AgentGuard v1.0.0
inter-agent-guard v1.0.0 — Release Notes
Released: 2026-07-15
PyPI: inter-agent-guard · Import: agentguard
Repo: https://github.com/nizba06/agentguard
Highlights
- Holdout gates cleared (20% Anthropic carve-out, 160 adversarial + 1,000 benign):
- Detection 99.4%, FPR 0.0%
- ML layer catches ~95% of content attacks; rules catch the rest of detected cases
- Default ONNX artifact is dynamic INT8 (~164 MB, SHA-256 pinned)
- Ship tooling:
scripts/check_v1_gates.py,scripts/ship_v1.ps1,run_benchmark_evaluation.ps1 -Holdout - Deterministic layers (capability / impersonation / trust) unchanged and production-ready
Install
pip install "inter-agent-guard==1.0.0"
# Download risk_scorer.onnx + model.sha256 from GitHub Releases into agentguard/models/
python scripts/verify_model.py # from a checkout, or use install_model helpersfrom agentguard import AgentGuard, CapabilityManifest
guard = AgentGuard(
risk_threshold=0.85,
require_ml_model=True,
task_objective="Analyse Q3 competitor pricing",
)ML model (required for enforce-mode scoring)
| Artifact | Notes |
|---|---|
risk_scorer.onnx |
Dynamic INT8 (~164 MB); SHA-256 1f758299994793a033153c27a033eb998c5e37090d51da3ce0af1c4807fe6894 |
tokenizer.json / tokenizer_config.json |
Bundled in the wheel under agentguard/models/ |
Latency SLA (honest)
CPU ONNX P95 on holdout is ~3 s, not the original 15 ms design target. For high-QPS production:
- Rules-only / capability / trust (no ONNX)
- GPU ONNX Execution Provider
- Async / monitor modes — see
docs/source/latency.md
v1.0 explicitly documents this CPU exception; do not assume sub-15 ms on CPU with ML enabled.
Benchmark authority
Holdout (benchmarks/results/holdout_report.md) is the ship gate. Full-corpus scores can overstate quality when the scorer trained on that JSONL.
Dataset: https://huggingface.co/datasets/Nizba/agentguard-benchmark-v1
Verify gates locally
py -3.12 scripts/verify_model.py
.\scripts\run_benchmark_evaluation.ps1 -Holdout -RequireModel
py -3.12 scripts/check_v1_gates.py --allow-cpu-latency