Flowise

Description

Flowise is FlowiseAI's open-source (Apache-2.0) visual builder for LLM applications. Its GitHub tagline is 'Build AI Agents, Visually.' The documentation distinguishes three product surfaces: Chatflow ('designed to build single-agent systems, chatbots and simple LLM flows'), Assistant ('the most beginner-friendly way of creating an AI Agent'), and Agentflow, which is 'the superset of Chatflow & Assistant. It can be used to create chat assistant, single-agent system, multi-agent systems, and complex workflow orchestration.' AgentFlow V2 'represents a significant architectural evolution, introducing a new paradigm in Flowise that focuses on explicit workflow orchestration,' adding typed Agent nodes, Document Store retrieval, conditional and LLM-based router nodes, and an explicit Supervisor/Worker multi-agent shape in V1.

Solution

A Flowise project is one of three flow types. A Chatflow is a single-agent LangChain-style chain. An Assistant follows instructions, uses tools and retrieves from uploaded files. An Agentflow (V2) is an explicit workflow graph of typed nodes — Agent (an LLM with tools / Document Stores), Condition (deterministic branch), LLM Router (semantic branch via 'Scenarios' and natural-language 'Instructions'), Tool, and Retrieval. AgentFlow V1's Multi-Agent shape composes a Supervisor agent that 'analyzes user requests, decomposes them into sub-tasks, and assigns these to specialized worker agents' over connected Workers, sequenced one task at a time. Document Stores centralise ingestion, splitting and vector-store upsert.

Primary use cases

• no-code/low-code building of chatbots and single-agent RAG apps
• multi-agent supervisor/worker systems on a visual canvas
• RAG via centralised Document Stores with multiple vector store backends
• LLM-driven conditional routing inside an AgentFlow
• self-hosted on-prem deployment