Flowise is an open-source, low-code platform for building AI agents, chatbots, and LLM workflows through a visual drag-and-drop interface. Built on top of LangChain, it allows developers and non-developers to compose agents, RAG pipelines, and multi-agent systems by connecting modular nodes on a visual canvas. Flowise significantly lowers the barrier to creating sophisticated AI workflows.

• Website: flowiseai.com
• GitHub: github.com/FlowiseAI/Flowise¹⁾
• License: Apache 2.0

The Node Editor is the primary interface where users drag and drop pre-built components from a Component Library onto a canvas. Components include:

• LLMs: GPT-4o, Claude, Gemini, Llama, Mistral, and other models
• Vector Databases: Pinecone, Weaviate, Chroma, Qdrant, Milvus
• Tools: Web search APIs, CRM integrations, database connectors, email services
• Memory Systems: Short-term conversation memory, long-term vector storage, hybrid options
• Document Loaders: PDF, CSV, web scrapers, API connectors

Users configure inputs (chat, API calls, file uploads), processing (LLM chains and tools), and outputs (JSON, text, API payloads) with real-time testing, tracing, and debugging.

Chatflow: Conversational pipelines with memory management, token tracking, caching, and guardrails. Supports short-term memory for context, long-term vector storage for knowledge, and human-in-the-loop approval steps.

Agentflow: Multi-agent systems with supervisor-worker patterns. Supports native agent-to-agent communication, conflict resolution, dynamic role assignment, and complex orchestration patterns.

Assistant: Rapid prototyping mode for quickly creating AI assistants with minimal configuration.

• Performance: Handles 1,000 concurrent connections with 150ms overhead and 200-300MB memory footprint
• Deployment: Local, Docker, Kubernetes, or serverless; production-ready API endpoints with authentication and rate limiting
• Embeddable Widgets: Chat widgets for embedding in websites; API/SDK access for programmatic control
• Version Control: Git-style versioning for flows
• Real-time Collaboration: Multiple users can work on flows simultaneously
• Cost Optimization: 40-60% LLM cost savings via intelligent caching, deduplication, and model switching
• Guardrails: Content filtering, hallucination detection, and human-in-the-loop checkpoints
• Self-hosting: As low as $6-8/month vs. $500-5,000 for hosted alternatives

Flowise connects with numerous external services via API endpoints and pre-built nodes:

• LLM providers (OpenAI, Anthropic, Google, local models via Ollama)
• Vector databases and document stores
• Third-party APIs (CRM, ticketing, analytics platforms)
• Platforms like Bubble.io via REST API
• Proxy support for hiding API keys from client applications

• Customer support chatbots with RAG-powered knowledge bases
• Research agents with multi-step web search and analysis
• Internal copilots for enterprise documentation
• Data analysis pipelines with database integration
• Embedded AI experiences in existing web applications

• LangChain
• Retrieval-Augmented Generation
• Tool Integration Patterns
• Multi-Agent Systems

• Langflow
• ChatDev
• LangGraph
• GitHub Copilot vs Windsurf
• Streamlit for AI