Benchmarking

This document describes how to run, interpret, and compare sandbox performance benchmarks for Greywall.

Quick Start

# Install dependencies
brew install hyperfine   # macOS
# apt install hyperfine  # Linux

go install golang.org/x/perf/cmd/benchstat@latest

# Run CLI benchmarks
./scripts/benchmark.sh

# Run Go microbenchmarks
go test -run=^$ -bench=. -benchmem ./internal/sandbox/...

Goals

1. Quantify sandbox overhead on each platform (sandboxed / unsandboxed ratio)
2. Compare macOS (Seatbelt) vs Linux (bwrap+Landlock) overhead fairly
3. Attribute overhead to specific components (proxy startup, bridge setup, wrap generation)
4. Track regressions over time

Benchmark Types

Layer 1: CLI Benchmarks (scripts/benchmark.sh)

What it measures: Real-world agent cost - full greywall invocation including proxy startup, socat bridges (Linux), and sandbox-exec/bwrap setup.

This is the most realistic benchmark for understanding the cost of running agent commands through Greywall.

# Full benchmark suite
./scripts/benchmark.sh

# Quick mode (fewer runs)
./scripts/benchmark.sh -q

# Custom output directory
./scripts/benchmark.sh -o ./my-results

# Include network benchmarks (requires local server)
./scripts/benchmark.sh --network

Options

Option	Description
-b, --binary PATH	Path to greywall binary (default: ./greywall)
-o, --output DIR	Output directory (default: ./benchmarks)
-n, --runs N	Minimum runs per benchmark (default: 30)
-q, --quick	Quick mode: fewer runs, skip slow benchmarks
--network	Include network benchmarks

Layer 2: Go Microbenchmarks (internal/sandbox/benchmark_test.go)

What it measures: Component-level overhead - isolates Manager initialization, WrapCommand generation, and execution.

# Run all benchmarks
go test -run=^$ -bench=. -benchmem ./internal/sandbox/...

# Run specific benchmark
go test -run=^$ -bench=BenchmarkWarmSandbox -benchmem ./internal/sandbox/...

# Multiple runs for statistical analysis
go test -run=^$ -bench=. -benchmem -count=10 ./internal/sandbox/... > bench.txt
benchstat bench.txt

Available Benchmarks

Benchmark	Description
BenchmarkBaseline_*	Unsandboxed command execution
BenchmarkManagerInitialize	Cold initialization (proxies + bridges)
BenchmarkWrapCommand	Command string construction only
BenchmarkColdSandbox_*	Full init + wrap + exec per iteration
BenchmarkWarmSandbox_*	Pre-initialized manager, just exec
BenchmarkOverhead	Grouped comparison of baseline vs sandbox

Layer 3: OS-Level Profiling

What it measures: Kernel/system overhead - context switches, syscalls, page faults.

Linux

# Quick syscall cost breakdown
strace -f -c ./greywall -- true

# Context switches, page faults
perf stat -- ./greywall -- true

# Full profiling (flamegraph-ready)
perf record -F 99 -g -- ./greywall -- git status
perf report

macOS

# Time Profiler via Instruments
xcrun xctrace record --template 'Time Profiler' --launch -- ./greywall -- true

# Quick call-stack snapshot
./greywall -- sleep 5 &
sample $! 5 -file sample.txt

Interpreting Results

Key Metric: Overhead Factor

Overhead Factor = time(sandboxed) / time(unsandboxed)

Compare overhead factors across platforms, not absolute times, because hardware differences swamp absolute timings.

Example Output

Benchmark                      Unsandboxed    Sandboxed    Overhead
true                           1.2 ms         45 ms        37.5x
git status                     15 ms          62 ms        4.1x
python -c 'pass'               25 ms          73 ms        2.9x

What to Expect

Workload	Linux Overhead	macOS Overhead	Notes
true	180-360x	8-10x	Dominated by cold start
echo	150-300x	6-8x	Similar to true
python3 -c 'pass'	10-12x	2-3x	Interpreter startup dominates
git status	50-60x	4-5x	Real I/O helps amortize
rg	40-50x	3-4x	Search I/O helps amortize

The overhead factor decreases as the actual workload increases (because sandbox setup is fixed cost). Linux overhead is significantly higher due to bwrap/socat setup.

Cross-Platform Comparison

Fair Comparison Approach

1. Run benchmarks on each platform independently
2. Compare overhead factors, not absolute times
3. Use the same greywall version and workloads

# On macOS
go test -run=^$ -bench=. -count=10 ./internal/sandbox/... > bench_macos.txt

# On Linux
go test -run=^$ -bench=. -count=10 ./internal/sandbox/... > bench_linux.txt

# Compare
benchstat bench_macos.txt bench_linux.txt

Caveats

• macOS uses Seatbelt (sandbox-exec) - built-in, lightweight kernel sandbox
• Linux uses bwrap + Landlock, this creates socat bridges for network, incurring significant setup cost
• Linux cold start is ~10x slower than macOS due to bwrap/socat bridge setup
• Linux warm path is still ~5x slower than macOS - bwrap execution itself has overhead
• For long-running agents, this difference is negligible (one-time startup cost)

[!TIP] Running Linux benchmarks inside a VM (Colima, Docker Desktop, etc.) inflates overhead due to virtualization. Use native Linux (bare metal or CI) for fair cross-platform comparison.

GitHub Actions

Benchmarks can be run in CI via the workflow at .github/workflows/benchmark.yml:

# Trigger manually from GitHub UI: Actions > Benchmarks > Run workflow

# Or via gh CLI
gh workflow run benchmark.yml

Results are uploaded as artifacts and summarized in the workflow summary.

Tips

Reducing Variance

• Run with --min-runs 50 or higher
• Close other applications
• Pin CPU frequency if possible (Linux: cpupower frequency-set --governor performance)
• Run multiple times and use benchstat for statistical analysis

Profiling Hotspots

# CPU profile
go test -run=^$ -bench=BenchmarkWarmSandbox -cpuprofile=cpu.out ./internal/sandbox/...
go tool pprof -http=:8080 cpu.out

# Memory profile
go test -run=^$ -bench=BenchmarkWarmSandbox -memprofile=mem.out ./internal/sandbox/...
go tool pprof -http=:8080 mem.out

Tracking Regressions

1. Run benchmarks before and after changes
2. Save results to files
3. Compare with benchstat

# Before
go test -run=^$ -bench=. -count=10 ./internal/sandbox/... > before.txt

# Make changes...

# After
go test -run=^$ -bench=. -count=10 ./internal/sandbox/... > after.txt

# Compare
benchstat before.txt after.txt

Workload Categories

Category	Commands	What it Stresses
Spawn-only	true, echo	Process spawn, wrapper overhead
Interpreter	python3 -c, node -e	Runtime startup under sandbox
FS-heavy	file creation, rg	Landlock/Seatbelt FS rules
Network (local)	curl localhost	Proxy forwarding overhead
Real tools	git status	Practical agent workloads

Benchmark Findings (12/28/2025)

Results from GitHub Actions CI runners (Linux: AMD EPYC 7763, macOS: Apple M1 Virtual).

Manager Initialization

Platform	Manager.Initialize()
Linux	101.9 ms
macOS	27.5 µs

Linux initialization is ~3,700x slower because it must:

• Start HTTP + SOCKS proxies
• Create Unix socket bridges for socat
• Set up bwrap namespace configuration

macOS only generates a Seatbelt profile string (very cheap).

Cold Start Overhead (one greywall invocation per command)

Workload	Linux	macOS
true	215 ms	22 ms
Python	124 ms	33 ms
Git status	114 ms	25 ms

This is the realistic cost for scripts running greywall -c "command" repeatedly.

Warm Path Overhead (pre-initialized manager)

Workload	Linux	macOS
true	112 ms	20 ms
Python	124 ms	33 ms
Git status	114 ms	25 ms

Even with proxies already running, Linux bwrap execution adds ~110ms overhead per command.

Overhead Factors

Workload	Linux Overhead	macOS Overhead
true (cold)	~360x	~10x
true (warm)	~187x	~8x
Python (warm)	~11x	~2x
Git status (warm)	~54x	~4x

Overhead decreases as the actual workload increases (sandbox setup is fixed cost).

Impact on Agent Usage

Long-Running Agents (greywall claude, greywall codex)

For agents that run as a child process under greywall:

Phase	Cost
Startup (once)	Linux: ~215ms, macOS: ~22ms
Per tool call	Negligible (baseline fork+exec only)

Child processes inherit the sandbox - no re-initialization, no WrapCommand overhead. The per-command cost is just normal process spawning:

Command	Linux	macOS
true	0.6 ms	2.3 ms
git status	2.1 ms	5.9 ms
Python script	11 ms	15 ms

Bottom line: For greywall <agent> usage, sandbox overhead is a one-time startup cost. Tool calls inside the agent run at native speed.

Per-Command Invocation (greywall -c "command")

For scripts or CI running greywall per command:

Session	Linux Cost	macOS Cost
1 command	215 ms	22 ms
10 commands	2.15 s	220 ms
50 commands	10.75 s	1.1 s

Consider keeping the manager alive (daemon mode) or batching commands to reduce overhead.

Additional Notes

• Manager.Initialize() starts HTTP + SOCKS proxies; on Linux also creates socat bridges
• Cold start includes all initialization; hot path is just WrapCommand + exec
• -m (monitor mode) spawns additional monitoring processes, so we'll have to benchmark separately
• Keep workloads under the repo - avoid /tmp since Linux bwrap does --tmpfs /tmp
• debug mode changes logging, so always benchmark with debug off