Flowise - Build AI Agents Visually with Open Source Power

What is Flowise

Building AI applications has traditionally required extensive coding knowledge. If you've ever tried to work with LangChain, you know the learning curve can be steep—configuring chains, managing memory, handling embeddings, and orchestrating multiple agents often means diving deep into TypeScript or Python. For many teams, this technical barrier has slowed down the adoption of generative AI in production.

Flowise changes that equation. It's the first open-source visual AI Agent builder that lets you construct complex LLM workflows and multi-agent systems through a drag-and-drop interface—no code required. Think of it as a visual canvas where you can connect language models, data sources, and tools just like building a flowchart.

But don't let the visual interface fool you—Flowise isn't just for non-technical users. Under the hood, it maintains full power by being built on LangChain, giving developers the flexibility to inject custom code when needed. Whether you're a data scientist prototyping a RAG system, a product manager building a customer service bot, or an enterprise team deploying production AI applications, Flowise adapts to your skill level.

The platform has gained significant traction in the market. With over 50,4k Stars and 23,9k Forks on GitHub, contributions from 310+ developers, and deployments at companies like AWS, Priceline, Accenture, Deloitte, Publicis Groupe, and InsightSoftware, Flowise has proven itself as a trusted foundation for AI applications across financial services, healthcare, telecommunications, and retail.

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Core Capabilities That Set Flowise Apart

Flowise provides a comprehensive suite of tools for building AI applications, but what makes it truly powerful is how these capabilities work together seamlessly.

Visual Builder: Build AI Workflows by Drawing Them

The Visual Builder is the heart of Flowise—a canvas where you construct AI applications by connecting nodes. You can think of it as a visual programming environment specifically designed for LLM applications. Instead of writing code to define how data flows between components, you simply drag components onto the canvas and connect them with lines.

Flowise offers three distinct building modes depending on your needs. Assistant mode is perfect for beginners—completely no-code and ideal for quick prototypes. Chatflow mode lets you build single-agent systems with some customization options, perfect for customer service bots or knowledge base assistants. Agentflow mode handles complex multi-agent orchestration with branching, loops, and routing logic for sophisticated enterprise automation.

Multi-Agent Systems: Orchestrate Complex Workflows

Modern AI applications often require multiple specialized agents working together. Agentflow V2 enables you to coordinate multiple agents in a single workflow, implementing sophisticated patterns like sequential processing, parallel execution, conditional branching, and dynamic routing. This is particularly valuable for enterprise automation scenarios where different agents handle different aspects of a business process.

Chat Assistants: Conversational AI Made Simple

Building a chatbot is about more than just connecting an LLM—you need tool calling capabilities, knowledge retrieval, and conversation memory. Flowise handles all of this natively. You can create chatbots that can access external tools (like searching a database or calling an API), maintain context across conversations, and retrieve relevant information from your documents.

RAG: Connect Your Knowledge Base

Retrieval-Augmented Generation is where Flowise shines. The platform supports advanced RAG patterns including Graph RAG, reranking, and sophisticated retrieval strategies. You can connect to over 100 data sources—from PDFs and Notion pages to SQL databases and cloud storage—to build powerful document question-answering systems. This makes it ideal for enterprise knowledge management and internal search applications.

Human in the Loop: Keep Humans in the Decision Loop

Not every AI decision should be fully automated. Flowise's Human in the Loop feature lets you insert human approval checkpoints into AI workflows. This is essential for scenarios like expense approvals, content moderation, or any process where human judgment adds value before final execution.

Observability: See What's Happening

When AI applications run in production, you need visibility into how they're performing. Flowise provides comprehensive execution tracing and debugging tools. You can integrate with Prometheus and OpenTelemetry to feed logs into your existing monitoring infrastructure, making it easier to identify issues and optimize performance.

API, SDK & Embed: Integrate Anywhere

Flowise isn't just a standalone tool—it integrates into your existing applications. The platform offers a REST API, TypeScript and Python SDKs, and an Embedded Chat Widget that you can drop into any website. This means you can build AI capabilities into your products without rebuilding everything from scratch.

Enterprise Features: Built for Production

For organizations deploying AI at scale, Flowise provides robust enterprise capabilities including team and workspace management, role-based access control (RBAC), SSO integration, and horizontal scaling through message queues and workers. Whether you need cloud deployment or want to keep everything on-premises, Flowise supports both.

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Who Is Using Flowise

Flowise serves a diverse range of users—from individual developers learning AI to enterprise teams deploying mission-critical applications. Here's how different organizations are putting Flowise to work.

Enterprise Customer Service

Companies are using Flowise to build intelligent customer service agents that dramatically reduce support costs. A chatflow connected to your knowledge base can answer common questions instantly, 24/7. Organizations report reducing customer service costs by up to 80% while cutting response times from minutes to seconds. The chatbot can handle FAQs, troubleshooting guides, and policy questions without human intervention, escalating complex issues to human agents when needed.

Internal Knowledge Management

Information scattered across documents, wikis, and databases is a perennial challenge. Flowise enables teams to build document Q&A assistants that let employees find information instantly through natural language queries. Connect it to your Notion, SharePoint, Google Drive, or SQL databases, and employees can ask questions in plain English and get accurate answers pulled from across your organization's knowledge base.

Multi-Agent Workflow Automation

For complex business processes that span multiple systems, Agentflow's multi-agent capabilities shine. Different agents can handle different parts of a workflow—perhaps one agent processes incoming emails, another checks inventory, and a third generates responses. This orchestration reduces manual repetitive tasks and frees your team to focus on higher-value work.

Data Analysis Assistants

Business users often need data but lack SQL skills. Flowise lets you build SQL chatbots that accept natural language queries and return results from your databases. A product manager can ask "What were our top-selling products last quarter?" and get an instant answer—no data team dependency required.

Digital Human Experiences

Companies like UneeQ are using Flowise to power digital human experiences. By connecting Flowise to their Synapse AI engine, they create digital representatives that can have natural, intelligent conversations with customers. This represents a new frontier in customer engagement.

Healthcare AI Assistance

Medical professionals need quick access to information but can't afford to search through endless documents. Liverpool Hospital uses Flowise to build generative AI applications that help doctors retrieve relevant medical information quickly, supporting better patient outcomes.

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Getting Started with Flowise

One of Flowise's strengths is how quickly you can go from installation to your first working AI application.

System Requirements

Before installing, ensure your environment meets these minimum requirements: Node.js version 18.15.0 or higher, the PNPM package manager, and a database (Flowise supports MySQL, PostgreSQL, MariaDB, and SQLite for different scale needs).

Installation Options

For local development, you can install Flowise directly via npm or pnpm:

npm install -g flowise
npx flowise start

Or use the official Docker image for faster deployment:

docker run -d --name flowise -p 3000:3000 flowiseai/flowise

Your First Flow

Creating your first AI application takes just minutes. Here's the typical workflow:

1. Choose a building mode based on your needs (Assistant, Chatflow, or Agentflow)
2. Select your LLM provider—Flowise supports OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, and 100+ others
3. Add components like document loaders, text splitters, or vector stores for RAG applications
4. Connect the nodes by drawing connections between components
5. Test and iterate using the built-in chat interface
6. Deploy via API, embed in your app, or scale with Kubernetes

Deployment Flexibility

Flowise meets you where you are infrastructure-wise. For teams wanting zero infrastructure management, Flowise Cloud provides a fully managed solution. Organizations requiring full control can deploy on-premises using Docker or Kubernetes. You can also deploy on platforms like AWS, Azure, GCP, Digital Ocean, Railway, Render, or HuggingFace Spaces.

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Technical Architecture and Features

Flowise's architecture reflects its dual mission: making AI accessible while remaining powerful enough for enterprise workloads.

Modular Architecture

Flowise uses a modular design with three core layers: the Server (handles API requests and workflow execution), the UI (visual builder and administration), and the Components (reusable nodes for different AI capabilities). This separation means you can customize any layer without affecting others. The platform also supports the Model Context Protocol (MCP), enabling both client and server nodes for flexible integration patterns.

Extensive Model Support

Whether you prefer OpenAI's GPT models, Anthropic's Claude, Google's Gemini, or open-source options via AWS Bedrock and HuggingFace, Flowise has you covered. The platform integrates with over 100 LLM providers, multiple embedding models, and all major vector databases including Pinecone, Weaviate, Milvus, Chroma, and Qdrant.

Enterprise-Grade Observability

For production deployments, understanding how your AI applications behave is critical. Flowise provides detailed execution logs and a visual debugger that shows exactly how data flows through your workflows. Integration with Prometheus and OpenTelemetry means you can feed this data into your existing monitoring and observability stacks.

Security and Compliance

Security isn't an afterthought in Flowise. The platform includes role-based access control (RBAC) so you can define exactly what each team member can see and do. SSO integration works with your existing identity providers. Credentials are encrypted, and Flowise integrates with secret managers for sensitive data handling. Rate limiting protects against abuse, and domain restrictions prevent unauthorized embedding. For organizations with strict data requirements, Flowise supports full on-premises deployment including air-gapped environments.

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Flowise Pricing: Finding the Right Plan

Flowise offers three pricing tiers designed to match different stages of your AI journey—from personal exploration to enterprise deployment.

The Free plan is genuinely useful for learning and small projects—you get access to core features without any investment. It's perfect for evaluating whether Flowise fits your needs before committing financially.

The Starter plan removes the prediction limits that约束 growth, giving you unlimited flows while maintaining affordable pricing. The first month free trial lets you test production workloads without initial cost.

The Pro plan is designed for teams taking AI applications seriously. Beyond higher limits, you get proper team collaboration through workspaces, administrative controls for managing user permissions, and priority support when issues arise.

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Frequently Asked Questions