Open-source visual editor (acquired by DataStax/IBM) for building, prototyping, and deploying agentic LLM workflows with hundreds of pre-built components.
Open-source visual editor (acquired by DataStax/IBM) for building, prototyping, and deploying agentic LLM workflows with hundreds of pre-built components.
Langflow is a visual drag-and-drop builder for LLM agents and workflows with 200+ components, bi-directional MCP support, and a managed cloud from DataStax.
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Excellent learning environment for teams that need to see how LLM, vector database, prompt, and tool components connect. The main caution: Public cloud pricing could not be verified from fetched HTML, so teams need to confirm costs manually.
Browser-based canvas for building AI applications by connecting component nodes. Real-time validation, node-level output inspection, and interactive testing in the built-in playground let you build and verify behavior without round-tripping through code.
Use Case:
Building a multi-agent research system visually: connect a web search agent, document analysis agent, and summary generator to create a complete research pipeline without writing boilerplate.
Create custom components as Python classes directly within the Langflow UI. Components can use any Python library (pandas, numpy, requests, custom SDKs), access external services, and integrate with built-in components on the same canvas.
Use Case:
Building a custom database query component that connects to your company's API and returns structured data for use in the AI pipeline.
Build multi-agent systems where agents with different tools and capabilities collaborate within a visual flow. Supports sequential, parallel, and conditional routing patterns with visual configuration of agent roles and tool access.
Use Case:
Customer service system where a router agent directs queries to specialized billing, technical support, or account management agents.
Every Langflow workflow automatically becomes available as an MCP server, allowing other AI agents and applications to call your flows as tools through the Model Context Protocol. No additional server code or wrapping needed.
Use Case:
Exposing a document analysis pipeline as an MCP tool that Claude Desktop, Cursor, or other MCP-compatible clients can invoke directly.
Interactive playground for testing flows with real-time execution. Inspect inputs, outputs, and intermediate state at every node without redeploying — making debugging far more visual than tracing through code or log files.
Use Case:
Debugging a RAG pipeline by inspecting retrieved documents at the retriever node, the formatted prompt, and the final model output step by step.
Deploy flows as API endpoints, run locally via pip install or Docker, use the desktop app for offline development, or deploy to the free cloud tier. Export flows as JSON for programmatic loading and version control.
Use Case:
Deploying a document analysis flow as a REST API that your web application calls when users upload documents for AI-powered review.
Free (MIT)
Custom
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DataStax's managed Langflow hosting was deprecated in March 2026 with full shutdown on April 9, 2026, consolidating activity around the open-source project and the free cloud tier at langflow.org. The platform has expanded native (non-LangChain) components and now includes built-in MCP server generation, making every flow callable as a tool by Claude Desktop, Cursor, and other MCP clients. Active development continues with 50,000+ GitHub stars.
AI Agent Framework
Open-source visual LLM and agent builder — drag-and-drop canvas on a Node.js/TypeScript stack, with MCP nodes and a managed Flowise Cloud option.
LLM app platform
Dify is an open-source LLM app development platform that combines a visual workflow builder, RAG pipelines, agent tools, and an LLMOps backbone.
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