Flowise - self hosting

Summary - tl;dr

Flowise - is an open source solution aiming to provide a low-code visual orchestration environment built upon the LangChain.js framework. It allows building of AI Agents and complex multi-agent autonomous flows visually.

The main value proposition of this framework lies in the ability to accelerate the development lifecycle of AI-driven applications while maintaining the flexibility of open-source software.

At this time, common approaches to building LLM applications require significant knowledge of asynchronous JavaScript or Python-based frameworks like LangChain or LlamaIndex. Flowise aims to mitigate this by offering a visual workspace that serves as a functional equivalent to an integrated development environment (IDE) for AI agents.

Solution Architecture Summary

Flowise is designed as a modular full-stack application, organized within a monorepository structure which includes the visual interface, the backend execution engine, and third-party integrations.

Module Breakdown

System comprises four primary modules, each residing in its own package within the repository:

• The Server Module: This is the Node.js backend that serves the application's API logic. It is responsible for interpreting the graph-based JSON payloads generated by the UI and translating them into executable LangChain.js or LlamaIndex operations. The server manages credential encryption, session persistence, and interaction with the underlying database.
• The UI Module: Built using the React library, this module provides the visual canvas where users construct their "chatflows" and "agentflows". It provides a real-time, interactive environment for node connection and configuration, serving as the primary interface for both developers and non-technical users.
• The Components Module: This package contains the logic for all third-party integrations, referred to as "nodes" in the Flowise ecosystem. It acts as a wrapper for various LLM providers, vector databases, search tools, and memory systems.
• API Documentation Module: This module provides auto-generated Swagger-UI documentation, allowing developers to programmatically interact with their deployed flows via standardized RESTful endpoints.

Technical Stack

TypeScript first stack, with JavaScript present as well and making around 30% of the code base. Backend is powered by the Express.js framework. For dependency management project utilises pnpm package manager.

Data management in Flowise is categorized into two layers: configuration persistence and runtime state management. For the configuration of chatflows, credentials, and user data, Flowise supports four major relational database types: SQLite, PostgreSQL, MySQL and MariaDB.

Flowise integrates with vector databases for the "long-term memory" required in various RAG systems. Pinecone, Weaviate, Qdrant, and Redis are supported.

Product Tiers

The platform offers three architectural patterns to match different complexity levels:

• Assistant: Streamlined chat agents with file-based knowledge retrieval (RAG)
• Chatflow: Single-agent systems supporting advanced patterns like Graph RAG and Reranker
• Agentflow: Full-spectrum orchestration for multi-agent systems and complex workflows

Deployment Architecture

Adoption in your organization

• Skill Set Required: Extracting a full value from Flowise requires understanding of LLM concepts (prompting, context windows, embedding models) and system design patterns. Non technical team members will ideally need to enablement by technical mentors during the adoption.
• Maintenance Ownership: Open-source platforms require organizations to manage updates, security patches, and compatibility with evolving AI model APIs. Best practice is to establish a clear ownership between technology, IT ops and fuctional teams.
• Cost Structure: While the software is open source, operational costs include infrastructure hosting, AI model API usage (OpenAI, Anthropic), vector database storage, and personnel time for workflow maintenance.
• Governance Framework: Visual simplicity can lead to workflow proliferation without proper governance. Implement review processes for production deployments, establish naming conventions, and create reusable component libraries to prevent duplicate efforts across teams.