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Drag-and-drop open-source UI for building customized LLM orchestration and multi-agent workflows.

Flowise AI is a leading low-code platform designed for developers and solution architects to visually orchestrate LLM applications. Built on top of the LangChain framework, it allows users to construct complex logic chains—from simple retrieval-augmented generation (RAG) to sophisticated multi-agent systems—using a drag-and-drop interface. In the 2026 market, Flowise distinguishes itself by bridging the gap between raw Python/TypeScript coding and high-level automation tools. It supports a massive library of over 100 components, including diverse vector stores (Pinecone, Weaviate, Milvus), multiple LLM providers (OpenAI, Anthropic, Ollama), and memory buffers. Its technical architecture focuses on flexibility, allowing users to export flows as API endpoints or embed them directly into web applications using their React and JS widgets. For enterprises, Flowise offers a pathway to bypass vendor lock-in by supporting local LLM deployments and self-hosted infrastructure, ensuring data privacy and compliance. Its shift toward 'Flowise Cloud' in 2025-2026 provides a managed environment for teams who require scalability without the overhead of server management, while maintaining the core open-source ethos that made it a developer favorite.
Flowise AI is a leading low-code platform designed for developers and solution architects to visually orchestrate LLM applications.
Explore all tools that specialize in rag implementation. This domain focus ensures Flowise AI delivers optimized results for this specific requirement.
Enables the creation of specialized agents that can hand off tasks to one another using supervisor nodes.
Programmatic endpoints to inject and vectorize new documents into connected vector stores in real-time.
Allows users to write custom JavaScript functions that LLMs can call as 'tools'.
Combines vector similarity with keyword-based BM25 search for higher retrieval accuracy.
Server-Sent Events (SSE) support for real-time token streaming in chat interfaces.
Encrypted storage for API keys and environment variables used across different flows.
Ability to nest complex logic flows inside a single node within another flow.
Install Node.js (v18 or higher) on your local machine or server.
Execute 'npm install -g flowise' via terminal for global installation.
Start the application by running 'npx flowise start' to launch the local UI.
Navigate to http://localhost:3000 to access the visual canvas.
Configure credentials for providers like OpenAI or Pinecone in the 'Credentials' tab.
Drag a 'Chat Model' node and a 'Chain' node onto the canvas.
Connect an 'Embeddings' node and 'Vector Store' node for RAG capabilities.
Test the flow in the built-in chat interface to verify logic.
Save the flow and obtain the API Endpoint URL from the 'API Configuration' section.
Deploy the flow to production via Docker or Flowise Cloud for external access.
All Set
Ready to go
Verified feedback from other users.
"Users praise the rapid prototyping speed and the vast array of integrations. Some note a learning curve regarding LangChain concepts, but appreciate the visual clarity."
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