Flowise is an open source low-code AI Agent building platform that orchestrates LLM workflow through a drag-and-drop visual interface. Supports RAG, Agent, MCP tool link connection, and multi-model switching API release. Apache-2.0 open source license, GitHub 47k+ Stars.

Flowwise

Core parameters and statistics

Parameters	Official verifiable information
Product positioning	Open-source low-code AI agent builder
Development Languages	TypeScript / Node.js
Open Source License	Apache-2.0
GitHub Stars	47,000+
Core capabilities	Visual LLM orchestration RAG, Agent, MCP tool chain
Deployment method	Docker / npm / Cloud
Supported models	100+ models from OpenAI, Anthropic, Google, Ollama, etc.
Developer	FlowiseAI Community

A brief comment: Flowise is a low-code platform that "draws" the code logic of LangChain/LlamaIndex, allowing non-developers to build AI workflows.

User and market recognition

Gradually build user awareness in the field, and product capabilities are used by content creators and teams to improve work efficiency. Some industry users have incorporated it into their daily workflow. It is recommended to refer to the latest official disclosures for specific user scale and industry adoption rate data.

Cost advantage

C-side/Personal: Completely free and open source. Deploy locally or self-host using Docker with zero platform fees.

API/Developers: Open source and infinitely scalable. The model calling fee is paid directly by the user to the model provider. Supports custom API key injection.

Enterprise/privatization: Open source can be deployed privately, and the data does not leave the network. The enterprise version provides additional functions (authority management, monitoring SSO), and pricing needs to be confirmed by the business.

Main functions

• Visual LLM workflow orchestration: Build AI applications by dragging nodes, supporting conditional branching, looping, and parallel execution. Complex logic concatenation can be completed without writing code.
• RAG Knowledge Base Access: Supports multiple data sources such as document upload (PDF, TXT, Markdown), web page crawling, and database connection. Built-in vectorization processing and retrieval enhance the generation process.
• Agent and Tool Calling: Supports ReAct and Function Calling Agent modes. It has built-in search engines, calculator API calls and other tools, and you can also customize tool extensions.
• MCP protocol support: Supports mounting MCP Server as the Agent tool chain to realize interaction between the model and external systems (browser, database, file system).
• Multi-model switching: Switch between 100+ models such as OpenAI, Anthropic, Google Gemini, Ollama local models, etc. with one click. Supports model parameter tuning (Temperature, Top P, Max Tokens).
• API Publishing: Publish the orchestrated workflow as a REST API with one click. Support API Key authentication and access control.

Model and version evolution

Continuous iterative updates, the latest version introduces performance optimization and new features. Historical version information can be viewed on the official release page. There is no complete public version evolution timeline yet. It is recommended to pay attention to the official announcement to understand the rhythm of feature updates.

Technical advantages

Main type judgment: Agent/automation tool - low-code AI Agent construction platform.

Architecture Link:

User drag and drop interface → Flowise backend (Node.js) → LLM API / local model
                    ↓
             RAG/MCP Server/Custom Tools

The core differentiation lies in "reducing the cognitive load of visualization" - upgrading LLM application development from code-level abstraction to flowchart-level abstraction, and non-technical personnel can also participate in AI workflow design.

How to use

Entrance	Description
Official website	Visit flowiseai.com to experience the Cloud version
Docker	docker run -p 3000:3000 flowiseai/flowise
npm	npx flowise start
GitHub	Clone the warehouse and deploy it yourself

Product Pricing

The pricing model is subject to the official real-time page. Usually a freemium or subscription system is used, and basic functions can be used for free. Advanced functions or high-frequency use require paid subscriptions, and users are advised to evaluate the optimal solution based on actual usage.

Application scenarios

• Internal AI prototype development: Quickly verify the feasibility of RAG Q&A, document summarization, data extraction and other scenarios.
• Automated Customer Service Agent: Connect knowledge base retrieval, sentiment analysis, and work order creation processes to build an end-to-end customer service robot.
• Content production pipeline: arrange automated content workflow for copywriting generation, translation, illustration, and publishing.
• Data Extraction and Analysis: Extract structured data from unstructured documents and connect to databases and BI tools.
• Education and Training: As a teaching tool for LLM application development, helping students understand the principles of AI workflow.

Applicable people

• Product managers and business people: Validate AI application ideas without programming, reducing reliance on development resources.
• Full stack developer: Quickly build AI prototypes and reduce repetitive LLM integration coding work.
• AI Entrepreneurship Team: Accelerate MVP delivery and verify product direction before investing in in-depth development.
• Not suitable for the crowd: For real-time scenarios that require deeply customized model training, ultra-large-scale distributed inference, or millisecond-level latency requirements, Flowise's abstraction layer will introduce additional overhead.

Summary and Outlook

Flowise transforms LLM application development from a programming task to a visual orchestration task, significantly lowering the threshold for building AI application prototypes. The open source community is active and the plug-in ecosystem continues to be enriched.

Not suitable for boundaries: Interface management costs increase as the number of nodes increases in complex logic scenarios; the open source version lacks enterprise-level permission management; workflow execution efficiency is limited by the Node.js single-thread model.

Purchase Suggestion: Free trial deployment for individuals and teams from Docker or npm. Before purchasing, enterprises need to confirm the privacy policy and data residency requirements of the Cloud version, as well as whether the SSO and auditing functions of the Enterprise version meet compliance requirements.

Related tools: CrewAI, langchain

Version Info

• Flowise 2.2 ：Supports MCP protocol tool chain connection, multi-agent orchestration mode enhancement, and custom tool chain expansion. （~2026-06）
• Flowise 2.1 ：Agent mode is upgraded to support multi-Agent collaboration and tool invocation. （~2026-03）
• Flowise 1.0 ：The first official version, basic LLM orchestration and RAG capabilities. （~2025-01）