macmon – Mac Monitor

macmon is a sudoless performance monitor for Apple Silicon Macs. It reads real-time CPU / GPU / ANE power usage, temperatures, and memory stats through a private macOS API — the same data powermetrics exposes — without requiring root access.

• 🚫 Runs without sudo
• ⚡ Real-time CPU / GPU / ANE power usage
• 📊 CPU frequency-scaled and active ratios per cluster
• 💾 RAM / Swap usage
• 📈 Historical charts with average and max values
• 🌡️ Average CPU / GPU temperature
• 📋 Process list with per-process CPU, memory, power and GPU usage
• 🎨 Follows your terminal's color scheme
• 🪟 Can be displayed in a small window
• 🦀 Written in Rust

Install macmon using brew:

brew install macmon

sudo port install macmon

cargo install macmon

nix-env -i macmon

Usage: macmon [OPTIONS] [COMMAND]

Commands:
  pipe    Output metrics in JSON format
  serve   Serve metrics over HTTP
  debug   Print debug information
  stress  Generate load for testing metrics
  help    Print this message or the help of the given subcommand(s)

Options:
  -i, --interval <INTERVAL>  Update interval in milliseconds [default: 1000]
  -h, --help                 Print help
  -V, --version              Print version

Run macmon without a subcommand to open the terminal UI: the metric boxes on top and the process list below. Press ? in the app to see the keys.

Press / to filter the process list. A filter is a comma-separated list of terms, each matched case-insensitively against the process name or pid: safari, cargo shows processes matching any term, and a term starting with ! hides its matches (chrome, !helper). Enter keeps the filter, Esc clears it.

You can use the pipe subcommand to output metrics in JSON format, which makes it suitable for piping into other tools or scripts. For example:

macmon pipe | jq

This command runs macmon in "pipe" mode and sends the output to jq for pretty-printing.

You can also specify the number of samples to collect using the -s or --samples parameter (default: 0, which runs indefinitely), and set the update interval in milliseconds using the -i or --interval parameter (default: 1000 ms). For example:

macmon pipe -s 10 -i 500 | jq

This will collect 10 samples with an update interval of 500 milliseconds.

You can use the serve subcommand to expose metrics over HTTP. This is useful for integrating with monitoring systems like Prometheus and Grafana.

macmon serve                   # default port 9090, interval 1000ms
macmon serve --host 127.0.0.1  # listen on localhost only
macmon serve -p 8080           # custom port
macmon serve -i 500            # sampling interval 500ms
macmon serve &                 # run in background

Two endpoints are available:

To start macmon serve automatically on login and keep it running:

macmon serve --install                   # install and start (default port 9090)
macmon serve --port 8080 --install       # with custom port
macmon serve --host 127.0.0.1 --install  # listen on localhost only
macmon serve --uninstall                 # stop and remove

This creates a launchd agent at ~/Library/LaunchAgents/com.macmon.plist that auto-starts on login and restarts on crash.

The /metrics endpoint exposes metrics in Prometheus format. See examples/grafana for a local demo stack with Prometheus and Grafana.

# HELP macmon_cpu_temp_celsius Average CPU temperature in Celsius
# TYPE macmon_cpu_temp_celsius gauge
macmon_cpu_temp_celsius{chip="Apple M3 Pro"} 47.3

# HELP macmon_cpu_power_watts CPU power consumption in Watts
# TYPE macmon_cpu_power_watts gauge
macmon_cpu_power_watts{chip="Apple M3 Pro"} 8.42

# HELP macmon_fan_speed_rpm Fan speed in revolutions per minute
# TYPE macmon_fan_speed_rpm gauge
macmon_fan_speed_rpm{chip="Apple M3 Pro",fan="fan0"} 1234

# HELP macmon_cpu_scaled_ratio Combined frequency-scaled CPU ratio (0–1), weighted by core count
# TYPE macmon_cpu_scaled_ratio gauge
macmon_cpu_scaled_ratio{chip="Apple M3 Pro"} 0.037

# HELP macmon_cpu_active_ratio Combined CPU active residency ratio (not frequency-scaled, 0–1), weighted by core count
# TYPE macmon_cpu_active_ratio gauge
macmon_cpu_active_ratio{chip="Apple M3 Pro"} 0.092

# HELP macmon_cpu_tier_freq_mhz CPU tier frequency in MHz
# TYPE macmon_cpu_tier_freq_mhz gauge
macmon_cpu_tier_freq_mhz{chip="Apple M3 Pro",tier="E"} 1100
macmon_cpu_tier_freq_mhz{chip="Apple M3 Pro",tier="P"} 1800

# HELP macmon_cpu_tier_scaled_ratio CPU tier frequency-scaled ratio (0–1)
# TYPE macmon_cpu_tier_scaled_ratio gauge
macmon_cpu_tier_scaled_ratio{chip="Apple M3 Pro",tier="E"} 0.083
macmon_cpu_tier_scaled_ratio{chip="Apple M3 Pro",tier="P"} 0.015

# HELP macmon_cpu_tier_active_ratio CPU tier active residency ratio (not frequency-scaled, 0–1)
# TYPE macmon_cpu_tier_active_ratio gauge
macmon_cpu_tier_active_ratio{chip="Apple M3 Pro",tier="E"} 0.18
macmon_cpu_tier_active_ratio{chip="Apple M3 Pro",tier="P"} 0.04

Use macmon stress to generate load while checking metric behavior:

macmon stress
macmon stress pulse --duration 30
macmon stress cpu --duration 30
macmon stress cpu --workers 8 --duration 30
macmon stress gpu --duration 30
macmon stress all --duration 30

The default pulse mode generates a predictable cyclic CPU load with a fixed 50% duty cycle on half of the logical CPUs. The cpu and gpu modes continuously load only the selected processor, while all loads both. The cpu and all modes use all logical CPUs unless --workers is specified; --workers has no effect in gpu mode.

macOS 27 broke the CPU/ANE power source that macmon previously used without root. The IOReport Energy Model counters can remain visible but stop updating: refreshing them now requires the private com.apple.private.pmgr.nrg.reporting entitlement. Apple's signed powermetrics has this entitlement and can keep using the named channels. Running macmon with sudo does not grant the same access.

The working rootless workaround we found is to read hidden energy reports from the AppleCLPC driver through IOReport. These reports use opaque IDs instead of discoverable CPU/GPU/ANE channel names, so macmon has to identify the counters and verify their units. This leaves macmon maintaining undocumented mappings for data that Apple's own tool can still read directly.

Both the CLI and the Rust library use known CLPC counters automatically, and Sampler::new() retains legacy fallbacks for older systems. New chips or driver versions may need additional mappings. On an unsupported configuration, zero CPU/ANE readings can mean unavailable counters rather than zero consumption. See docs/clpc-discovery.md for details.

The pipe command and the HTTP /json endpoint return the same metrics:

{
  "timestamp": "2025-02-24T20:38:15.427569+00:00",
  "temp": {
    "cpu_temp_avg": 43.73614, // Celsius, null when no sensor has a valid reading
    "gpu_temp_avg": 36.95167, // Celsius, null when no sensor has a valid reading
  },
  "memory": {
    "ram_total": 25769803776, // Bytes
    "ram_usage": 20985479168, // Bytes
    "swap_total": 4294967296, // Bytes
    "swap_usage": 2602434560, // Bytes
  },
  "fans": [
    { "name": "fan0", "rpm": 999, "max_rpm": 4900 },
    { "name": "fan1", "rpm": 1200, "max_rpm": 5200 },
  ],
  "cpu_scaled_ratio": 0.036854, // Combined frequency-scaled CPU ratio (weighted by core count, 0–1)
  "cpu_active_ratio": 0.092, // Combined active residency ratio (not frequency-scaled, weighted by core count, 0–1)
  "cpu_tiers": [ // CPU core types from the lowest to the highest: E, P on M1–M4; E, S on M5; P, S on M5 Pro/Max; E, P, S on M6
    {
      "label": "E",
      "freq_mhz": 1100, // Tier frequency
      "scaled_ratio": 0.082656614, // Frequency-scaled ratio (0–1)
      "active_ratio": 0.18, // Active residency (not frequency-scaled, 0–1)
      "cores": [
        { "die_id": 0, "core_id": 0, "freq_mhz": 1600, "scaled_ratio": 0.14, "active_ratio": 0.24 },
        { "die_id": 0, "core_id": 1, "freq_mhz": 1700, "scaled_ratio": 0.12, "active_ratio": 0.2 },
      ],
    },
    {
      "label": "P",
      "freq_mhz": 1800,
      "scaled_ratio": 0.015181795,
      "active_ratio": 0.04,
      "cores": [
        { "die_id": 0, "core_id": 0, "freq_mhz": 2100, "scaled_ratio": 0.05, "active_ratio": 0.08 },
        { "die_id": 0, "core_id": 1, "freq_mhz": 2200, "scaled_ratio": 0.07, "active_ratio": 0.06 },
      ],
    },
  ],
  // Deprecated, use cpu_tiers: ecpu_* is the lowest tier, pcpu_* the highest
  "ecpu_freq_mhz": 1100,
  "ecpu_scaled_ratio": 0.082656614,
  "ecpu_active_ratio": 0.18,
  "pcpu_freq_mhz": 1800,
  "pcpu_scaled_ratio": 0.015181795,
  "pcpu_active_ratio": 0.04,
  "ecpu_cores": [/* cores of the first tier */],
  "pcpu_cores": [/* cores of the last tier */],
  "gpu_freq_mhz": 461, // GPU frequency
  "gpu_scaled_ratio": 0.021497859, // Frequency-scaled ratio (0–1)
  "gpu_active_ratio": 0.09, // GPU active residency ratio (not frequency-scaled, 0–1)
  "cpu_power": 0.20486385, // Watts
  "gpu_power": 0.017451683, // Watts
  "ane_power": 0.0, // Watts
  "all_power": 0.22231553, // Watts
  "sys_power": 5.876533, // Watts
  "ram_power": 0.11635789, // Watts
  "gpu_ram_power": 0.0009615385, // GPU SRAM power, Watts
}

active_ratio is the share of the sampling interval during which the processor was doing any work. scaled_ratio is the same measure adjusted for operating frequency, showing the share of the processor's maximum possible capacity that was used.

For interval $T$, $t_i$ is the time spent doing work at frequency $f_i$, and $f_\text{max}$ is the hardware maximum frequency for the CPU cluster or GPU. The product $T f_\text{max}$ is the theoretical maximum frequency-time the processor could deliver at full load without thermal or power throttling:

CPU ratios are calculated per core and averaged across the cluster. A core that did no work contributes zero; combined CPU ratios average all cores from all clusters:

Examples:

• Full interval at half the maximum frequency → active = 1.0, scaled = 0.5.
• Half the interval at the maximum frequency → active = 0.5, scaled = 0.5.
• One of ten cores working at maximum frequency → cluster active = scaled = 0.1.

macmon can be used as a Rust library to collect Apple Silicon metrics in your own applications.

Add it to your project:

cargo add macmon --no-default-features

The default app feature enables the macmon executable and its terminal UI dependencies. Disable default features when using macmon only as a library.

Run the standalone demo app:

cargo run --manifest-path examples/demo-app/Cargo.toml

Then use the Sampler to collect metrics:

use macmon::Sampler;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut sampler = Sampler::new()?;

    // collect metrics over a 1000ms window
    let metrics = sampler.get_metrics(1000)?;

    println!("CPU power:  {:.2} W", metrics.cpu_power);
    println!("GPU power:  {:.2} W", metrics.gpu_power);
    match metrics.temp.cpu_temp_avg {
        Some(value) => println!("CPU temp:   {value:.1} °C"),
        None => println!("CPU temp:   N/A"),
    }
    println!("RAM usage:  {} / {} bytes", metrics.memory.ram_usage, metrics.memory.ram_total);
    for tier in &metrics.cpu_tiers {
        println!("{}-CPU:      {} MHz  {:.1}%", tier.label, tier.freq_mhz, tier.scaled_ratio * 100.0);
    }

    Ok(())
}

get_metrics(duration_ms) blocks the calling thread while collecting one IOReport delta over the complete interval. For a UI, server, or async application, create the sampler inside a dedicated worker thread and send the completed metrics back through a channel:

use std::{sync::mpsc, thread};

use macmon::Sampler;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let (tx, rx) = mpsc::channel();

    thread::spawn(move || {
        let mut sampler = Sampler::new().expect("failed to create sampler");

        while let Ok(metrics) = sampler.get_metrics(1000) {
            if tx.send(metrics).is_err() {
                break;
            }
        }
    });

    // Use recv() in a consumer thread or try_recv() in a non-blocking event loop.
    let metrics = rx.recv()?;
    println!("CPU power: {:.2} W", metrics.cpu_power);

    Ok(())
}

Creating Sampler inside the worker keeps its low-level macOS handles on that thread. In an async runtime, use its blocking-thread facility rather than calling get_metrics directly from an executor worker.

All contributions are welcome! Feel free to open an issue or submit a pull request.

Distributed under the MIT License.

• tlkh/asitop – The original tool. Written in Python, requires sudo.
• dehydratedpotato/socpowerbud – Written in Objective-C, sudoless, no TUI.
• op06072/NeoAsitop – Written in Swift, sudoless.
• graelo/pumas – Written in Rust, requires sudo.
• context-labs/mactop – Written in Go, requires sudo.