Overview
Flowise is an innovative open-source low-code/no-code platform designed to revolutionize how developers and non-technical users build customized Large Language Model (LLM) applications. Built with a drag-and-drop interface using LangchainJS, Flowise enables users to visually construct AI chatbots, conversational agents, and complex LLM workflows without writing extensive code.
At its core, Flowise provides a visual canvas where users can connect different AI components like language models, vector databases, embeddings, memory systems, and data loaders through an intuitive node-based interface. This approach democratizes AI development by making it accessible to business analysts, product managers, and developers who want to rapidly prototype and deploy LLM applications.
The platform supports multiple LLM providers including OpenAI GPT-4, GPT-3.5, Anthropic Claude, Google PaLM, HuggingFace models, and local models through Ollama. This flexibility allows organizations to choose the most suitable model based on their requirements, budget, and data privacy concerns. Users can seamlessly switch between different LLM providers within the same workflow, enabling A/B testing and fallback strategies.
Flowise excels in Retrieval Augmented Generation (RAG) implementations, allowing users to connect their own data sources such as PDFs, text files, websites, databases, and APIs to ground LLM responses in factual information. The platform includes built-in support for various vector databases like Pinecone, Weaviate, Milvus, Chroma, and Qdrant, making it easy to implement semantic search and long-term memory for chatbots.
One of Flowise's standout features is its agent capabilities, where LLM-powered agents can use tools to perform actions, make API calls, query databases, and execute custom code. This transforms static chatbots into intelligent assistants that can accomplish real-world tasks like booking appointments, searching documentation, or processing data.
For VPS hosting, Flowise offers significant advantages. Running Flowise on your own VPS ensures complete data privacy and control, as sensitive conversations and proprietary data never leave your infrastructure. Self-hosting eliminates per-conversation or per-token costs associated with hosted AI platforms, making it economical for high-volume applications. VPS deployment also enables customization of the entire stack, integration with internal systems, and compliance with data residency requirements.
The platform is built on Node.js and provides a RESTful API for programmatic access, making it easy to integrate Flowise flows into existing applications. The web-based UI allows multiple team members to collaborate on building and testing AI workflows, with version control and export/import capabilities for sharing templates.
Flowise is particularly valuable for organizations looking to experiment with generative AI without vendor lock-in. All configurations are stored as JSON, enabling easy migration between environments and backup of complex workflows. The active open-source community contributes new integrations, templates, and improvements regularly, ensuring the platform stays current with rapidly evolving AI technologies.
Key Features
Visual LLM Workflow Builder
Drag-and-drop interface to connect language models, vector databases, embeddings, and tools without coding. Build complex AI applications through an intuitive node-based canvas.
Multi-LLM Provider Support
Seamlessly integrate OpenAI (GPT-4, GPT-3.5), Anthropic Claude, Google PaLM, HuggingFace models, Azure OpenAI, Cohere, and local models via Ollama with easy provider switching.
Retrieval Augmented Generation (RAG)
Connect your own data sources (PDFs, websites, databases, APIs) with built-in vector database support (Pinecone, Weaviate, Chroma, Qdrant, Milvus) for grounded, factual responses.
Conversational Memory Systems
Implement conversation history with various memory types including buffer memory, summary memory, and entity memory to create contextual, coherent chatbot interactions.
AI Agent Capabilities
Build autonomous agents that can use tools, make API calls, query databases, execute code, and perform multi-step reasoning to accomplish complex tasks.
RESTful API & Embeddings
Expose LLM flows via REST API for integration with external applications. Includes built-in embedding functionality and webhook support for real-time notifications.
Common Use Cases
- **Customer Support Chatbots**: Build intelligent support agents that answer questions using your knowledge base, documentation, and FAQs with accurate, contextual responses
- **Internal Knowledge Assistants**: Create AI assistants that help employees search company documentation, policies, and procedures across multiple data sources
- **Content Generation Workflows**: Design automated content creation pipelines for blog posts, social media, product descriptions, or marketing materials with custom prompts and templates
- **Data Analysis & Reporting**: Deploy AI agents that can query databases, analyze data, generate insights, and create natural language reports based on business metrics
- **Document Processing & Q&A**: Build applications that ingest PDF manuals, contracts, or research papers and answer questions about their content with source citations
- **Intelligent Automation Agents**: Create AI-powered automation workflows that can make decisions, call APIs, send notifications, and trigger actions based on natural language instructions
Installation Guide
Installing Flowise on a VPS requires Node.js 18 or higher. Begin by updating your system and installing Node.js from NodeSource repository. Clone the official Flowise repository from GitHub, navigate to the project directory, and install dependencies using npm or yarn.
For production deployments, configure environment variables for database connections, API keys, and authentication settings. Flowise supports multiple database backends including SQLite (development), PostgreSQL (recommended for production), and MySQL. Set the DATABASE_PATH or DATABASE_URL environment variable accordingly.
Create a systemd service file to run Flowise as a background service, ensuring it restarts automatically after system reboots. Configure Nginx or Apache as a reverse proxy to handle SSL termination and serve Flowise over HTTPS on port 443 while the application runs on port 3000 internally.
For optimal performance, allocate at least 4GB RAM to prevent memory issues when loading large language models or processing multiple concurrent requests. Enable process management with PM2 for automatic restarts, log management, and zero-downtime deployments.
Secure the installation by configuring FLOWISE_USERNAME and FLOWISE_PASSWORD environment variables for basic authentication. Consider implementing additional security layers such as IP whitelisting, VPN access, or OAuth2 integration for enterprise deployments.
To integrate vector databases, install necessary dependencies and configure connection strings for Pinecone, Weaviate, or self-hosted solutions like Chroma or Qdrant. Ensure firewall rules allow outbound connections to LLM provider APIs and inbound HTTPS traffic on port 443.
Configuration Tips
Key configuration options are managed through environment variables. Set FLOWISE_USERNAME and FLOWISE_PASSWORD to enable authentication for the web interface. Configure DATABASE_TYPE (sqlite, postgres, mysql) and DATABASE_PATH or DATABASE_URL for data persistence.
For LLM integrations, provide API keys through environment variables like OPENAI_API_KEY, ANTHROPIC_API_KEY, or GOOGLE_PALM_API_KEY depending on which providers you plan to use. These can also be configured per-chatflow through the UI for multi-tenant scenarios.
Customize the application port with PORT environment variable (default 3000). Set CORS_ORIGINS to restrict which domains can access the Flowise API. Enable FLOWISE_SECRETKEY_OVERWRITE for custom JWT token generation in production.
Configure file upload limits with FILE_SIZE_LIMIT environment variable to control maximum document sizes for RAG implementations. Set DEBUG=true for development environments to enable detailed logging and error messages.
For vector database integrations, configure connection parameters through environment variables specific to each provider (PINECONE_API_KEY, WEAVIATE_URL, etc.). Implement caching strategies using Redis to improve response times for frequently asked questions.
Best practices include using a reverse proxy for SSL termination, implementing rate limiting to prevent API abuse, regular backups of the database containing chatflow configurations, and monitoring logs for errors or performance bottlenecks using tools like PM2 or systemd journalctl.
Technical Requirements
System Requirements
- حافظه: 4GB
- CPU: 2 cores
- ذخیرهسازی: 10GB
Dependencies
- ✓ Node.js 18.x or higher
- ✓ npm or yarn package manager
- ✓ PostgreSQL (optional, for persistence)
- ✓ Redis (optional, for caching)
- ✓ API keys for LLM providers (OpenAI, Anthropic, etc.)