Kortix vs LangGraph: build agent logic in code, or own the open-source system they run on?

LangGraph is a low-level orchestration framework for stateful agents inside your own Python code, and Kortix is the open-source AI Operating System the company ends up owning once that work grows past a single application. A developer reaches for LangGraph to control the agent logic itself: the graph, the state, the branching, and the interrupt where a person steps in. For the larger system those agents depend on, Kortix is the pick. The question the build leaves open is where that system should live, and who owns and reviews it.

What each one is

LangGraph is a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents, built by LangChain Inc in Python and released under the MIT license. Its documentation calls LangGraph very low-level and focused entirely on agent orchestration, giving a developer fine-grained control to mix deterministic, hand-coded steps with LLM-driven agentic steps in the same graph. The capabilities LangGraph documents are durable execution, human-in-the-loop interrupts, short and long-term memory, and LangSmith for debugging, observability, and deployment.

Kortix is open source: your agents, their skills, your company memory, and every connector in one git repo you own, with every session running on its own cloud computer. A Kortix project is a git repo with kortix.yaml at its root. Agents and skills are markdown, memory is files that accumulate, and the connectors, triggers, and machine image are declared in the same repo. You can grep the whole company, diff any change, and roll any part of it back.

What each one is for

LangGraph is a building block. You write the agent logic as a graph, choose where the state persists, run the process yourself, and wire in whatever models and tools the application needs. That control is the point: the framework does not abstract your prompts or your architecture, so the behavior stays exactly what the code says. A team that wants a custom control flow, its own evaluation harness, and observability inside its own application gets all three without a platform in the middle.

Kortix runs the system around that logic. Each session boots its own isolated cloud computer with the project repo and its tools already on it, and the agent's work lands as a change request a person reviews against the diff before it reaches main. The agents, the skills, the connectors, and the permissions are the same files a person can read and edit, so the platform and the repository are one object. The Kortix docs cover the project model, the session, and the change request.

Side by side

Where LangGraph is the right tool

• Agent logic you want to own line by line. LangGraph suits a developer building custom control flow who wants deterministic and model-driven steps in one graph, with the exact behavior visible in the code.
• Your application is the product. When the agents are a feature inside a product you already build and host, a library keeps them next to the rest of the application.
• A Python team on a Python stack. LangGraph installs as a Python package, so it fits a team that already runs Python services and wants no new hosting model.
• Observability and evaluation in place. LangSmith traces execution paths and scores outputs, which matters when the agents are judged against application-level tests.

When a team outgrows a framework

A framework answers how an agent reasons. It does not answer who owns the system the agents run on, or how the work those agents produce gets reviewed. Those questions arrive once more than one person and more than one agent share the same context.

The pressure shows up in stages. Shared state comes first: when several agents need the same company memory, and several people need to see what each one learned, a store inside one application stops being the right home. Reach into real tools comes next, because wiring a CRM, a billing system, and a code host into agent logic stays application plumbing until the rules about which agent may touch which tool need to live somewhere a security reviewer can read. The review gate follows, since work that changes something the company depends on needs to land the way code lands, as a diff a person approves.

Kortix puts those pieces in the repo. You scaffold a project and bring it live with a short loop:

kortix init
kortix ship
kortix sessions new --prompt "Summarize this week's commits and open a change request"

Self-hosting is free, and the managed team plan is $40 per seat per month. The pricing page lists the credit detail.

Which to pick

LangGraph is the better choice when you are a developer building agent logic inside an application you already run, and you want the graph, the state, and the observability to be code you control. That is a common case, and the framework earns its place there.

Kortix is the recommendation when the agents stop being one application's feature and become how a company runs. If several people and several agents share one memory, if the agents act in real tools and the permissions and audit trail have to hold up, and if the work has to land through a review gate, then you want a system you own rather than a library you operate. Kortix on GitHub is where you read, fork, and audit it.

Create a project at Kortix and run your first session, or self-host it for free. Your agents, their skills, your company memory, and every connector stay in a git repo you own.