Lobster Trap: OpenClaw in Containers from Local to K8s and Back — Sally Ann O'Malley, Red Hat

2026-05-22 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

Sally Ann O'Malley from Red Hat advocates for running the open-source AI agent framework OpenClaw within containers and Kubernetes, drawing on her 10 years of experience with Linux security and OpenShift. She highlights containerization's benefits for AI workloads, including enhanced reproducibility, secure secret isolation, infrastructure portability (laptop, X86, Mac, Kubernetes), natural sandboxing, and robust backup/recovery via volumes. O'Malley demonstrates a local installer that streamlines OpenClaw setup, integrating Podman secrets for API key management, configurable AI providers like Open Router and Anthropic, and optional Open Telemetry with Jaeger for observability. She envisions OpenClaw scaling across enterprises, citing an Nvidia team using it with 10 engineers for model evaluations, and proposes a workplace model for standardized, yet customizable, team-specific OpenClaw environments.

Key takeaway

For AI Engineers or MLOps teams deploying AI agents, embracing containerization for frameworks like OpenClaw is crucial for operational efficiency and security. You should use container platforms like Podman or Kubernetes to ensure reproducibility. Isolate sensitive API keys using native secret management. This approach streamlines deployment from local development to scaled production. It also simplifies onboarding and standardizes agent configurations across your team, freeing up time for creative tasks.

Key insights

Containerizing AI agent frameworks like OpenClaw provides secure, reproducible, and portable environments for development and scalable deployment.

Principles

• Containerization ensures application reproducibility and isolation.
• Secrets management should separate credentials from application code.
• Develop locally, then lift to Kubernetes for scale.

Method

Use a local installer to spin up OpenClaw in Podman/Docker, configuring API keys via Podman secrets, selecting AI providers, and optionally integrating Open Telemetry for observability.

In practice

• Run OpenClaw agents in Podman or Docker containers.
• Implement Podman secrets or Kubernetes secrets for API keys.
• Standardize team-specific OpenClaw environments for new hires.

Topics

• OpenClaw
• Containerization
• Kubernetes
• Podman
• AI Agents
• Secrets Management
• MLOps

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.