Is Flowise and What Can You Build With It?

Flowise is a visual builder for LLM applications — you drag and connect nodes representing AI components: LLM providers (OpenAI, Claude, Ollama), document loaders, vector databases (Pinecone, Qdrant), memory modules, and output parsers. Common use cases: internal document chatbots (RAG over company docs), multi-step AI agents, customer support bots, and prototyping LangChain pipelines without writing Python. The resulting flow deploys as an API endpoint or embeddable chat widget in one click.

Also see: LangChain · n8n

How to Self-Host Flowise

1. Docker (quickest): docker run -d -p 3000:3000 flowiseai/flowise
2. Open http://localhost:3000 in your browser
3. Add an API key under Settings → API Keys for protected access
4. Build flows by dragging nodes from the left panel
5. For persistent storage: mount a Docker volume (-v flowise_data:/root/.flowise)
6. Click 'Deploy as API' to get an embeddable chat widget or REST endpoint

Also see: LlamaIndex · Pinecone

Flowise vs n8n: Which Should You Use for AI Pipelines?

Flowise is purpose-built for LLM applications — its nodes map directly to LangChain primitives, making it the fastest path from idea to a working RAG chatbot or agent. n8n is a general-purpose automation platform with AI nodes added as a capability. For AI-first use cases (document chatbots, RAG pipelines, agent workflows): Flowise. For general automation workflows that include some AI steps alongside other integrations: n8n. Many teams run both: Flowise handles the AI logic, n8n triggers it via HTTP request as part of a broader workflow.

Also see: n8n · LangChain