Crewship

About Crewship

Crewship is deployment infrastructure for AI agents built with existing frameworks, aimed at the gap between an agent that runs on a laptop and one that runs in production. You install a CLI, run a single deploy command and the agent is live — no Dockerfile, no infrastructure config, no cluster to size. It is deliberately framework-agnostic rather than another agent framework: CrewAI, LangGraph and LangGraph.js are live today, with Agno, AutoGen, Pydantic AI, OpenAI Agents and Mastra listed as coming, so the deployment layer does not force a rewrite of how you build. Once deployed, runs stream in real time over Server-Sent Events, so you watch an agent think, decide and act step by step instead of waiting for a final payload — which matters most when debugging a long chain that fails halfway. Artifacts are handled as a first-class concern: every file, report and output an agent produces is collected automatically and retrievable through the API, which is usually the piece teams end up building themselves. Deployments are versioned, so rollback is immediate and outputs can be compared across versions with a full audit trail. Secrets are encrypted and environments isolated, and the platform auto-scales from zero to thousands of concurrent runs so idle agents cost nothing. A web console covers deployments, run details and schedules for the cases where the CLI is not where you want to be.

Key Features

Crewship Pros & Cons

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