Kortix vs Langflow: one flow is a JSON export, five are a repo you can diff

Langflow is the quickest way to get one AI flow onto a canvas, and Kortix is the open-source AI Operating System a company reaches for once it runs five. A year of flows leaves a team owning different things. A Langflow team keeps a JSON export per flow and the workspace database around them; a Kortix team keeps a repository it can read, diff and roll back.

An afternoon on the canvas

Langflow is a low-code, Python-based builder for AI applications. Its documentation calls it an open-source, Python-based, customizable framework for building AI applications. You work in a visual editor, connecting component nodes into a flow. Each node is one step, and you can run a single one or test the whole flow in the Playground before wiring it into an application.

The project repository lists a visual builder, Python access to change any component, and multi-agent orchestration with conversation management and retrieval. A flow also deploys as an API, or as an MCP server that turns it into a tool other MCP clients can call. It supports the major LLMs and vector databases, installs as a Python package or a Docker image, and ships a desktop app for macOS and Windows (install guide). The core is MIT-licensed, which lets you use, modify, and redistribute it (LICENSE).

• A canvas is easy to see and iterate on. You drag components into a flow and get real-time feedback in the Playground. A first working prototype comes together without an orchestration layer to write (Langflow docs).
• Python is there when the canvas is not enough. Source-code access lets you customize any component in Python, so a flow you drew stays open to change (langflow-ai/langflow).
• A flow ships as an API or a tool. Langflow deploys a flow as an API and exports it as JSON for a Python app. It can also serve the flow as an MCP server, so other MCP clients use it as a tool (langflow-ai/langflow).
• The install stays light. A Python package, a Docker image, or a desktop app, with deployment guides for Docker, Kubernetes, or a remote server (install guide).

For one AI feature in one afternoon, those strengths decide the choice.

What the repo holds

An agent in Kortix is a markdown file. Beside it, kortix.yaml names the image each session boots and the integrations an agent may call. The same file lists the schedules that wake it and the secrets it expects. Memory is plain files that grow as work runs. The repository holds all of it (Kortix docs). That makes an agent and a colleague two editors of one object. Anyone on the team can search the whole company, read a diff and undo part of it.

A project is that repository, with the manifest at its root. One isolated sandbox per session, each on a branch cut from the repo. Thousands can be in flight at once, and a schedule can start one with nobody in the room:

triggers:
  - slug: flow-drift-check
    type: cron
    cron: "0 30 6 * * 1-5"
    timezone: Europe/Berlin
    prompt: Diff each flow exported from Langflow yesterday against the copy in flows/, and open a change request for every node that changed.

Models come from whichever provider you hold a key with, or from the ChatGPT subscription you already pay for (Kortix docs). Connector credentials are brokered server-side and never enter the machine. Run it on Kortix Cloud, in your VPC, or on your own on-prem network. Self-host is free. The paid plan is credits from $50 a month, at 100 credits per dollar (Kortix pricing, read October 2026).

Where the canvas runs out

Langflow is organized around the flow you build. A company runs on more than one flow, and four things follow from that.

• Configuration lives in the Langflow workspace. Flows are built and stored in the app. A flow exports as JSON, but the connectors, permissions, and memory around it are not one repo a team diffs and reviews (Langflow docs).
• How work lands is a served flow. Langflow deploys a flow as an API or an MCP server (langflow-ai/langflow). In Kortix, work arrives as a change request against main.
• The runs share one server. You deploy Langflow as a server or a container, and it serves flows from there (deployment docs). Its own security page says it provides no isolation between tenants, and that authentication and authorization must be enforced outside the container. A run does not get its own computer the way a Kortix session does.
• The deliverable is an application. Langflow's output is a flow inside an app, and the people, roles, and audit trail a company needs sit outside the flow.

A visual builder is the right tool for one flow. The limits show up when a company runs many pieces of work and needs one place to own them.

The gate on the way back in

Session work reaches main through a change request. A person merges it unless the agent's kortix_permissions grant includes project.gitops.merge, so merge is default-deny for agents (Kortix docs).

Approval gates are off until you set them. Each connector call is set to Allow, Ask or Block, and a rule can reach down to the arguments of a call. A call set to Ask pauses the run until a person answers, then the work resumes. Per-resource permissions for people and agents. Roles, groups, and an audit trail.

A Langflow flow ends as an application you publish and then look after by hand. In Kortix the last step of a job is a diff against the repo, and that diff is what the next person reads.

Who should stay on Langflow

For a single AI application, Langflow is the faster path. Its Python package or Docker image starts a working editor in minutes (install guide). The canvas needs no repo, and the Playground tests a flow before it ships (Langflow docs). A team that wants one retrieval pipeline or one customer-facing assistant, and nothing more, gets there with less to run and less to maintain. Kortix asks you to adopt a repo, a review process, and a session runner, which is more than one flow needs.

The two shapes, row by row

The call, for each kind of team

Next reads: Kortix vs LangGraph covers the code-first frameworks under a builder, and Kortix vs Flowise and Kortix vs Dify compare two more visual builders.

Start free, self-host free. Try Kortix and put all five flows in a repo your team reviews.