Framework Integrations

EnforceCore provides zero-dependency adapter modules for popular AI agent frameworks. Each adapter wraps the framework's native tool decorator with full enforcement — policy checks, PII redaction, audit trails, resource guards, and rate limiting — in a single line.

!!! note "No hard dependencies" EnforceCore does not depend on any framework. Adapter modules use optional imports and raise a clear ImportError with install instructions if the framework is not installed.

Quick Start

# LangGraph / LangChain
from enforcecore.integrations.langgraph import enforced_tool

@enforced_tool(policy="policy.yaml")
def search_web(query: str) -> str:
    """Search the web."""
    return api.search(query)

# CrewAI
from enforcecore.integrations.crewai import enforced_tool

@enforced_tool(policy="policy.yaml")
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))

# AutoGen
from enforcecore.integrations.autogen import enforced_tool

@enforced_tool(policy="policy.yaml", description="Get weather")
def get_weather(city: str) -> str:
    return f"Weather in {city}: 22°C, sunny"

Shared Utilities

These helpers are useful when building custom adapters for other frameworks.

::: enforcecore.integrations._base.wrap_with_policy

::: enforcecore.integrations._base.require_package

LangGraph / LangChain Adapter

The LangGraph adapter creates a StructuredTool (from langchain-core) that wraps the decorated function with the full EnforceCore enforcement pipeline.

from enforcecore.integrations.langgraph import enforced_tool

@enforced_tool(policy="policy.yaml")
def search_web(query: str) -> str:
    """Search the web for information."""
    return api.search(query)

# Use in a LangGraph node — tool calls are automatically enforced
result = search_web.invoke({"query": "AI safety"})

Requirements: pip install langchain-core

CrewAI Adapter

The CrewAI adapter creates a Tool (from crewai) with enforcement applied to every invocation.

from enforcecore.integrations.crewai import enforced_tool

@enforced_tool(policy="policy.yaml")
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))

# Assign to a CrewAI agent
agent = Agent(tools=[calculator], ...)

Requirements: pip install crewai

AutoGen Adapter

The AutoGen adapter creates an FunctionTool (from autogen-core v0.4+) with enforcement wrapping.

from enforcecore.integrations.autogen import enforced_tool

@enforced_tool(policy="policy.yaml", description="Get current weather")
def get_weather(city: str) -> str:
    return f"Weather in {city}: 22°C, sunny"

# Register with an AutoGen agent
agent.register_tool(get_weather)

Requirements: pip install autogen-core

Building Custom Adapters

Use wrap_with_policy to add enforcement to any callable:

from enforcecore.core.policy import Policy
from enforcecore.integrations import wrap_with_policy

policy = Policy.from_file("policy.yaml")

def my_tool(x: int) -> int:
    return x * 2

enforced_fn = wrap_with_policy(my_tool, policy=policy)
result = enforced_fn(21)  # Fully enforced