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About
Flowise is an open-source agentic AI development platform that empowers developers and teams to visually construct sophisticated AI systems — from simple chatbots to complex multi-agent orchestration pipelines. Using an intuitive drag-and-drop interface, users can assemble modular building blocks to create Agentflows (multi-agent workflows), Chatflows (single-agent assistants with tool calling and RAG), and more — all without deep ML expertise. Key capabilities include support for 100+ LLMs, embeddings, and vector databases, making it straightforward to integrate with virtually any AI stack. The platform also offers Human-in-the-Loop (HITL) review gates, full execution tracing, and observability integrations with Prometheus and OpenTelemetry for production-grade monitoring. Developers can extend and embed Flowise into their own applications via REST APIs, TypeScript and Python SDKs, and an embeddable chat widget. Flowise is built for scale: it supports horizontal scaling with message queues and workers, and can be deployed both on the cloud and fully on-premises — making it suitable for enterprise environments with strict data governance needs. A free hosted tier is available, alongside Starter and Pro plans for teams needing more capacity and collaboration features. Trusted by companies across healthcare, analytics, digital humans, and enterprise AI, Flowise is a go-to platform for teams that want to move from prototype to production quickly.
Key Features
- Visual Agentflow Builder: Design and orchestrate multi-agent systems with a drag-and-drop canvas, coordinating distributed agents across complex agentic workflows.
- RAG-Powered Chatflows: Build single-agent chatbots and assistants with knowledge retrieval (RAG) from multiple data sources and built-in tool-calling support.
- Human-in-the-Loop (HITL): Insert human review checkpoints into agent pipelines so teams can validate, approve, or correct agent actions before they complete.
- Full Observability & Tracing: Monitor AI pipelines with full execution traces and integrations with Prometheus and OpenTelemetry for production-level observability.
- API, SDK & Embed Support: Integrate Flowise into any application using REST APIs, TypeScript and Python SDKs, or an embeddable chat widget for seamless deployment.
Use Cases
- Building multi-agent AI systems that coordinate tasks across specialized sub-agents for enterprise automation workflows.
- Creating RAG-powered internal knowledge base chatbots that answer employee questions using company documents and data sources.
- Prototyping and deploying AI copilots embedded directly into SaaS products using Flowise's SDK and embeddable chat widget.
- Orchestrating AI pipelines with human-in-the-loop approval steps for sensitive or high-stakes business processes.
- Developing and iterating on LLM-powered customer support bots that integrate with CRMs, help desks, and other business tools.
Pros
- Truly Open Source & Self-Hostable: Flowise is MIT-licensed and can be fully self-hosted, giving teams complete data control and eliminating vendor lock-in.
- Rapid Prototyping to Production: The visual builder enables quick iteration while enterprise features like horizontal scaling, on-prem deployment, and observability make it production-ready.
- Broad AI Stack Compatibility: Supports 100+ LLMs, embeddings, and vector databases, making it compatible with virtually any modern AI toolchain.
- Developer-Friendly Extensibility: REST APIs, TypeScript and Python SDKs, and embeddable widgets make it straightforward to integrate Flowise into existing applications.
Cons
- Free Cloud Tier is Very Limited: The free hosted plan caps users at 2 flows and 100 predictions per month, which may not suffice for meaningful testing or demos.
- Self-Hosting Requires DevOps Knowledge: While open source, setting up and maintaining a self-hosted Flowise instance with scaling, queuing, and observability requires infrastructure expertise.
- Enterprise Features Behind Paid Plans: Advanced features like multiple workspaces, admin roles, permissions, and priority support are only available on paid cloud tiers.
Frequently Asked Questions
Is Flowise really free to use?
Yes — Flowise is open source and free to self-host with no limitations. The cloud-hosted version has a free tier limited to 2 flows and 100 predictions per month. Paid cloud plans (Starter at $35/month and Pro at $65/month) offer more capacity.
Do I need to know how to code to use Flowise?
Basic technical knowledge helps, especially for self-hosting or using the API/SDK. The visual builder is largely no-code, but building advanced agent pipelines benefits from familiarity with LLMs and prompt engineering.
What LLMs and AI services does Flowise support?
Flowise supports 100+ LLMs, embedding models, and vector databases, including OpenAI, Anthropic, HuggingFace, Ollama, and many others, along with major vector stores like Pinecone, Weaviate, and Chroma.
Can Flowise be deployed on-premises?
Yes. Flowise is designed for both cloud and on-premises deployment, making it suitable for organizations with strict data governance or compliance requirements.
How does Flowise handle scaling for production workloads?
Flowise supports horizontal scaling using message queues and worker processes, allowing teams to handle high volumes of predictions reliably in production environments.
