normlayer 0.1.0

Framework-agnostic Python SDK for enforcing behavioral policies between agents in multi-agent pipelines

NormLayer

A framework-agnostic Python SDK that enforces behavioral policies between agents at runtime in multi-agent pipelines.

The Problem

When multiple agents work together (one planning, one executing, one reviewing) there is no standard way to enforce how they behave toward each other. Existing safety tools focus on agent-to-human behavior. NormLayer focuses on agent-to-agent behavior: detecting deception between agents, enforcing role boundaries, catching collusion, and escalating conflicts — all at runtime, across any framework.

Installation

# Core SDK (no heavy dependencies)
pip install normlayer

# With semantic scoring for NoDeception policy
pip install normlayer[embeddings]

# With AWS S3 logging + SageMaker audit
pip install normlayer[aws]

# Everything
pip install normlayer[all]

Quick Start

from normlayer import PolicyEngine, policies

engine = PolicyEngine(
    policies=[
        policies.NoDeception(threshold=0.8, handler="escalate"),
        policies.RoleRespect(strict=True, handler="block"),
        policies.EscalateOnConflict(to="supervisor_agent"),
        policies.LoopDetection(max_repetitions=3, handler="warn"),
        policies.NoUnsanctionedAction(
            permissions={"worker": ["execute", "update"]},
            handler="block",
        ),
    ],
)

# Use as a decorator
@engine.enforce
def my_agent(message, context):
    return response

# Or wrap an existing agent inline
safe_agent = engine.wrap(existing_agent)

Built-in Policies

Policy	Description	Default Handler
NoDeception	Detects when an agent misrepresents information to another agent using embedding similarity	warn
RoleRespect	Flags agents operating outside their defined role/scope	warn
EscalateOnConflict	Triggers escalation to a supervisor when agents disagree past a threshold	escalate
LoopDetection	Detects agents stuck in unproductive repetitive exchanges	warn
ResponseProportionality	Catches disproportionate responses relative to the triggering input	warn
CoalitionConsistency	Checks whether agents apply norms consistently across in-group vs. out-group	warn
NormConflictResolution	Detects contradictory directives given to an agent (e.g., "be brief" + "be thorough")	warn
NoUnsanctionedAction	Enforces action allowlists per agent — blocks unauthorized actions	block

Framework Adapters

NormLayer works with any multi-agent framework through thin adapters.

LangGraph

from normlayer.adapters import LangGraphAdapter

adapter = LangGraphAdapter(engine)
safe_graph = adapter.wrap(compiled_graph)
result = safe_graph.invoke({"messages": [HumanMessage(content="Plan the task")]})

CrewAI

from normlayer.adapters import CrewAIAdapter

adapter = CrewAIAdapter(engine)
safe_crew = adapter.wrap(my_crew)
result = safe_crew.kickoff()

AutoGen

from normlayer.adapters import AutoGenAdapter

adapter = AutoGenAdapter(engine)
safe_agent = adapter.wrap(my_agent)
response = await safe_agent.on_messages(messages, cancellation_token)

AWS Integration

S3 Violation Logging

engine = PolicyEngine(
    policies=[...],
    aws_bucket="my-normlayer-logs",
    aws_region="us-east-1",
)

# Violations are automatically shipped to S3
# Flush buffered violations manually if needed
engine.flush_violations()

SageMaker Batch Audit

from normlayer.logging import SageMakerAuditJob

job = SageMakerAuditJob(
    role_arn="arn:aws:iam::123456789:role/SageMakerRole",
    input_s3_uri="s3://my-normlayer-logs/violations/",
    output_s3_uri="s3://my-normlayer-logs/audit-results/",
)
job.run()
print(job.status())

Handler Actions

Each policy can dispatch one of four handler actions on violation:

Handler	Behavior
block	Raises EnforcementError, stopping the message
warn	Logs the violation but allows the message through
escalate	Routes to a designated supervisor agent
log	Records silently for audit; no visible action

Examples

See the examples/ directory for demo notebooks:

• langgraph_demo.ipynb — wrapping a LangGraph compiled graph
• crewai_demo.ipynb — wrapping a CrewAI crew
• autogen_demo.ipynb — wrapping an AutoGen agent

Development

# Clone and install dev dependencies
git clone https://github.com/baekbyte/NormLayer.git
cd normlayer
pip install -e ".[dev]"

# Run tests
pytest --tb=short -q

# Type checking
mypy normlayer/

Contributing

Contributions are welcome! Please open an issue or pull request on GitHub.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Write tests for your changes
4. Ensure all tests pass (pytest)
5. Submit a pull request

License

MIT License. See LICENSE for details.