Agents Never Sleep vs A.I.G (AI Infra Guard): Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agents Never Sleep and A.I.G (AI Infra Guard) — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agents Never Sleep
Agents Never Sleep
A tiny $4.99 Mac menu bar app that keeps long-running AI agents alive with the laptop lid closed, using one three-level slider.
Key features
- Closed-Lid Operation: Keeps agents and background jobs running with the MacBook lid shut, which the built-in caffeinate command cannot do.
- Three-Level Slider: One control with Sleepy, Awake, and Never sleeps settings, so changing sleep behavior takes a single drag rather than a terminal command.
- Menu Bar Only: Lives entirely in the menu bar with no window, dock icon, or configuration screens to manage.
- Native and Tiny: A small native macOS binary rather than a wrapped web app, minimizing overhead on a machine already running agents.
- Zero Data Collection: No telemetry, analytics, or tracking; what runs on the machine stays on the machine.
- Safe Revert: Toggling back down restores normal sleep behavior, avoiding the common mistake of leaving sudo pmset -a disablesleep 1 permanently enabled.
- No-Questions Refund: A 14-day money-back guarantee with no form to fill in and no reason required.
Best for
- Overnight Agent Runs: Letting a coding agent work through a long task while the laptop is closed and put away.
- Long Builds and Tests: Preventing a multi-hour build, test suite, or data job from being interrupted mid-run by sleep.
- Mobile Working: Carrying a MacBook between rooms or offices without propping the lid open to keep a job alive.
- Model Downloads and Training: Keeping large model downloads or local fine-tuning runs going unattended.
- Avoiding pmset Mistakes: Replacing manual sudo pmset toggles that are easy to enable and easy to forget to undo.
- Remote Sessions: Keeping a Mac reachable and working for a remote session or background service while it sits closed on a desk.
A.I.G (AI Infra Guard)
Tencent Zhuque Lab
Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.
Key features
- Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
- MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
- AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
- Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
- Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
- Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
- Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
- AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.
Best for
- Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
- MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
- CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
- Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
- AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
- Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.