haskaomni

haskaomni / agentic-cpu

Public

Codex CLI CPU 火焰图分析：AI Agent 真的需要那么多 CPU 吗？

16
1
100% credibility
Found Apr 20, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This project shares flame graph visualizations and analysis revealing that an AI coding agent's high CPU usage stems mostly from process startup and cleanup overhead rather than intensive local computation.

How It Works

1
🔍 Wonder about AI power use

You hear AI helpers guzzle computer power and get curious to learn the real story.

2
📖 Discover the report

You find this easy-to-read guide packed with charts breaking down where all the effort goes.

3
📊 View busy time picture

Stunning colorful charts show most computer time spent cleaning memory scraps, not smart thinking.

4
⏳ View waiting time picture

More charts reveal time wasted idling for tiny jobs and online answers to arrive.

5
💡 Grasp the discoveries

You understand AI brains work far away, while your computer fusses inefficiently with starters and cleaners.

✅ Feel empowered

Now you see simple software fixes beat buying extra hardware, easing worries about shortages.

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Star Growth

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AI-Generated Review

What is agentic-cpu?

This repo delivers flame graph analysis—both on-CPU and off-CPU—for Codex CLI during terminal tasks on Linux, exposing where agentic AI CPU demand really goes. It profiles workloads like script generation and execution, showing most cycles burn on kernel memory cleanup and process exits rather than AI compute, with reproduction steps using perf and inferno. Developers get SVG visualizations and bash commands to profile their own codex cli install on Ubuntu or similar setups.

Why is it gaining traction?

It cuts through FOMO on agentic AI CPU needs with hard data: 50% of CPU in zap_pte_range page recycling, not LLM inference, plus off-CPU waits on subprocs and I/O via ppoll/epoll_wait. The hook is actionable insights for codex cli vs claude code debates, including system-wide sampling for concurrent agentic CPU workloads and tips like process pooling to slash overhead. No install needed—just clone, run perf commands, and generate graphs.

Who should use this?

DevOps engineers tuning agentic AI CPU for codex github integration or copilot-like tools in CI/CD. AI builders profiling codex cli skills on low-spec servers (2-core, 8GB RAM) before scaling. Linux admins debugging high CPU in codex exec --full-auto pipelines or github actions.

Verdict

Grab it if you're deep into codex cli github profiling—detailed docs and repro steps make it immediately useful despite 16 stars and 1.0% credibility score. Maturity is early (single README, no code/tests), but the analysis alone justifies a fork for your agentic CPU experiments.

(178 words)

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