FlowiseAI

Drag-and-drop builder for custom LLM agents, RAG, and workflows

Some setup needed

builder workflow #ai-agent-builder#rag-workflows#visual-builder

About

Wire up LLM agents by dragging nodes instead of writing boilerplate. Developers and data teams use it to build document Q&A, summarization, and analysis backends that run on GPT-5.4, Claude 4.6, or Gemini 3.1 with tool use. Open source with token cost tracking, LangChain integration, real-time collaboration, and default security hardening.

Editor's Take

We recommend Flowise for engineers and data teams who want a low-code visual way to prototype RAG and agent backends across multiple models; best suited when teams prefer a visual canvas and built-in cost/ collaboration tools over writing provider boilerplate.

Key Features

Drag nodes to design a Q&A or RAG flow → deploy a working LLM backend without boilerplate
Select GPT-5.4, Claude 4.6, or Gemini 3.1 → run tool-using agents across providers in one canvas
Run a batch test → monitor usage with built-in token cost tracking
Invite a teammate to the canvas → co-edit in real time with version history
Connect LangChain components → reuse existing chains and tools inside your Flowise project

Use Cases

A product engineer building a document Q&A bot for a 500-page internal knowledge base
A data scientist prototyping a RAG support agent without wiring multiple provider SDKs by hand

Try It Like This

1
Build a document Q&A bot
Sign up or self-host Flowise → import your documents and add a RAG node to the canvas → wire a retriever, encoder, and LLM node, run a batch test, then deploy the backend to serve queries.
2
Prototype a multi-provider agent
Open a new canvas and drag in agent and tool nodes → select GPT-5.4 for generation and Claude 4.6 for a fallback, connect tool-use nodes for search or API calls → test interactions in the canvas and iterate until the agent hands off to tools correctly.
3
Integrate an existing LangChain flow
Install or point Flowise at your LangChain components → drop a LangChain node and map inputs/outputs to the visual flow → run a preview to confirm the chain works and then deploy as a unified backend.
4
Run cost-aware load tests
Create a realistic test dataset and hook it to the batch test node → run the batch test to simulate usage and watch the built-in token cost tracker → review cost metrics and refine prompt/temperature or model selections to control spend.
5
Collaborate on an internal agent
Invite teammates to the project canvas and enable real-time collaboration → co-edit nodes, use version history to compare changes, and assign reviewer comments → after consensus, deploy the updated backend to a staging environment for validation.

Pros & Cons

Pros

Visual drag-and-drop builder lets developers design RAG/agent flows without writing boilerplate code.
Supports multiple top-tier models (GPT-5.4, Claude 4.6, Gemini 3.1) so agents can run across providers in one canvas.
Built-in token cost tracking and batch testing make it easy to monitor usage and control spend during prototyping.

Cons

Flows can become slow or unwieldy at large scale, which may impact iteration speed on complex projects.
The visual approach can be limiting for very complex or highly custom workflows that require low-level coding.

Getting Started

1 Visit the FlowiseAI GitHub and deploy locally with Docker or on your server
2 Create a new flow, add an LLM node and a document loader, and configure a model
3 Run the flow on a sample PDF and get grounded answers from your documents