Flowise

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Flowise is an open-source visual platform for building AI agents and LLM workflows, with a free hosted plan and free self-hosting options.

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⓪ Overview

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✦ Features

⚖ Pros & Cons

◎ Use Cases

⚉ Who's it for

❓ FAQs

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ℹ️Tool Info

💳 PRICING

Freemium

•Free (Cloud): 2 flows, 100 predictions per month, 5MB storage, community support

•Fully free open-source self-hosted version available (Apache 2.0) with unlimited local usage

•Starter: Around $35 per month with unlimited flows and 10,000 predictions per month

•Pro: Around $65–$99 per month with higher prediction limits, more storage, multiple users and priority support

•Enterprise: Custom pricing for on-prem, SSO, advanced security and dedicated support

🌐 INDIA SUPPORT

Yes (100% accessible in India via standard web browsers or local Docker/npm installation.)

🕒 LAST UPDATED

26 Aug 2026

💡What is Flowise?

Flowise is an open-source visual node-based framework built for orchestrating LangChain pipelines, RAG applications, and autonomous AI agents. Designed for developers, AI builders, and tech-savvy creators, Flowise provides a drag-and-drop canvas for connecting vector stores, memory nodes, LLMs, and custom tools. Flowise offers 100% free open-source self-hosting via Docker and npm alongside a hosted cloud free tier, enabling users to prototype, test, and deploy customized AI chatbots, RAG document assistants, and multi-agent systems with complete data ownership.

⚡Flowise's key features

✓Visual node-based canvas drag-and-dropping LangChain components and AI models

✓100% free open-source self-hosting architecture installable locally via Docker and npm

✓Native RAG pipeline connecting Pinecone, Chroma, Qdrant, and local vector stores

✓Multi-agent framework supporting sequential, hierarchical, and tool-using AI agents

✓Multi-model support integrating OpenAI, Claude, Hugging Face, and local Ollama models

✓Instant API and widget deployment generating embeddable web chat widgets and REST endpoints

✓Built-in chat memory nodes preserving conversation context across user interactions

✓Document loader nodes parsing PDFs, CSVs, TXT files, and web pages for embeddings

✓Custom JS/Python tool nodes executing custom code snippets within AI agent flows

✓Clean visual execution canvas displaying node-by-node data flow and debugging output

🎯Use Cases

→Building private customer support chatbots that query custom company PDF manuals

→Creating autonomous AI agents that execute web searches and custom Python scripts

→Deploying embeddable AI chat widgets on websites with custom branding

→Building backend LLM processing pipelines exposed as REST APIs for mobile apps

→Running 100% private offline AI RAG applications using local open-source models

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⚖️Pros & Cons

✅ PROS

100% free open-source self-hosting provides complete privacy and zero vendor lock-in
Visual LangChain abstraction makes building complex RAG and agent graphs intuitive
Supports both local offline LLMs (via Ollama) and commercial cloud AI APIs
Instantly exports embeddable web chat widgets and REST API backend endpoints
Active open-source community on GitHub with rapid model updates and component nodes

❌ CONS

Self-hosting requires technical familiarity with Docker, npm, or local server setups
Cloud hosted free tier limits monthly predictions before requiring plan upgrades
Connecting custom vector databases and local embeddings requires basic backend setup

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