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Flowise is an open source agentic systems development platform that enables developers and teams to build AI agents and LLM workflows through a visual, drag-and-drop interface. It provides modular building blocks suitable for everything from simple compositional workflows to fully autonomous multi-agent systems, without requiring extensive coding.
The platform offers three primary visual builders: Agentflow for multi-agent orchestration with branching, looping, and routing logic; Chatflow for single-agent systems and chatbots with support for tool calling and advanced RAG techniques; and Assistant, the most beginner-friendly mode for creating chat assistants with knowledge retrieval from uploaded files.
Flowise includes a complete set of production capabilities, including full execution traces, support for Prometheus and OpenTelemetry observability tools, human-in-the-loop review, and enterprise-grade infrastructure for cloud and on-premises deployments. It connects to over 100 LLMs, embedding models, and vector databases, and supports horizontal scaling via message queues and workers.
Developers can extend and integrate Flowise into their own applications using a REST API, JavaScript and Python SDKs, and an embeddable chat widget. Security controls include RBAC, SSO, encrypted credentials, secret managers, rate limiting, and restricted domains. The platform also offers evaluations, datasets, MCP client and server nodes, and an affiliate program for community contributors.
- Build multi-agent systems with coordinated workflow orchestration across distributed agents
- Create chatbots with tool calling and retrieval-augmented generation from various data sources
- Allow human reviewers to inspect and approve agent tasks within the feedback loop
- Integrate AI capabilities into external applications using REST API, SDK, or embedded chat widget
- Deploy AI workflows on-premises or in cloud environments with enterprise-grade infrastructure
- Monitor agent execution with full traces and observability tools like Prometheus and OpenTelemetry
- Build no-code SQL chatbots that query databases through conversational interfaces
- Rapidly prototype and iterate on LLM applications using visual drag-and-drop building blocks
- Construct RAG pipelines with graph RAG, rerankers, and retriever configurations
- Scale AI workloads horizontally using message queue and worker architecture
