Kortix vs Langflow: an open source system you own, or a visual flow builder
Kortix is open source and runs each agent session on its own cloud computer. Langflow is a low-code builder where you drag nodes into a visual flow. Both are built for AI work, and they solve different halves of it.
Kortix vs Langflow, side by side
Two open source projects built for different jobs, compared on how you build with each and where each one runs.
| Criteria | Open source | What it is | How you build | What it runs | Everything is code | Where it runs | Pick it for |
|---|---|---|---|---|---|---|---|
| Our pickKortix | Yes. Read it, fork it, audit it. | An open-source system that runs agent sessions | Markdown and kortix.yaml in a git repo | Agents on their own cloud computers, from Slack | Agents, skills, memory, and connectors are files you own | Kortix Cloud, your VPC, or on-prem; self-host free | A team that wants to own its agent system |
| Langflow | Yes | A low-code builder for AI workflows and agents | Nodes dragged onto a visual canvas | Flows you run, or serve as an API or MCP server | Flows export as JSON; components are Python | A Python package, a Docker image, or Langflow's cloud | Shipping one AI workflow quickly |
What Kortix is
Kortix is the open-source AI Operating System: your agents, their skills, your company memory, and every connector in one git repo you own, with the agents working on real cloud computers. One isolated sandbox per session. Each session has its own isolated machine and branch. Session work reaches main through a change request. Merge is default-deny for agents. Thousands of agents in parallel on one config, each on its own cloud computer.
What Langflow is
Langflow is a low-code builder for AI agents and workflows. Its documentation describes it as an open-source, Python-based framework for building AI applications (Langflow docs), and its repository lists a visual builder, Python access to every component, multi-agent orchestration, and deployment as an API or an MCP server (langflow-ai/langflow). It supports all major LLMs and vector databases, runs from a Python package or a Docker image, and ships a desktop app for Windows and macOS.
Where Langflow fits
Langflow fits a builder who wants to see the flow. You drag nodes onto a canvas, wire a model to a data source, test the result in a playground, and then serve it as an API or hand it to an MCP client. For a retrieval pipeline or a fixed sequence of steps you can draw, the canvas is the place to iterate. Langflow also ships pre-built templates, and its own site points to hundreds of ready-made flows and components (langflow.org).
Where Kortix fits
Kortix is built for a team that wants to own the system. Everything the agents are or know sits in files you control: agents/<name>.md for what each agent does, kortix.yaml for its connectors, triggers and permissions, and the memory files the company accumulates. The code is open source. Read it, fork it, audit it (Kortix). A person can grep the whole company.
Kortix runs the work as agents. It reaches 3,000+ apps in a click, plus MCP, OpenAPI, Postman, GraphQL and raw HTTP, and connector credentials are brokered server-side and never enter the machine (Kortix docs). 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. Slack and Microsoft Teams are live. Run it on Kortix Cloud, in your VPC, or on your own on-prem network. Self-host is free.
How to choose between them
Pick Langflow when the job is a flow you want to draw, see, and iterate on, such as a retrieval pipeline or a sequence of steps that stays the same each run. Pick Kortix when the job is open-ended work that needs its own machine and a schedule: research, a report, a fix, a reply in Slack. The follow-up question is where the configuration lives, because that decides who can review and change it. If you are weighing a visual builder against the code-first frameworks underneath it, Kortix vs LangGraph covers that choice.
Questions teams ask before they pick
Short answers to what comes up most often.
- 01
Is Langflow open source?
Yes. Langflow is developed in the open on GitHub, you can run it yourself, and you can customize any component in Python (langflow-ai/langflow). Kortix is open source too. Read it, fork it, audit it.
- 02
Do I need to write code to build with Langflow?
No. The visual canvas is the main way to build, and you can ship a flow without opening Python. You write code only when you want to change a component, which Langflow lets you do in Python.
- 03
Can Kortix and Langflow be used together?
Yes. Langflow can deploy a flow as an API or an MCP server, and a Kortix agent connects through MCP, OpenAPI, Postman, GraphQL and raw HTTP. A team can keep Langflow for the flows it wants to draw and run Kortix for work that has to run on its own.
Own the system your agents run on.
Your agents, their skills, your company memory, and every connector in one git repo you own.