Move from local development to running agents in production
You have a working flow on your laptop. Now you want it running in the cloud, surviving restarts, and accessible to your team. Three steps.
Locally, the server runs embedded in your Python process. In production, you deploy it as a standalone service so your team can share a single view of all executions — and so agents can run independently of your machine.
The server stores execution metadata, checkpoint state, and logs. It does not access your cloud storage directly — it brokers temporary credentials so clients and the UI can read artifacts when needed.
Point your local client at the deployed server:
kitaru login https://kitaru.your-company.comFrom here, the CLI, KitaruClient, and the UI all talk to the same server. Any executions you start will be visible to your whole team.
A stack is a named runtime that tells Kitaru where to run your agent code and where to store its outputs. Pick the compute backend that matches your cloud:
Once your stack is created, switch to it:
Your code doesn't change. The same flow, the same checkpoints, the same replay — now running on cloud compute with durable storage.
When you call .run(), the client fetches short-lived credentials from the server and dispatches the execution directly to your stack's compute backend. Checkpoint outputs are written to cloud storage. You can observe the execution from the UI, the CLI, or any KitaruClient connected to the same server.
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kitaru stack use prod-k8sif __name__ == "__main__":
research_agent.run(topic="durable execution for AI agents")