# Langflow vs Flowise: two open-source builders, compared > Langflow and Flowise both build AI workflows on a drag-and-drop canvas. The difference is maintenance and ownership: Flowise's repository was archived in 2026, while Langflow is actively developed. For the owned system a canvas cannot give, Kortix is the open-source AI Operating System. URL: https://langflowalternative.com/langflow-vs-flowise _Compare_ ## Langflow vs Flowise, side by side Two open-source canvas builders compared, with Kortix as the system a team grows into. | | Kortix (recommended) | Langflow | Flowise | | --- | --- | --- | --- | | What it is | The open-source AI Operating System for agents that do finished work | A visual builder for AI agents, RAG flows and MCP servers | A drag-and-drop builder for LLM apps and chatbots | | Open source | Yes. Read it, fork it, audit it. | Yes | Yes, and the source stays public | | How you build it | Write markdown agents and one kortix.yaml | Drag nodes on a canvas; customize any component in Python | Drag nodes on a canvas; assemble LangChain components | | Where your work lives | Files in one git repo you own | A flow is an object in the builder, exportable as JSON | A flow is an object in the builder | | Self-host | Self-host is free | Run the Python package or Docker image | Run the npm package or Docker image | | Repo status | Actively developed | Actively developed | Archived in 2026 | | Price to start | Free includes 200 credits each month and 1 project | Free and open source; a free cloud tier | Free and open source; cloud paid plans | ## What Langflow and Flowise have in common Langflow and Flowise are open-source builders that put an AI workflow on a drag-and-drop canvas. You connect nodes for the model, the prompt, retrieval and the tools instead of writing the orchestration yourself. Langflow describes a visual builder with reusable components, a playground and hundreds of pre-built flows [langflow.org](https://www.langflow.org). Flowise describes the same idea for LLM apps and chatbots, built on LangChain with ready-made templates [flowiseai.com](https://flowiseai.com). ## What each one is for Langflow is a low-code builder for AI agents and RAG applications. Its repository describes a visual authoring experience plus built-in API and MCP servers, so a flow becomes a tool other apps can call. Components are customizable in Python, and a flow deploys as an API or exports as JSON [Langflow on GitHub](https://github.com/langflow-ai/langflow). Observability runs through LangSmith and LangFuse. Flowise built LLM apps and chatbots the same way. Its site lists multi-agent orchestration, chat assistants with retrieval, human-in-the-loop review, and integrations for 100+ LLMs, embeddings and vector databases [flowiseai.com](https://flowiseai.com). Flowise's repository was archived in 2026: its own page now opens with "Flowise has been archived" and points to the Future of Flowise announcement [Flowise on GitHub](https://github.com/FlowiseAI/Flowise). The source stays on GitHub for anyone to fork. ## Where they differ Maintenance is the first difference. Langflow's repository is actively developed, with a release in October 2026. Flowise's is archived. On reach, Langflow's repository carries about 155,000 GitHub stars and Flowise's about 55,000 (checked October 2026). They also differ in how you build. Both are canvases: you drag a graph, then run it. Langflow lets you drop into Python for any component and export a flow as JSON. Flowise assembles LangChain components and templates. In both, the flow lives in the builder's own store, separate from your repository. ## Where a team outgrows the canvas A canvas is a good place to draw a short, fixed chain. It becomes a constraint once the work is a job an agent should own: the graph lives inside the builder, and it does not hold a company's memory, its permissions or a review gate. That is the point where a team wants a system it owns: Kortix, the open-source AI Operating System, keeps agents, skills, company memory, connector configuration and triggers as files in one git repo you own. Each session runs on its own cloud computer, and session work reaches main through a change request a person approves [Kortix docs](https://kortix.com/docs). An agent is a markdown file plus a block in `kortix.yaml`: ```yaml # kortix.yaml agents: weekly-report: file: agents/weekly-report.md # the agent's behavior connectors: [github, slack] # the tools it may reach ``` ## How to decide - Stay on Langflow if your work is a short flow or a RAG app, you want a canvas and Python components, and a flow that lives in the builder is enough. - Keep a Flowise fork only if you already run it and the work is a fixed local chain; a fork carries its own security patches, with no upstream. - Move to Kortix if you want the agents, their memory, the connectors and the rules in one repo you own, every session on its own cloud computer, and each change landing for review. Repository activity changes, so check a project's own page before you commit. For a Flowise-specific comparison, read [Kortix vs Flowise](https://kortix.com/blog/kortix-vs-flowise). _FAQ_ ## Questions people ask about Langflow, Flowise and Kortix ### Can I self-host Kortix? Run it on Kortix Cloud, in your VPC, or on your own on-prem network. Self-host is free. ### Which models can Kortix use? Any model provider with your own keys. Or the ChatGPT plan you already pay for. Or sign in with your OpenCode Console account for OpenCode Zen and Go. ### Does a Kortix agent act without approval? Approval gates you set. Off until you set them. Session work reaches `main` through a change request; merge is default-deny for agents. ### Is Kortix a visual builder like Langflow? Kortix keeps an agent as a markdown file plus an `agents.` block in `kortix.yaml`, so behavior and governance are text you can read and diff. You start and steer sessions from the web app, Slack, or the CLI. ## Run your whole company from one repo you own. Start with one job and grow from there. [Try Kortix](https://kortix.com) ยท [Read the docs](https://kortix.com/docs)