Drag-and-drop builder for LangChain style LLM flows and agents
Flowise is an open-source tool that puts a visual canvas over the components commonly used to build LLM applications: prompt templates, model nodes, retrievers, vector stores, tools and agents. You drag them onto a canvas, connect them, and get a working chatflow or agentflow without writing the glue code. It runs locally or in Docker, supports many model providers, and exposes each flow through an API or an embeddable chat widget. For teams already thinking in LangChain terms, it is the quickest way to see a pipeline working end to end.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
prototyping rag apps, internal chat assistants, learning llm pipelines and demos
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It is code-optional. Most flows are assembled on the canvas, but understanding what the underlying components do - retrievers, embeddings, chains - is what separates a working flow from a confusing one.
Yes. Each flow can be exposed as an API endpoint or embedded as a chat widget, which makes it practical for internal tools and quick customer-facing prototypes.
For internal or moderate-traffic use, often yes. High-traffic deployments need attention to scaling, secrets, monitoring and error handling that the canvas does not provide on its own.
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