flowise-tutorial

layout	default
title	Flowise LLM Orchestration
nav_order	4
has_children	true
format_version	v2

Flowise LLM Orchestration: Deep Dive Tutorial

Project: Flowise — An open-source visual tool for building LLM workflows with a drag-and-drop interface.

Why This Track Matters

Flowise makes LLM orchestration visual and accessible — a drag-and-drop canvas for building production pipelines without boilerplate, with auto-generated APIs for every workflow you create.

This track focuses on:

• building LLM workflows visually with Flowise's node canvas
• developing custom nodes to extend Flowise with new integrations
• connecting LLM providers, vector stores, and tools in production pipelines
• deploying and monitoring Flowise workflows with Docker

What Is Flowise?

Flowise is an open-source visual workflow builder for LLM applications. It provides a drag-and-drop canvas for connecting AI models, data sources, and tools into production-ready pipelines — without writing boilerplate code.

Feature	Description
Visual Canvas	Drag-and-drop workflow builder with real-time preview
Node Library	100+ pre-built nodes for LLMs, tools, and data sources
Streaming	Real-time streaming responses with SSE
Multi-Model	OpenAI, Anthropic, Google, HuggingFace, Ollama
Custom Nodes	Extensible architecture for building custom integrations
API Export	Auto-generated REST APIs for every workflow

Mental Model

graph TB
    subgraph Frontend["React Frontend"]
        CANVAS[Workflow Canvas]
        NODES[Node Library]
        PREVIEW[Chat Preview]
    end

    subgraph Backend["Node.js Backend"]
        ENGINE[Execution Engine]
        STORE[Workflow Store]
        STREAM[SSE Streaming]
        API[REST API]
    end

    subgraph Integrations["Integrations"]
        LLM[LLM Providers]
        TOOLS[Tool Nodes]
        DB[Vector Stores]
    end

    Frontend --> Backend
    ENGINE --> Integrations

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Chapter Guide

Chapter	Topic	What You'll Learn
1. System Overview	Architecture	Monorepo structure, component relationships
2. Workflow Engine	Execution	Node graph execution, data flow, streaming
3. Node Development	Extensibility	Building custom nodes, input/output types
4. Advanced Integrations	Connections	LLM providers, vector stores, tools
5. Production Deployment	Operations	Docker, scaling, monitoring
6. Security and Governance	Security	Secret handling, policy controls, tool safety
7. Observability	Monitoring	Tracing, metrics, and failure analysis
8. Extension Ecosystem	Growth	Extension contracts, distribution, compatibility

Tech Stack

Component	Technology
Backend	Node.js, Express
Frontend	React, ReactFlow
Database	SQLite / PostgreSQL / MySQL
Streaming	Server-Sent Events
Deployment	Docker, npm

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Ready to begin? Start with Chapter 1: System Overview.

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Built with insights from the Flowise repository and community documentation.

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Navigation & Backlinks

• Start Here: Chapter 1: Flowise System Overview
• Back to Main Catalog
• Browse A-Z Tutorial Directory
• Search by Intent
• Explore Category Hubs

Full Chapter Map

1. Chapter 1: Flowise System Overview
2. Chapter 2: Workflow Engine
3. Chapter 3: Node Development
4. Chapter 4: Advanced Integrations
5. Chapter 5: Production Deployment
6. Chapter 6: Security and Governance
7. Chapter 7: Observability
8. Chapter 8: Extension Ecosystem

Current Snapshot (auto-updated)

• repository: FlowiseAI/Flowise
• stars: about 50.9k
• latest release: flowise@3.1.0 (published 2026-03-16)

What You Will Learn

• how Flowise's node graph execution engine processes data flow and streaming responses
• how to build custom nodes with typed inputs and outputs for new integrations
• how to connect LLM providers, vector stores, and external tools in visual workflows
• how to deploy Flowise with Docker and manage security, governance, and observability

Source References

• Flowise

Generated by AI Codebase Knowledge Builder