Skip to main content

Adaptive AI Agent Execution Layer for risk scoring, audit trails, and regulatory compliance

Project description

Vaara

PyPI Python License CI OpenSSF Scorecard OpenSSF Best Practices

Vaara intercepts agent tool calls, scores each one with a conformal risk interval, and writes a hash-chained audit record. Online learning across five expert signals via Multiplicative Weight Update. Distribution-free conformal coverage on the score.

For broader agent governance (zero-trust identity, capability-based access control, multi-language SDKs) see Microsoft's Agent Governance Toolkit.

Numbers

  • 5,955-entry adversarial corpus (3,422 attack across 8 categories, 2,533 benign)
  • 97.1% attack recall on held-out distribution-shift split, threshold 0.55
  • PAIR adaptive-attacker calibration: ASR 0/25 against Qwen2.5-32B
  • 140 µs / 210 µs p99 inference latency, commodity CPU
  • Distribution-free conformal coverage on the score
  • MWU regret bound O(sqrt(T log N))

Install

pip install vaara

Python 3.10+. Zero runtime deps. Optional XGBoost classifier: pip install vaara[ml].

Quick start

from vaara.pipeline import InterceptionPipeline

pipeline = InterceptionPipeline()
result = pipeline.intercept(
    agent_id="agent-007",
    tool_name="fs.write_file",
    parameters={"path": "/etc/service.yaml", "content": "..."},
    agent_confidence=0.8,
)
if result.allowed:
    pipeline.report_outcome(result.action_id, outcome_severity=0.0)
else:
    print(result.reason)

report_outcome closes the loop. MWU reweights signals based on which ones predicted the outcome.

Where things live

  • docs/formal_specification.md: math. MWU regret bound O(sqrt(T log N)), conformal coverage guarantees, security properties.
  • COMPLIANCE.md: Article-level evidence mapping for EU AI Act (Articles 9, 11 to 15, 61) and DORA (Articles 10, 12, 13). Eval numbers, threshold sweeps, PAIR adversarial calibration.
  • Article 14 runtime: why oversight of agentic AI has to be evidenced as action, not model: why this exists. Posted on the EU Apply AI Alliance Futurium.
  • src/vaara/integrations/: LangChain, OpenAI Agents SDK, CrewAI, MCP server.
  • src/vaara/audit/: hash-chain trail, SQLite backend, append-only WAL.
  • src/vaara/sandbox/: synthetic-trace cold-start calibration.

Vaara helps deployers assemble evidence for their own conformity work. It does not certify compliance or constitute legal advice. Deployers own their obligations under the EU AI Act and other applicable law.

License

LICENSE

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vaara-0.8.0.tar.gz (384.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

vaara-0.8.0-py3-none-any.whl (349.1 kB view details)

Uploaded Python 3

File details

Details for the file vaara-0.8.0.tar.gz.

File metadata

  • Download URL: vaara-0.8.0.tar.gz
  • Upload date:
  • Size: 384.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for vaara-0.8.0.tar.gz
Algorithm Hash digest
SHA256 2de52ac24e63d5c86cacb542a0535c72af6e537d661089d70f1c527046a66765
MD5 b743fc4c125ed2b93f842c67088f17c0
BLAKE2b-256 b9095b129b8d6371f6508472a0b2d036b82a7635e830bfa7b2e56e3ca4911c78

See more details on using hashes here.

Provenance

The following attestation bundles were made for vaara-0.8.0.tar.gz:

Publisher: release.yml on vaaraio/vaara

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file vaara-0.8.0-py3-none-any.whl.

File metadata

  • Download URL: vaara-0.8.0-py3-none-any.whl
  • Upload date:
  • Size: 349.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for vaara-0.8.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3392ebbf514556c08babfa995b782bf8e81fcd331ad496f13e36428c1812f5e6
MD5 6ef3ec025d668873ad5d3dd90e2aefff
BLAKE2b-256 fe946efc6aeef8688ccfebda8b5128fbbd9dae50c759ded7e6c549ed6eded5f3

See more details on using hashes here.

Provenance

The following attestation bundles were made for vaara-0.8.0-py3-none-any.whl:

Publisher: release.yml on vaaraio/vaara

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page