Build AI Apps Visually with Flowise

Flowise is a visual drag-and-drop tool for building LLM applications. It's built on LangChain but requires no coding. Ideal for: non-developers wanting AI chatbots, developers prototyping quickly, teams democratizing AI development, and anyone who learns better visually. Export flows as APIs for integration.

Multiple options: 1) npx/npm for local development, 2) Docker for containerized deployment, 3) Railway/Render for one-click cloud hosting, 4) Your own servers for full control. Flowise Cloud offers managed hosting. For production, use Docker with proper environment variables and database persistence.

Yes, Flowise uses your API keys stored locally or in environment variables. Keys are never shared with Flowise (unless using their cloud). Support for OpenAI, Anthropic, Google, Cohere, Hugging Face, Replicate, and more. Manage keys in the 'Credentials' section for secure storage.

Same capabilities, different interfaces. Flowise is faster for prototyping and non-developers. Direct LangChain offers maximum flexibility and version control. Many teams prototype in Flowise, then implement in code for production. Flowise also exports flows as JSON, bridging both worlds.

Flowise supports: Pinecone, Qdrant, Weaviate, Chroma, Supabase, Redis, Milvus, Postgres/pgvector, Faiss (in-memory), and more. Each has a dedicated node. Connect your embedding model, data source, and vector store to build RAG applications without coding.

Yes, Flowise is built for extensibility. Create custom nodes in JavaScript/TypeScript following the node template. Place in the packages/components/nodes folder. Custom nodes can wrap any API, add business logic, or integrate proprietary systems. Community shares custom nodes on GitHub.

Copy the embed code from your chatflow's 'Embed' tab. Options include: 1) Full page chat, 2) Floating bubble widget, 3) Popup modal. Customize colors and position. For advanced customization, use the API endpoint directly and build your own UI. The embed widget works on any website.

Yes, Flowise supports streaming for chat models that support it (OpenAI, Anthropic). Enable streaming in the chat model node. The API returns server-sent events for real-time responses. The built-in chat widget handles streaming automatically. Implement SSE handling for custom UIs.

Use persistent memory nodes: Redis, PostgreSQL, MongoDB, or Supabase for chat history. In-memory options are fine for development but lost on restart. Configure the memory node with your database credentials. Each session gets a unique ID for retrieving conversation history.

Yes, Flowise handles concurrent users well. Each API call is independent. For scaling, use Docker with multiple replicas behind a load balancer. Consider caching strategies for expensive LLM calls. Database-backed memory ensures session isolation. Monitor resource usage for optimal scaling.

Flowise supports: API key authentication for endpoints, basic auth for the dashboard, and integration with OAuth via custom middleware. For enterprise, deploy behind your existing auth proxy. Flowise Cloud includes user management and API key controls.

Enable the chat logs panel to see step-by-step execution. Check individual node outputs in the builder (green dots = success). Review browser console for frontend errors and server logs for backend issues. Isolate problems by testing individual nodes. Common issues: missing credentials, context length exceeded, or API rate limits.