AI Process Monitoring
CPU, memory, PID. Per tab, per agent. No more guessing which AI ate your RAM.
Questions this answers
- How much CPU does Claude Code use?
- Monitor AI agent memory usage in terminal
- Which AI coding tool is using the most resources?
- How to track process stats for AI CLI tools?
How it works
Chau7 ties process-level metrics directly to the AI agent detected in each tab. Once an agent is identified, Chau7 tracks its PID along with CPU and resident memory usage. These metrics are sampled periodically and surfaced in the tab status panel accessible via the MCP server or the UI.
Because monitoring is scoped per tab, you can compare resource usage across agents side by side. A Claude Code session in one tab and a Codex session in another each report their own independent metrics without cross-contamination.
The data feeds into the broader telemetry system, so you can correlate resource spikes with specific AI operations, prompts, or tool-call bursts after the fact.
Why it matters
AI coding agents are quietly hungry. They consume CPU during tool calls, gobble memory during long sessions, and Activity Monitor shows you a wall of node and python processes with no indication of which tab or agent owns them. Chau7 gives you per-tab, per-agent resource visibility so you can spot the runaway session before your fans hit jet-engine mode.
Frequently asked questions
Does process monitoring add significant overhead?
No. Chau7 reads standard system metrics on a throttled interval. The overhead is comparable to running a lightweight top query and is imperceptible during normal use.
Can I see historical resource usage for a session?
Process metrics are captured as part of the telemetry run data. You can query past sessions through the MCP server to review resource consumption over time.