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Quick Start

# Install
cargo install openentropy-cli

# Discover entropy sources on your machine
openentropy scan

# Benchmark all fast sources
openentropy bench

# Output 64 random hex bytes
openentropy stream --format hex --bytes 64

# Live TUI dashboard
openentropy monitor

By default, only fast sources (<2s) are used. Pass --all to include slower sources (DNS, TCP, GPU, BLE). The positional alias all is also accepted for compatibility.

External QRNG hardware

Most OpenEntropy sources come from the host machine itself. OpenEntropy also supports external hardware devices when they are attached and discoverable:

• Crypta Labs QCicada USB QRNG — exposed as the qcicada source. OpenEntropy can read the device's quantum output and benchmark, analyze, record, or compare it alongside on-host sources. The device's own output modes map to raw, sha256, and samples, and OpenEntropy now uses QCicada's fresh-start continuous-mode helper from qcicada 0.2.2, so it discards already-buffered device input once after entering continuous mode instead of serving the first read from stale queued bytes. See the QCicada manual.

Python

pip install openentropy

from openentropy import EntropyPool, detect_available_sources

sources = detect_available_sources()
print(f"{len(sources)} entropy sources available")

pool = EntropyPool.auto()
source = sources[0]["name"]
data = pool.get_source_bytes(source, 256, conditioning="sha256")

Build from source (native extension):

git clone https://github.com/amenti-labs/openentropy.git && cd openentropy
pip install maturin
maturin develop

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Two Audiences

Security engineers use OpenEntropy to validate entropy quality and seed CSPRNGs:

openentropy analyze --profile security --output audit.md

Researchers use OpenEntropy to study raw noise characteristics:

openentropy analyze --profile deep --output analysis.json

Security engineers seed CSPRNGs, generate keys, and supplement /dev/urandom with independent hardware entropy. The SHA-256 conditioned output (--conditioning sha256, the default) produces cryptographic-quality random bytes. The security profile enables the NIST test battery, min-entropy breakdown, and SHA-256 conditioning in one flag.

Researchers study the raw noise characteristics of hardware subsystems. Pass --conditioning raw to get unwhitened, unconditioned bytes that preserve the actual noise signal from each source. The deep profile enables 100K samples, cross-correlation, and PEAR-style trial analysis.

Raw mode enables:

• Hardware characterization — measure min-entropy, autocorrelation, and spectral properties of individual noise sources
• Silicon validation — compare noise profiles across chip revisions, thermal states, and voltage domains
• Anomaly detection — monitor entropy source health for signs of hardware degradation or tampering
• Cross-domain analysis — study correlations between independent entropy domains (thermal vs timing vs IPC)

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What Makes This Different

Most random number generators are pseudorandom — deterministic algorithms seeded once. OpenEntropy continuously harvests real physical noise from your hardware:

• Thermal noise — three independent oscillator beats (CPU crystal vs audio PLL, display PLL, PCIe PHY PLLs)
• Timing and microarchitecture — clock phase noise, DRAM row buffer conflicts, speculative execution variance, TLB shootdowns, DVFS races, ICC bus contention, prefetcher state, APRR JIT timing, ANE clock domain crossing
• I/O and IPC — disk and NVMe latency (including IOKit sensor polling, raw device, and Linux passthrough), USB enumeration, Mach port IPC, pipe buffer allocation, kqueue events, fsync journal
• GPU and compute — GPU warp divergence, IOSurface cross-domain timing, Neural Engine inference timing
• Scheduling and system — nanosleep drift, GCD dispatch queues, thread lifecycle, P/E-core migration, timer coalescing, kernel counters, process table snapshots
• Network and sensors — DNS resolution timing, TCP handshake variance, WiFi RSSI, BLE ambient RF, audio ADC noise
• Deep hardware — dual clock domain beats, SITVA, AES-XTS context switching, SEV broadcast, COMMPAGE seqlock, SMC thermistor, getentropy TRNG reseed

The pool XOR-combines independent streams. No single source failure can compromise the pool.

Conditioning Modes

Conditioning is optional and configurable. Use --conditioning on the CLI or ?conditioning= on the HTTP API:

Mode	Flag	Description
SHA-256 (default)	--conditioning sha256	SHA-256 conditioning. Cryptographic quality output.
Von Neumann	--conditioning vonneumann	Debiasing only — removes bias while preserving more of the raw signal structure.
Raw	--conditioning raw	No processing. Source bytes with zero whitening — preserves the actual hardware noise signal for research.

Raw mode is what makes OpenEntropy useful for research. Most HWRNG APIs run DRBG post-processing that makes every source look like uniform random bytes, destroying the information researchers need. Raw output preserves per-source noise structure: bias, autocorrelation, spectral features, and cross-source correlations. See Conditioning for details.

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Documentation

• Docs site: https://amenti-labs.github.io/openentropy/
• PyPI package: https://pypi.org/project/openentropy/