36.7K stars! Drag and drop to build AI processes, this open source LLM application framework is amazing!
Open source LLM application framework Flowise, the Lego building blocks of the AI era, drag and drop to build intelligent workflows and quickly implement AI application development.
Core content:
1. Introduction to the Flowise framework and GitHub high-star rating
2. Core functions: visual design, multi-model support, enterprise-level features
3. Technical architecture and quick start guide, typical application scenario examples
Just drag and drop nodes to build your own AI workflow in 5 minutes!
FlowiseIt is a revolutionary low-code LLM application building tool. Developers can quickly build intelligent workflows based on large language models through a visual drag-and-drop interface. The project has received 36.7K stars on GitHub in less than a year , and is praised by developers as "Lego blocks in the AI era."
// Typical application scenario code example
const flow = {
nodes : [
{ type : 'Document Loading' , params : { path : 'Annual Report.pdf' } },
{ type : 'text vectorization' , model : 'text-embedding-3-small' },
{ type : 'Question and answer chain' , prompt : "Summarize the key data in the document" }
],
connections : [
{ source : 'Document loading' , target : 'Text vectorization' },
{ source : 'text vectorization' , target : 'question-answer chain' }
]
}
Core Features
Visual process designer
Drag-and-drop node orchestration: built-in 50+ preset nodes, covering document processing, model calling, API docking, etc. Real-time debugging panel: Each node can be tested separately, and intermediate results preview is supported Version control: automatically save the historical version of the process and support one-click rollback
Multiple model support
Enterprise-grade features
RAG enhancement : support for parsing 20+ document formats including PDF/Word/web pages Permission management : fine-grained team collaboration permission control Monitoring dashboard : real-time statistics of token consumption and API calls
Technical Architecture
Quick Start
Local deployment (completed in 3 steps)
# 1. Install dependencies
npm install -g flowise
# 2. Start the service (with authentication)
npx flowise start --FLOWISE_USERNAME=admin --FLOWISE_PASSWORD=123456
# 3. Access interface
http://localhost:3000
Typical application scenarios
Intelligent customer service system : access to the enterprise knowledge base to automatically answer product questions Contract Review Assistant : parse legal documents and automatically generate risk reports Scientific research literature analysis : batch processing of papers and extraction of key research results Intelligent recruitment system : automatically parse resumes and generate candidate assessments
Product Advantages
Project Practice
Building an Intelligent Email Classifier
Drag in the "Mail Receiving" node to configure the IMAP protocol Connect the "Text Classification" node to select the classification model Add "Auto Reply" node to set different scenario templates Deploy as API to access corporate email system
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Summarize
Flowise redefines the way LLM applications are developed, leading the industry through three major innovations:
Zero-code visualization : Business experts can also directly participate in AI process design Ready to use : Pre-installed enterprise-level features to avoid reinventing the wheel Elastic expansion : Supports smooth expansion from single-machine deployment to K8s cluster
Whether it is a startup team quickly verifying AI ideas or a large enterprise building an intelligent middle platform, Flowise can provide best practice solutions.
Project interface
Project gallery
https://github.com/FlowiseAI/Flowise
