FlowiseAI
Drag-and-drop builder for custom LLM agents, RAG, and workflows
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.
Pros and cons
What works
- 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.
What to watch
- 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.
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.
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- 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.
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
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
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.
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FAQ
Does FlowiseAI support Korean?
Korean is not currently supported.