Flowise

FlowiseAI is an open-source, low-code platform designed for visually building and orchestrating workflows involving large language models (LLMs) and AI agents. Through a drag-and-drop interface, users connect modular components—such as document loaders, text processors, LLMs, logic operators, and tool interfaces—into dynamic pipelines tailored for natural language automation.

The platform supports both single-agent and multi-agent architectures. Users can design simple assistants using Chatflow or build complex, hierarchical systems in Agentflow mode. These workflows can include retrieval-augmented generation (RAG), memory mechanisms, branching logic, loops, human-in-the-loop steps, and external tool integrations.

FlowiseAI integrates natively with LangChain, LlamaIndex, and major vector databases like Pinecone and Milvus. It also supports API interactions, custom scripting in JavaScript or TypeScript, and embedding via SDK or command-line tools. Workflows can be deployed in enterprise environments or embedded directly into third-party applications.

Key developer features include live execution tracing, real-time debugging, Prometheus and OpenTelemetry observability, validation tools, reusable templates, and role-based collaboration. Users can run the platform via self-hosted Docker setups or opt for a cloud-hosted solution. The managed version supports scalable infrastructure with paid plans offering higher throughput, team features, audit logs, SSO, and air-gapped deployment options.

FlowiseAI is used for applications such as document-backed chatbots, multilingual content transformation, business workflow automation, and multi-agent coordination. It appeals to developers building production-grade LLM systems who value visual design, open-source flexibility, and modular AI tooling.

Keywords: open-source, low-code, LLM orchestration, AI agents, visual workflow, drag-and-drop, RAG, LangChain integration, multi-agent, execution tracing, human-in-the-loop, vector databases, SDK, self-hosted deployment