Metadata-Version: 2.4
Name: vaara
Version: 0.13.0
Summary: Adaptive AI Agent Execution Layer for risk scoring, audit trails, and regulatory compliance
License-Expression: Apache-2.0
Project-URL: Homepage, https://vaara.io
Project-URL: Repository, https://github.com/vaaraio/vaara
Project-URL: Issues, https://github.com/vaaraio/vaara/issues
Project-URL: PyPI, https://pypi.org/project/vaara/
Keywords: ai,ai-agents,ai-governance,agent-governance,ai-act,eu-ai-act,article-14,compliance,audit-trail,risk-scoring,conformal-prediction,overt,attestation,runtime-monitoring
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Security
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: ruff>=0.6.0; extra == "dev"
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Requires-Dist: mypy>=1.8; extra == "dev"
Provides-Extra: export
Requires-Dist: cryptography>=41.0; extra == "export"
Provides-Extra: ml
Requires-Dist: xgboost>=2.0; extra == "ml"
Requires-Dist: scikit-learn>=1.3; extra == "ml"
Requires-Dist: joblib>=1.3; extra == "ml"
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Provides-Extra: server
Requires-Dist: fastapi>=0.110; extra == "server"
Requires-Dist: uvicorn>=0.27; extra == "server"
Provides-Extra: attestation
Requires-Dist: cbor2>=5.4; extra == "attestation"
Requires-Dist: cryptography>=41.0; extra == "attestation"
Dynamic: license-file

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="docs/vaara-wordmark-dark.png">
    <img src="docs/vaara-wordmark-light.png" alt="Vaara" width="900">
  </picture>
</p>

<p align="center">
  <a href="https://pypi.org/project/vaara/"><img src="https://img.shields.io/pypi/v/vaara.svg" alt="PyPI"></a>
  <a href="https://pypi.org/project/vaara/"><img src="https://img.shields.io/pypi/pyversions/vaara.svg" alt="Python"></a>
  <a href="https://github.com/vaaraio/vaara/blob/main/LICENSE"><img src="https://img.shields.io/pypi/l/vaara.svg" alt="License"></a>
  <a href="https://github.com/vaaraio/vaara/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/vaaraio/vaara/ci.yml?branch=main&label=tests" alt="CI"></a>
  <a href="https://scorecard.dev/viewer/?uri=github.com/vaaraio/vaara"><img src="https://api.scorecard.dev/projects/github.com/vaaraio/vaara/badge" alt="OpenSSF Scorecard"></a>
  <a href="https://www.bestpractices.dev/projects/12612"><img src="https://www.bestpractices.dev/projects/12612/badge" alt="OpenSSF Best Practices"></a>
</p>

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](https://github.com/microsoft/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
- [vaara-bench-v1](bench/vaara-bench-v1.md): 77-trace synthetic-corpus benchmark with frozen methodology, 100% soft TPR, 0% hard FPR
- 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

```bash
pip install vaara
```

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

## Quick start

```python
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.

## HTTP API

The same scorer and audit trail are available over HTTP for non-Python agents and for control planes that prefer a network boundary. Install with the `server` extra:

```
pip install 'vaara[server]'
vaara serve --host 0.0.0.0 --port 8000
```

```
curl -sX POST http://localhost:8000/v1/score \
  -H 'content-type: application/json' \
  -d '{"tool_name":"tx.transfer","agent_id":"agent-007","base_risk_score":0.5}'
```

The contract is in [docs/openapi.yaml](docs/openapi.yaml). Vaara defines the interface. Control-plane and orchestration vendors call it. Integration recipes for adopters live under `examples/recipes/`.

v0.13.0 adds three operator-facing endpoints. `POST /v1/policy/reload` atomically swaps the running policy without restarting the agent process (start with `vaara serve --policy PATH` to enable; in-flight requests keep the old thresholds, the next request sees the new ones). `POST /v1/detect/injection` and `POST /v1/detect/pii` expose Vaara's adversarial scorer and a zero-dependency PII extractor as named buyer-visible endpoints; the corresponding `vaara detect injection` and `vaara detect pii` CLI subcommands exit non-zero when the detector fires, so they slot into CI gates. `vaara compliance dashboard --db PATH --out site/` renders a single-file static HTML article-coverage page from the same evidence model as `vaara compliance report`.

## OVERT 1.0 attestation

Vaara implements the OVERT 1.0 ([overt.is](https://overt.is/)) Protocol Profile 1.0 Base Envelope. OVERT 1.0 is an open standard for runtime trust in AI systems, authored by Glacis Technologies and published 25 March 2026. Closed-schema 9-field structure at AAL-3 Phase 2 (Provisional Receipt), canonical CBOR (RFC 8949), Ed25519 signatures, HMAC-SHA256 keyed commitments, IEEE-754 float rejection. v0.13.0 adds a reference Phase 3 IAP (`vaara.attestation.iap`) that notary-signs the Provisional Receipt and anchors it in a transparency log; production deployments can swap in sigstore Rekor or an equivalent independently-operated log at the same call sites.

```
pip install 'vaara[attestation]'
```

```python
from vaara.attestation.overt import emit_base_envelope, make_request_commitment, encoder_binary_identity

envelope = emit_base_envelope(
    signing_key=key,
    request_commitment=make_request_commitment(payload, operator_key=op_key),
    encoder_binary_identity=encoder_binary_identity(arbiter_version="vaara/0.13.0", policy_hash=ph),
    non_content_metadata={"action_class": "tx.transfer", "decision": "escalate"},
    monotonic_counter=42,
    arbiter_instance_identifier=uuid_bytes,
)
```

Vaara operates as the **Arbiter** in OVERT terms. See [COMPLIANCE.md](COMPLIANCE.md) "Position relative to open runtime-attestation standards" for the architectural framing.

v0.12.0 adds an OVERT S3P (MEA-2) emitter with exact Clopper-Pearson confidence intervals (pure Python, no scipy), plus a proposed Protocol Profile extension that reports aggregate statistics over Vaara's per-action conformal prediction intervals alongside the standard binomial CI. The agentic-controls mapping in [COMPLIANCE.md](COMPLIANCE.md) "OVERT 1.0 Part 3 (Agentic AI Controls) mapping" walks Vaara's coverage of TOOL-*, MCP-*, MULTI-*, CAP-*, DISC-*, HITL-*, and DRIFT-* control by control.

```python
from vaara.attestation.s3p import emit_s3p_attestation, ConformalExtension, make_epoch_nonce_commitment
```

## Where things live

- [docs/formal_specification.md](docs/formal_specification.md): math. MWU regret bound O(sqrt(T log N)), conformal coverage guarantees, security properties.
- [COMPLIANCE.md](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](https://futurium.ec.europa.eu/ga/apply-ai-alliance/community-content/article-14-runtime-why-oversight-agentic-ai-has-be-evidenced-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/policy/`: declarative YAML / JSON policy schema with `vaara policy validate` (semantic checks) and `vaara policy test` (Conftest-style cases-file runner) for reviewing the policy artifact in CI independently from agent code.
- `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](LICENSE)
