Open-source no-code AI workflow builder and visual LLM application platform with drag-and-drop interface. Build chatbots, RAG systems, and AI agents using LangChain components, supporting 100+ integrations.
Build AI chatbots, RAG systems, and LLM applications using drag-and-drop visual workflow builder. Open-source no-code platform for creating conversational AI with LangChain components.
Flowise is an open-source agentic systems development platform that lets you build AI agents and LLM applications visually through a drag-and-drop interface, with the core platform available free for self-hosting and managed cloud options available for teams seeking hosted deployment. The project has amassed over 35,000 GitHub stars since its initial release, reflecting strong community adoption among developers and non-technical teams building conversational AI solutions.
At its core, Flowise provides two primary workflow modes: Chatflow for building single-agent chatbots with retrieval-augmented generation (RAG) and tool calling, and Agentflow for orchestrating multi-agent systems where multiple coordinated agents handle complex task decomposition with handoffs between them. The platform ships with a component library of over 100 integrations spanning LLM providers (OpenAI, Anthropic, Google, Cohere, Mistral, and local models via Ollama), vector databases (Pinecone, Weaviate, Qdrant, ChromaDB, Milvus, pgvector), cloud platforms (AWS, GCP, Azure, Railway), and communication tools (Slack, Discord, Twilio). This breadth of connectors means teams can wire together their preferred stack without writing custom integration code.
For document-based RAG workflows, Flowise supports ingesting 8 file formats including TXT, PDF, RTF, DOC, HTML, CSV, MD, and SQL, allowing users to visually configure retrieval pipelines that chunk, embed, and store documents in vector databases. Human-in-the-Loop (HITL) workflows add a critical approval layer where humans review and validate agent actions before execution, making Flowise suitable for compliance-sensitive industries like healthcare and finance where AI outputs require oversight.
Deployment is straightforward: install via npm (npm install -g flowise), Docker using the official flowise/flowise image, or one-click deploy on cloud platforms like Railway, Render, and Replit. Every chatflow can be deployed as a REST API endpoint at /api/v1/prediction/:id with a single click, and Flowise provides an embeddable chat widget for website integration alongside TypeScript and Python SDKs for programmatic access. The built-in conversation memory persistence ensures continuity across sessions without additional configuration.
Observability is built into the platform with full execution traces that support Prometheus and OpenTelemetry, enabling teams to track every node execution, LLM call, tool invocation, and token usage across their workflows. For monitoring and debugging, Flowise also integrates with LangSmith and Langfuse for detailed trace analysis.
Enterprise deployments benefit from horizontal scaling through message queues and workers, on-premises and cloud deployment options, SSO, and role-based access control. The community marketplace offers pre-built chatflows for common use cases, and real-world production usage includes companies like Qmic Qatar, which uses Flowise function-calling capabilities in their iFleet product's copilot features. Being Node.js-based and written in TypeScript, the platform aligns well with JavaScript-centric development teams and supports custom component development for advanced use cases beyond the built-in library.
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Flowise provides an excellent drag-and-drop interface for building LLM workflows based on LangChain components. Perfect for visual thinkers and rapid prototyping, though complex production deployments require understanding the underlying framework concepts.
Build multi-agent systems with workflow orchestration distributed across multiple coordinated agents. Each agent can have its own tools, memory, and instructions, with handoffs between agents for complex task decomposition.
Build single-agent systems and chatbots with support for tool calling and knowledge retrieval (RAG) from various data sources. Supports document formats including TXT, PDF, RTF, DOC, HTML, CSV, MD, and SQL.
Allow humans to review tasks performed by agents within the feedback loop before final execution. This is critical for regulated industries and high-stakes decisions where AI outputs need human validation.
Full execution traces support Prometheus, OpenTelemetry, and other observability tools out of the box. Track every node execution, LLM call, tool invocation, and token usage across your workflows.
Extend and integrate to your applications using REST APIs, TypeScript and Python SDKs, and an embeddable chat widget. Deploy any chatflow as a /api/v1/prediction/:id endpoint with a single click.
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Visual builder support for multi-agent conversations and handoffs.
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Flowise has expanded its Agentflow capabilities for multi-agent orchestration, added Human-in-the-Loop (HITL) workflows for regulated industries, and improved observability with Prometheus and OpenTelemetry support. The platform continues to grow its community marketplace and component library.
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