Flowise
What is Flowise?
Flowise is an open-source AI workflow builder for product teams, developers, and enterprise teams that assemble agents and LLM pipelines visually. It centers on a drag-and-drop canvas for no-code and low-code workflows, including RAG, SQL chatbots, and AWS Bedrock apps. Flowise is self-hostable, backed by 52,874 GitHub stars, and used by AWS, Priceline, Accenture, Deloitte, Publicis, QMIC, GlobeTelecom, InsightSoftware, and SynthesisHealth.
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At a glance
- Flowise is best for builders who want to assemble AI agents and workflows without coding every step.
What does Flowise do?
Flowise handles agent and workflow assembly by letting teams connect LLM components visually, then run them as open-source agentic systems. The product centers on a drag-and-drop builder, with no-code and low-code workflows for tasks like RAG, SQL chatbots, and AWS Bedrock apps. That makes it easier to prototype, test, and ship AI flows without wiring everything by hand. At scale, Flowise is backed by 52,874 GitHub stars and is trusted by teams such as AWS, Priceline, Accenture, Deloitte, and Publicis. It is self-hostable, so teams can keep the runtime on their own infrastructure when needed. The ecosystem also includes docs, webinars, and a cloud sign-in path for getting started quickly, while the open-source core keeps the platform flexible for builders who want more control than a closed visual tool.
Why use Flowise?
- Webinars and docs around RAG, SQL chatbots, and AWS Bedrock show a practical path from demo to production use.
- Adoption by AWS, Accenture, Deloitte, and Priceline signals that the platform fits both experimentation and enterprise use.
Who is Flowise for?
- Product teams who need to prototype AI workflows quickly without heavy engineering overhead.
- Developers who want a visual layer for assembling LLM pipelines and agent logic.
- Enterprise teams who need self-hostable AI workflow infrastructure with more control.
- Solution architects who are building RAG, chatbot, or Bedrock-based applications.
- Technical operators who want reusable no-code components for internal AI systems.
What are Flowise's key features?
Visually
Design AI agents in a visual canvas, then connect components without code. This helps teams prototype and update workflows faster, with 52,874 GitHub stars backing the approach.
What are Flowise's use cases?
Prototype AI workflows fast
Product teams who need to prototype AI workflows quickly without heavy engineering overhead use Flowise to map out agent flows visually and test ideas before committing to code. The Visually interface helps them turn rough concepts into working chatbot or RAG prototypes faster, so they can validate user value earlier.
Visual pipeline building for developers
Developers who want a visual layer for assembling LLM pipelines and agent logic use Flowise to stitch components together without starting from scratch. With Visually, they can iterate on prompts, branching logic, and integrations in one place, then move from experiment to deployable workflow with less rework.
Self-hosted AI infrastructure control
Enterprise teams who need self-hostable AI workflow infrastructure with more control use Flowise to keep AI systems closer to their own environment. The Visually builder gives technical operators a reusable way to assemble internal workflows while maintaining the control and deployment flexibility enterprise projects require.
RAG and chatbot delivery
Solution architects building RAG, chatbot, or Bedrock-based applications use Flowise to design the workflow visually and align the pieces before implementation. Visually helps them standardize how retrieval, generation, and agent steps fit together, reducing handoff friction and speeding delivery.
How does Flowise work?
- Connect your first data source or model endpoint, then open the Visually canvas to start assembling the workflow from the building blocks Flowise provides.
- Drag in the components you need for prompts, retrieval, and agent logic, then wire them together to define how data moves through the pipeline.
- Tune each node's settings directly in the visual editor, adjusting inputs, outputs, and branching behavior until the workflow matches your use case.
- Run the flow and inspect the results, using the canvas to spot bottlenecks or weak prompts before you share it with your team.
- Reuse the finished workflow as a template for new internal systems, so technical operators can launch similar AI apps without rebuilding the same logic.
Frequently asked questions
What is Flowise?
Flowise is an open-source AI workflow builder for product teams, developers, and enterprise teams that assemble agents and LLM pipelines visually. It centers on a drag-and-drop canvas for no-code and low-code workflows, including RAG, SQL chatbots, and AWS Bedrock apps. Flowise is self-hostable and is used by AWS, Priceline, Accenture, Deloitte, and Publicis.
What is Flowise used for? Who is it for?
Flowise is used for Visually. It's built for Product teams, Developers, and Enterprise teams.
Editor's read
Check whether self-hosting is a requirement before rollout, since Flowise is designed to run on your own infrastructure when needed. Also verify that your target use case fits the visual builder pattern for RAG, SQL chatbots, or AWS Bedrock apps.
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