hlinor-registry 0.4.2

Governance and security layer for auditable AI agent systems.

Hlinor Agent Registry

Latest: The OpenAI/Hugging Face Sandbox Escape: Why Declarative AI Governance is No Longer Optional - Dev.to article

Open-source registry layer for auditable AI agent systems. Define what your AI agents may do, validate it before execution, and keep the decision auditable — without replacing the framework that runs your agents.

Hlinor Agent Registry is a declarative governance layer for agent systems. It turns action boundaries, policies, approvals, and runtime evidence into reviewable YAML contracts that developers and security teams can understand.

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⚡ Quickstart (Zero Friction)

Get up and running in 3 simple steps:

1. Install

pip install hlinor-registry

2. Initialize Templates

Generate a ready-to-use registry manifest and agent policy file with safe defaults:

hlinor-registry init

(This creates registry.yaml and my_agent.yaml in your current directory)

3. Compile and Test

Compile your policies into an integrity-checked JSON bundle:

hlinor-registry compile --manifest registry.yaml --output bundle.json

New manifests should declare schema_version, metadata.environment, metadata.bundle_revision, and metadata.policy_revision. The legacy top-level version field remains accepted for 0.4.x migration compatibility.

Test the governance enforcement directly from the CLI:

# Test an allowed action
hlinor-registry check --bundle bundle.json --agent my-agent --action read_database

# Test a blocked action (Fail-closed in action)
hlinor-registry check --bundle bundle.json --agent my-agent --action send_external_email

For an auditable machine-readable decision, emit JSONL and optionally append the same provenance-aware event to a durable log file:

hlinor-registry check \
  --bundle bundle.json \
  --agent my-agent \
  --action read_database \
  --format jsonl \
  --audit-log logs/governance-decisions.jsonl

Each event includes the decision ID, timestamp, reason code, and SHA-256 digest of the policy bundle used to make the decision. It also binds the decision to a canonical request digest.

For context-rich evaluation, use the immutable request API:

from hlinor_registry import ActionRequest, PolicyChecker

request = ActionRequest(
    agent_id="financial-audit-agent",
    action="read",
    actor_id="service:finance-prod",
    resource="report:quarterly",
    attributes={"classification": "confidential"},
    environment="production",
)
decision = PolicyChecker("bundle.json").evaluate(request)

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🛡️ Use cases

Prevent PII leaks

Keep agents that process sensitive data away from external communication and make the restriction explicit in a reviewed registry file:

id: financial-audit-agent
name: Financial Audit Agent
department: finance
description: Audits internal financial reports.
skills: [read_database, anomaly_detection, generate_report]
validators: [financial-data-validator]
policies: [no-pii-in-logs, read-only-database-access]
allowed_actions: [read, analyze, summarize, generate_pdf_report]
blocked_actions: [send_external_email, delete_records]

The blocklist takes priority over the allowlist:

from hlinor_registry import PolicyChecker

checker = PolicyChecker("bundle.json")
decision = checker.check_action("financial-audit-agent", "send_external_email")

assert decision.denied
# decision.reason_code: ACTION_BLOCKLISTED
# decision.matched_policy_ids: ("no_pii_in_logs",)

Block unauthorized actions

Use a strict allowlist for agents that should only perform a narrow set of operations. Everything outside the list is denied by PolicyChecker:

decision = checker.check_action("research-agent", "delete_records")

if decision.denied:
    print(f"Blocked before execution: {decision.reason_code}")

This gives security reviews a concrete answer to the question: “What can this agent do?”

Enforce API budgets and rate limits

Declare budget and rate-limit policies next to the agent's permitted actions. Adapters or preflight checks can evaluate these policies before a costly call:

id: web-research-agent
name: Web Research Agent
department: marketing
description: Collects competitor information from public sources.
skills: [web_search, scrape_public_website, summarize_text]
validators: [public-source-validator]
policies:
  - max_10_searches_per_hour
  - require_budget_check
  - block_known_malicious_domains
allowed_actions: [search, read_public_url, extract_keywords]
blocked_actions: [login_to_website, submit_forms, call_premium_paid_api]
metadata:
  api_budget_limit_usd: 5.00

The registry makes the constraint visible, versionable, and reviewable instead of burying it inside one agent implementation.

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🏗️ Architecture

flowchart LR
    A["Developer or security team"] --> B["Explicit registry.yaml manifest"]
    B --> C["hlinor-registry compile"]
    C --> D["Integrity-checked JSON policy bundle"]
    D --> E["Runtime adapter or PolicyChecker"]
    E --> F{"Action permitted?"}
    F -->|Yes| G["Execute tool or skill"]
    F -->|No| H["Block and record decision"]
    E --> I["Execution receipts and audit evidence"]
    I --> J["Review, compliance, and incident response"]

Hlinor sits beside your execution framework. Your agents can continue to run in LangChain, CrewAI, or a custom stack while their action boundaries are compiled from an explicit, inspectable manifest.

Long-lived LangChain tools and @governed functions detect a changed bundle and reload it before the next decision. Deploy new bundles atomically so a running process always observes a complete, digest-verified file.

The compiler writes through a verified temporary file and atomically replaces the destination. Agent and capability namespaces are separate, unknown explicit entity types are rejected, and production manifests reject permissive agents unless the unsafe CLI override is deliberately supplied. A missing type remains compatible with legacy agent files; new files should declare type: agent or type: capability explicitly.

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🆚 Hlinor vs. alternatives

Capability	LangChain	CrewAI	Build it yourself	Hlinor Registry
Primary role	Agent and tool orchestration	Multi-agent orchestration	Whatever you implement	Governance and policy layer
Policy source	Application and tool code	Agent/task configuration	Custom conventions	Declarative YAML contracts
Action decisions	Add your own guardrails	Add your own guardrails	Fully custom	Reusable PolicyChecker and validators
Runtime boundaries	Framework-dependent	Framework-dependent	Custom	Allowlist/blocklist patterns and schemas
Audit model	Build around your stack	Build around your stack	Fully custom	Audit-ready receipts and evidence schemas
Works with other frameworks	Not the goal	Not the goal	Depends on design	Designed to sit beside them

Hlinor is not an execution framework. Use it when governance must be explicit, reviewable, and portable across the systems that execute your agents.

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👥 Who is this for?

• Platform teams building internal agent infrastructure.
• Security and compliance teams reviewing agent capabilities.
• Developers who need a policy boundary before tools cause side effects.
• Teams operating multiple agents across departments or projects.
• Open-source maintainers who want YAML examples and automated validation in CI.

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📦 Installation

From PyPI

pip install hlinor-registry

The core package requires Python 3.10 or newer and PyYAML. It does not install LangChain or another agent framework.

Optional integrations

Hlinor is framework-agnostic. We provide ready-to-use wrappers for popular agent ecosystems:

LangChain

pip install "hlinor-registry[langchain]"

from hlinor_registry.integrations.langchain import GovernedTool

safe_tool = GovernedTool(
    tool=my_langchain_tool,
    agent_id="research-agent",
    bundle_path="./dist/policy-bundle.json",
)

CrewAI

pip install "hlinor-registry[crewai]"

from hlinor_registry.integrations.crewai import GovernedCrewTool

safe_search_tool = GovernedCrewTool(
    executor=my_crewai_tool.func,
    name="web_search",
    description="Search the web",
    agent_id="research-agent",
    action_name="search_web",
    bundle_path="./dist/policy-bundle.json",
)

See examples/ for complete, runnable integration examples.

Development dependencies

pip install -e ".[dev]"
pytest

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💻 CLI Reference

Zero-friction commands:

hlinor-registry --version                          # Show version
hlinor-registry init                               # Generate template registry.yaml and my_agent.yaml
hlinor-registry check --bundle X --agent Y --action Z  # Test an action against a compiled bundle
hlinor-registry explain --bundle X --agent Y --action Z  # Get detailed audit explanation
hlinor-registry check --bundle X --agent Y --action Z --format jsonl --audit-log decisions.jsonl

Core commands:

# Compile an explicit manifest into the integrity-checked runtime bundle
hlinor-registry compile --manifest registry.yaml --output dist/policy-bundle.json

# Explicit unsafe override for controlled migration only
hlinor-registry compile --manifest registry.yaml --output dist/policy-bundle.json \
  --allow-permissive-production

# Validate a registry file
hlinor-registry validate-agent examples/search-agent.yaml

# Validate runtime governance contracts
hlinor-registry validate-execution-context <path>
hlinor-registry validate-action-preflight <path>
hlinor-registry validate-capability <path>
hlinor-registry validate-capability-registration examples/funding_intelligence.yaml
hlinor-registry validate-protected-resource-boundary <path>
hlinor-registry validate-evidence-claim <path>
hlinor-registry validate-circuit-breaker <path>

# Inspect a YAML file without changing it
hlinor-registry inspect <path>

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📚 Documentation

Models and architecture

• Execution model
• Approval model
• Runtime bindings and execution receipts
• Audit trail
• ActionRequest and decision provenance
• Control Layer architecture
• Project isolation
• Task workspace
• Department handoff

Governance patterns

• Production action boundary
• Protected resource boundary
• Preflight before a costly action
• Evidence-bound claims
• Capability verification
• Agent lifecycle operating modes

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🛡️ Trust signals

• Comprehensive automated tests covering compilation, validation, policy enforcement, and CLI commands.
• GitHub Actions runs the test suite on Python 3.10, 3.11, 3.12, and 3.13.
• Pre-commit hooks (ruff, mypy, yamllint) ensure consistent code quality.
• YAML schemas, examples, and governance decisions are designed to be reviewed in pull requests.
• Licensed under Apache-2.0 for broad open-source and commercial use.

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🤝 Community and support

• Star the repository if it helps your team.
• Report bugs or request features through GitHub Issues.
• Discuss designs and use cases in GitHub Discussions.
• Read CONTRIBUTING.md before opening a pull request.
• Follow the Code of Conduct when participating.

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🏢 Enterprise

Teams adopting agent governance at scale can contact the HlinorAI team at [e-mail] for architecture guidance, policy design, and integration support.

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📜 License

Hlinor Agent Registry is available under the Apache License 2.0.

🚀 Contributing

Contributions are welcome. Start with an issue or pull request that explains the governance problem, the proposed registry contract, and how the behavior is tested.

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