Flowise

Flowise is an open-source visual builder and generative AI development platform for building AI agents and LLM orchestration applications. Users can chain models, vector stores, tools, and memory as modular nodes on a drag-and-drop interface. The resulting applications can be exposed as an API or embedded chat widget, aiming to simplify prototyping and internal tools where custom framework code is not required.

• Agentflow for building multi-agent systems with workflow orchestration distributed across multiple coordinated agents
• Chatflow for building single-agent systems and chatbots with support for tool calling and knowledge retrieval from data sources
• Human In the Loop capabilities allowing humans to review tasks performed by agents within the feedback loop
• Full execution traces with support for Prometheus, OpenTelemetry, and other observability tools
• Integration capabilities via APIs, embedded widgets, and TypeScript and Python SDKs
• Production scale deployment for on-premises and cloud environments using message queues and workers
• Support for over 100 LLMs, embeddings, and vector databases

Pros

• Provides a visual drag-and-drop interface that makes it fast to prototype and build generative AI applications without heavy coding
• Supports both self-hosted open-source deployments under the Apache 2.0 license and managed cloud plans
• Includes modular building blocks for both simple compositional workflows and complex autonomous multi-agent systems
• Offers developer flexibility with TypeScript and Python SDKs, APIs, and embeddable chat widgets

Cons

• The website text notes that the project is sunsetting, which buyers should check before adopting
• The free tier is limited to 2 flows and assistants, 100 predictions per month, and 5MB of storage
• The Starter plan caps usage at 10,000 predictions per month and 1GB of storage
• The Pro plan is limited to 50,000 predictions per month and 10GB of storage, with additional users costing 15 dollars per user per month

Flowise fits developers, small teams, and medium-sized businesses looking to rapidly prototype or deploy generative AI applications, chatbots, and multi-agent systems without writing extensive framework code. It is suitable for creating internal tools and customer-facing assistants that integrate with various LLMs and vector databases. It is a poor fit for organizations seeking a proprietary, fully-supported closed-source platform or teams that require unlimited prediction volumes without moving to higher-priced tiers or self-hosting.

Flowise offers a freemium model with a free tier and paid cloud plans. The Free tier costs 0 dollars per month and includes 2 flows and assistants, 100 predictions per month, 5MB of storage, evaluations and metrics, custom embedded chatbot branding, and community support. The Starter plan costs 35 dollars per month for individuals and small teams, offering unlimited flows and assistants, 10,000 predictions per month, 1GB of storage, and community support. The Pro plan costs 65 dollars per month for medium-sized businesses, including everything in Starter, 50,000 predictions per month, 10GB of storage, unlimited workspaces, 5 users included with additional users at 15 dollars per user per month, admin roles and permissions, and priority support.

Unlike traditional software development approaches that require writing manual framework code to connect large language models and vector stores, Flowise provides a visual drag-and-drop interface for node-based orchestration. Compared to purely code-centric libraries, it allows users to visually link agents, tools, and memory, and immediately expose the outcome through an API or embeddable widget. It bridges the gap between low-code prototyping and enterprise-ready, self-hosted or cloud-deployed agentic systems.

Flowise integrates with over 100 LLMs, embeddings, and vector databases. It supports TypeScript and Python SDKs, REST APIs, embedded chat widgets, Prometheus, OpenTelemetry, Notion, and Slack.

The facts state that Flowise is sunsetting, which is a critical factor for any buyer to evaluate before adopting the platform. While it provides an open-source visual interface and modular building blocks for AI agents, the public notice about sunsetting makes long-term deployment a significant risk. Teams should review the sunset status and usage limits carefully before deciding to build on the platform.