Flowise

Flowise is an open-source platform for building AI agents and chatbots through a visual drag-and-drop interface. Instead of writing boilerplate code to wire up language models, vector databases, and tool integrations, you assemble workflows on a canvas by connecting pre-built components. The result is a working AI system you can test immediately and deploy to production without switching tools. The project has gained significant traction in the open-source community, with adoption at companies like AWS, Accenture, Priceline, and Deloitte for production applications where control over infrastructure matters.

The problem it tackles is the gap between understanding what you want an AI agent to do and actually building one. Modern AI development involves stitching together a surprising number of pieces: prompt templates, embedding models, retrieval pipelines, tool calls, memory systems, and orchestration logic. Each component has its own API, configuration quirks, and failure modes. Flowise packages these into reusable nodes that handle the integration work, so you spend less time on plumbing and more time on the behavior that matters to your use case. For teams that have been building agents in code and found themselves repeating the same patterns across projects, the visual abstraction offers a faster iteration loop without sacrificing the ability to customize when needed.

There are two main workflow types. Chatflow handles single-agent setups with tool calling and retrieval-augmented generation, which covers most conversational AI needs like customer support bots, internal knowledge assistants, and document Q&A systems. Agentflow supports multi-agent systems where several agents coordinate across a shared workflow, useful when you need specialized agents to handle different parts of a complex task or when you want agents to collaborate on research, analysis, or decision-making. Both workflow types support human-in-the-loop review, letting you add approval steps before agents take consequential actions. That matters in domains where autonomous AI decisions carry risk, and you need a person to sign off before the agent proceeds.

The platform connects to over a hundred language models, embedding models, and vector databases. You are not locked into any particular vendor, so you can swap OpenAI for Anthropic, switch from Pinecone to Weaviate, or test different embedding models as your needs change. The integrations run deep enough for production use, with APIs, SDKs in TypeScript and Python, embeddable chat widgets for adding conversational interfaces to existing applications, and support for horizontal scaling via message queues and workers. Observability is built in through Prometheus and OpenTelemetry integrations, which matters once your agents are handling real traffic and you need to debug latency issues, track token usage, or understand where workflows fail.

Flowise works for individual developers experimenting with AI, but the traction has come from larger organizations that need control over their AI infrastructure. The open-source core means you can self-host on your own servers, which satisfies compliance requirements that rule out sending sensitive data to third-party clouds. You can inspect the code, audit security, and modify behavior when the default components do not fit your needs. There is also a managed cloud option for teams that prefer not to run their own infrastructure, which handles scaling, updates, and reliability so you can focus on building agents rather than operating servers.

Pricing on the cloud side starts free with two flows, a hundred predictions per month, and five megabytes of storage. That is enough to experiment but not enough for production use. The Starter tier at thirty-five dollars per month unlocks unlimited flows, ten thousand predictions per month, and more storage. Pro at sixty-five dollars per month raises the prediction cap to fifty thousand, adds multi-user support for team collaboration, and provides ten gigabytes of storage. Self-hosting is free since the code is open source, though you bear the infrastructure cost yourself and need the technical capability to run and maintain the deployment.

The platform fits teams that want to move from prototype to production without rewriting everything in custom code. If you have experimented with language model APIs and found yourself rebuilding the same retrieval and orchestration patterns over and over, Flowise consolidates that into a visual layer that is easier to iterate on. The open-source model means you can inspect how it works, extend it when needed, contribute improvements back to the community, and avoid the lock-in that comes with proprietary agent builders. For organizations where data sovereignty, auditability, or compliance requirements matter, the self-hosted option makes Flowise viable in contexts where many competitors are ruled out by policy.