Flowise
FreemiumBuild AI Agents, Visually
An open-source, visual drag-and-drop platform for building and deploying AI agents, chatbots, and multi-agent workflows with 50+ LLM integrations and first-class RAG support.
What is it
Flowise is an open-source (Apache 2.0) visual development platform for agentic systems. It provides a modular node-based canvas where users construct AI applications ranging from simple no-code chat assistants to complex multi-agent orchestrations. Acquired by Workday, it is one of the most popular open-source AI agent frameworks with over 52,000 GitHub stars.
What it can do
Users build flows across three tiers — Assistant (no-code chatbots), Chatflow (single-agent systems with tool calling and RAG), and Agentflow (multi-agent orchestration with distributed coordination). The platform connects to 50+ LLM providers, vector databases, document loaders, and APIs. It supports HITL review loops, MCP protocol integration, full execution tracing with Prometheus and OpenTelemetry, and deployment via REST API, TypeScript/Python SDK, or an embedded chat widget.
Who is it for
It is built for developers who want to prototype and productionize AI agents without writing boilerplate orchestration code; DevOps teams that need self-hosted, air-gapped deployments for data privacy; and business operators who need no-code chatbots and workflow automation.
Key Features
Visual Drag-and-Drop Builder
Construct AI workflows on a node-based canvas using modular building blocks. Connect LLMs, memory, tools, vector stores, and logic nodes without writing orchestration code. Supports 100+ pre-built integrations out of the box.
Three-Tier Builder Model
Assistant for no-code chatbot creation, Chatflow for single-agent systems with tool calling and knowledge retrieval, and Agentflow for multi-agent orchestration where tasks are distributed across coordinated agents. Each tier scales from prototype to production.
50+ LLM Integrations
Connect to OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Groq, Cohere, Ollama, and dozens more. Switch models or combine them within the same flow without refactoring architecture.
First-Class RAG
Build retrieval-augmented generation pipelines with native document loaders, embedding models, vector stores, and rerankers. Ingest PDFs, text files, web pages, and databases to ground agent responses in private knowledge.
Use Cases
AI chatbots for customer support
Support teams build no-code chatbots with the Assistant tier, connect them to internal knowledge bases via RAG, and embed the widget on help centers to deflect tickets.
Document Q&A over private knowledge
Legal and research teams ingest contracts, manuals, and internal wikis into a Chatflow with vector retrieval, enabling employees to ask natural-language questions and receive cited answers.
Multi-agent workflow automation
Operations teams use Agentflow to coordinate specialized agents — one for data extraction, one for validation, one for reporting — to automate end-to-end business processes.
Rapid LLM prototyping for developers
Engineers test prompts, compare model outputs, and iterate on agent logic visually before committing to code. Flows export to APIs for seamless integration into existing backends.
Pricing plans
Frequently Asked Questions
Discussion
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