Guardrails¶

Guardrails inspect inputs and outputs for policy violations — PII, harmful content, prompt injection, intellectual property, and custom rules — and respond by logging, blocking, or modifying the data.

The UiPath LangChain SDK exposes two complementary patterns:

Middleware — a LangChain-native approach. Guardrail classes are passed as a list to create_agent(..., middleware=[...]). Each class registers hooks (before_agent, after_agent, before_model, after_model, wrap_tool_call) automatically for the configured scope.
Decorator — the same @guardrail API from the core SDK, extended to understand LangChain/LangGraph objects. Scope is inferred automatically from the decorated target type.

For the full list of validators, actions, execution stages, and the low-level API, see the core guardrails documentation.

Middleware pattern¶

Middleware is the preferred approach when all guardrail configuration should live in one place, next to the create_agent() call.

How it works¶

Each built-in middleware class is iterable — unpacking it with * yields one or more AgentMiddleware hook objects that LangChain registers internally. Pass them all inside the middleware=[] list:

from langchain.agents import create_agent

agent = create_agent(
    model=llm,
    tools=[my_tool],
    system_prompt=SYSTEM_PROMPT,
    middleware=[
        *UiPathPIIDetectionMiddleware(...),
        *UiPathHarmfulContentMiddleware(...),
        # ... more middleware
    ],
)

Imports¶

from uipath_langchain.guardrails import (
    BlockAction,
    LogAction,
    LoggingSeverityLevel,
    UiPathDeterministicGuardrailMiddleware,
    UiPathHarmfulContentMiddleware,
    UiPathIntellectualPropertyMiddleware,
    UiPathPIIDetectionMiddleware,
    UiPathUserPromptAttacksMiddleware,
    PIIDetectionEntity,
    HarmfulContentEntity,
    GuardrailExecutionStage,
)
from uipath_langchain.guardrails.enums import (
    HarmfulContentEntityType,
    IntellectualPropertyEntityType,
    PIIDetectionEntityType,
)
from uipath.core.guardrails import GuardrailScope

Built-in middleware classes¶

Class	Supported scopes	Stage support	Extra parameters
`UiPathPIIDetectionMiddleware`	AGENT, LLM, TOOL	PRE / POST / PRE_AND_POST	`entities`, `tools`, `stage`
`UiPathHarmfulContentMiddleware`	AGENT, LLM, TOOL	PRE / POST / PRE_AND_POST	`entities`, `tools`, `stage`
`UiPathUserPromptAttacksMiddleware`	LLM only	PRE only	—
`UiPathIntellectualPropertyMiddleware`	AGENT, LLM only	POST only	`entities`
`UiPathDeterministicGuardrailMiddleware`	TOOL only	PRE / POST / PRE_AND_POST	`tools`, `rules`, `stage`

TOOL scope for UiPathPIIDetectionMiddleware and UiPathHarmfulContentMiddleware requires passing tools=[...] to restrict wrap_tool_call hooks to specific tools.

All classes share these common parameters:

name (str) — display name for this guardrail instance.
action — what to do on violation: LogAction(...) or BlockAction(...).
scopes (list[GuardrailScope]) — restrict which hooks are registered. Defaults shown in the table above. Use GuardrailScope.AGENT, GuardrailScope.LLM, GuardrailScope.TOOL.
enabled_for_evals (bool, default True) — set False to skip this guardrail when the agent runs in evaluation mode.

Additional parameters per class:

UiPathPIIDetectionMiddleware / UiPathHarmfulContentMiddleware: entities (list of entity configs), tools (restrict TOOL-scope hooks to specific tools), stage.
UiPathUserPromptAttacksMiddleware: no extra parameters.
UiPathIntellectualPropertyMiddleware: entities (list of IntellectualPropertyEntityType values).
UiPathDeterministicGuardrailMiddleware: tools (required — list of tools to guard), rules (list of lambda functions), stage.

Full example¶

from langchain.agents import create_agent
from langchain_core.tools import tool
from uipath_langchain.chat import UiPathChat
from uipath_langchain.guardrails import (
    BlockAction,
    LogAction,
    LoggingSeverityLevel,
    UiPathDeterministicGuardrailMiddleware,
    UiPathHarmfulContentMiddleware,
    UiPathIntellectualPropertyMiddleware,
    UiPathPIIDetectionMiddleware,
    UiPathUserPromptAttacksMiddleware,
    PIIDetectionEntity,
    HarmfulContentEntity,
    GuardrailExecutionStage,
)
from uipath_langchain.guardrails.enums import (
    HarmfulContentEntityType,
    IntellectualPropertyEntityType,
    PIIDetectionEntityType,
)
from uipath.core.guardrails import GuardrailScope


@tool
def analyze_text(text: str) -> str:
    """Count words in the provided text."""
    return f"Word count: {len(text.split())}"


llm = UiPathChat(model="gpt-4o-2024-08-06")

agent = create_agent(
    model=llm,
    tools=[analyze_text],
    system_prompt="You are a helpful assistant.",
    middleware=[
        # PII detection on agent input/output and LLM messages
        *UiPathPIIDetectionMiddleware(
            name="PII detector",
            scopes=[GuardrailScope.AGENT, GuardrailScope.LLM],
            action=LogAction(severity_level=LoggingSeverityLevel.WARNING),
            entities=[
                PIIDetectionEntity(PIIDetectionEntityType.EMAIL, threshold=0.5),
                PIIDetectionEntity(PIIDetectionEntityType.CREDIT_CARD_NUMBER, threshold=0.5),
            ],
        ),
        # PII detection restricted to TOOL scope for specific tools
        *UiPathPIIDetectionMiddleware(
            name="Tool PII detector",
            scopes=[GuardrailScope.TOOL],
            action=LogAction(severity_level=LoggingSeverityLevel.WARNING),
            entities=[
                PIIDetectionEntity(PIIDetectionEntityType.PHONE_NUMBER, threshold=0.5),
            ],
            tools=[analyze_text],
            enabled_for_evals=False,
        ),
        # Block adversarial user prompts at the LLM level
        *UiPathUserPromptAttacksMiddleware(
            name="User prompt attacks",
            action=BlockAction(),
            enabled_for_evals=False,
        ),
        # Block harmful content in agent + LLM messages
        *UiPathHarmfulContentMiddleware(
            name="Harmful content",
            scopes=[GuardrailScope.AGENT, GuardrailScope.LLM],
            action=BlockAction(),
            entities=[
                HarmfulContentEntity(HarmfulContentEntityType.VIOLENCE, threshold=2),
            ],
        ),
        # Log IP violations in LLM output (POST only)
        *UiPathIntellectualPropertyMiddleware(
            name="IP detection",
            scopes=[GuardrailScope.LLM],
            action=LogAction(severity_level=LoggingSeverityLevel.WARNING),
            entities=[IntellectualPropertyEntityType.TEXT],
        ),
        # Deterministic: block tool input longer than 1000 chars
        *UiPathDeterministicGuardrailMiddleware(
            tools=[analyze_text],
            rules=[lambda data: len(data.get("text", "")) > 1000],
            action=BlockAction(detail="Input too long"),
            stage=GuardrailExecutionStage.PRE,
            name="Length limiter",
        ),
    ],
)

Deterministic middleware¶

UiPathDeterministicGuardrailMiddleware applies in-process rule functions without any API call.

rules — list of callables. For PRE stage: (input_dict: dict) -> bool. For POST stage: (input_dict: dict, output_dict: dict) -> bool. Return True to signal a violation.
rules=[] (empty list) — always triggers the action, useful for unconditional transforms.
stage — GuardrailExecutionStage.PRE, POST, or PRE_AND_POST.

# Always replace a word in tool output (unconditional transform, empty rules)
*UiPathDeterministicGuardrailMiddleware(
    tools=[analyze_text],
    rules=[],
    action=CustomFilterAction(word_to_filter="count", replacement="total"),
    stage=GuardrailExecutionStage.POST,
    name="Output transformer",
),

Custom middleware hooks¶

For logic that doesn't fit a built-in class, implement raw middleware hooks using the langchain.agents.middleware decorators and pass them directly to the middleware list.

Available hook decorators: before_agent, after_agent, before_model, after_model, wrap_tool_call.

from langchain.agents.middleware import AgentState, before_agent, after_agent
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.runtime import Runtime


@before_agent
async def log_agent_input(state: AgentState, runtime: Runtime) -> None:
    for msg in reversed(state.get("messages", [])):
        if isinstance(msg, HumanMessage):
            print(f"[INPUT] {msg.content}")
            break


@after_agent
async def log_agent_output(state: AgentState, runtime: Runtime) -> None:
    for msg in reversed(state.get("messages", [])):
        if isinstance(msg, AIMessage):
            print(f"[OUTPUT] {msg.content}")
            break


LoggingMiddleware = [log_agent_input, log_agent_output]

agent = create_agent(
    model=llm,
    tools=[analyze_text],
    middleware=[
        *LoggingMiddleware,
        *UiPathPIIDetectionMiddleware(...),
    ],
)

Custom actions¶

Both the built-in middleware and UiPathDeterministicGuardrailMiddleware accept any GuardrailAction subclass as the action parameter. This lets you implement content sanitisation, redaction, or any other custom response to a violation:

import re
from dataclasses import dataclass
from typing import Any
from uipath.core.guardrails import GuardrailValidationResult, GuardrailValidationResultType
from uipath_langchain.guardrails import GuardrailAction


@dataclass
class RedactAction(GuardrailAction):
    pattern: str
    replacement: str = "[REDACTED]"

    def handle_validation_result(
        self,
        result: GuardrailValidationResult,
        data: str | dict[str, Any],
        guardrail_name: str,
    ) -> str | dict[str, Any] | None:
        if result.result != GuardrailValidationResultType.VALIDATION_FAILED:
            return None
        if isinstance(data, str):
            return re.sub(self.pattern, self.replacement, data, flags=re.IGNORECASE)
        return None

Decorator pattern¶

Importing uipath_langchain.guardrails auto-registers a LangChain adapter that extends @guardrail to wrap LangChain/LangGraph objects in addition to plain Python callables. All validators, actions, execution stages, stacking, and GuardrailExclude work exactly as described in the core guardrails documentation — use uipath_langchain.guardrails as the import path, which re-exports everything from the core SDK.

from uipath_langchain.guardrails import guardrail, PIIValidator, BlockAction, ...

LangChain target types¶

On LangChain objects, scope is inferred automatically from the target — no stage inference, but no explicit scopes= parameter is needed either:

Decorated target	Inferred scope	Notes
`@tool` function	TOOL	`@guardrail` must be placed above `@tool`
`BaseChatModel` factory	LLM	Decorate the factory function, not the model instance
`create_agent()` factory	AGENT	Decorate the factory function, not the returned agent
LangGraph node (async fn)	AGENT	—

LLM factory¶

from uipath_langchain.chat import UiPathChat
from uipath_langchain.guardrails import guardrail, PIIValidator, BlockAction, GuardrailExecutionStage, PIIDetectionEntity
from uipath_langchain.guardrails.enums import PIIDetectionEntityType

@guardrail(
    validator=PIIValidator(
        entities=[PIIDetectionEntity(PIIDetectionEntityType.EMAIL, threshold=0.5)],
    ),
    action=BlockAction(),
    name="LLM PII check",
    stage=GuardrailExecutionStage.PRE,
)
def create_llm():
    return UiPathChat(model="gpt-4o-2024-08-06")

llm = create_llm()

Tool¶

@guardrail must be placed above @tool:

from langchain_core.tools import tool
from uipath_langchain.guardrails import guardrail, CustomValidator, BlockAction, GuardrailExecutionStage

@guardrail(
    validator=CustomValidator(lambda args: len(args.get("text", "")) > 1000),
    action=BlockAction(detail="Text exceeds 1000 characters"),
    stage=GuardrailExecutionStage.PRE,
    name="Length limiter",
)
@tool
def analyze_text(text: str) -> str:
    """Count words in the provided text."""
    return f"Word count: {len(text.split())}"

Agent factory¶

from langchain.agents import create_agent
from uipath_langchain.guardrails import guardrail, HarmfulContentValidator, BlockAction, GuardrailExecutionStage, HarmfulContentEntity
from uipath_langchain.guardrails.enums import HarmfulContentEntityType

@guardrail(
    validator=HarmfulContentValidator(
        entities=[HarmfulContentEntity(HarmfulContentEntityType.VIOLENCE, threshold=2)],
    ),
    action=BlockAction(),
    name="Block harmful content",
    stage=GuardrailExecutionStage.PRE,
)
def create_my_agent():
    return create_agent(model=llm, tools=[analyze_text], system_prompt="...")

agent = create_my_agent()

LangGraph node¶

from uipath_langchain.guardrails import guardrail, PIIValidator, BlockAction, GuardrailExecutionStage, PIIDetectionEntity
from uipath_langchain.guardrails.enums import PIIDetectionEntityType

@guardrail(
    validator=PIIValidator(
        entities=[PIIDetectionEntity(PIIDetectionEntityType.PERSON, threshold=0.5)],
    ),
    action=BlockAction(title="Person name in input"),
    name="Node PII check",
    stage=GuardrailExecutionStage.PRE,
)
async def my_node(state: Input) -> Output:
    ...

Choosing between middleware and decorator¶

	Middleware	Decorator
Configuration location	Centralised in `create_agent()`	Per-target, co-located with the object
Scope specification	Explicit `scopes=` parameter	Auto-inferred from the decorated type
Works outside `create_agent`	No	Yes — any LangGraph graph or plain function
Reusable validator objects	No	Yes — declare once, use in multiple decorators
Parameter exclusion	No	`GuardrailExclude()` annotation
Custom deterministic rules	`UiPathDeterministicGuardrailMiddleware(rules=[...])`	`CustomValidator(lambda ...)`

Use middleware when you want all guardrail policy in one place alongside a single create_agent() call. Use decorators when building custom LangGraph graphs, reusing validators across multiple agents, or guarding code outside the agent context.

Sample agents¶

samples/joke-agent — middleware pattern
samples/joke-agent-decorator — decorator pattern

Reference¶

GuardrailScope¶

Imported from uipath.core.guardrails.

Value	Description
`AGENT`	Hooks run at agent input/output boundary (`before_agent` / `after_agent`)
`LLM`	Hooks run around every LLM call (`before_model` / `after_model`)
`TOOL`	Hooks run around every tool call (`wrap_tool_call`)

GuardrailExecutionStage¶

Imported from uipath_langchain.guardrails.

Value	When it fires
`PRE`	Before the call (inspect / block inputs)
`POST`	After the call (inspect / transform outputs)
`PRE_AND_POST`	Both — used only by `UiPathDeterministicGuardrailMiddleware`

LoggingSeverityLevel¶

Used in LogAction(severity_level=...). Imported from uipath_langchain.guardrails.

Value
`DEBUG`
`INFO`
`WARNING`
`ERROR`

PIIDetectionEntityType¶

Imported from uipath_langchain.guardrails.enums. Wrap each value in PIIDetectionEntity(entity_type, threshold=0.5) — threshold is a float from 0.0 to 1.0.

PII detection entity types supported by UiPath guardrails.

Value
`PERSON`
`ADDRESS`
`DATE`
`PHONE_NUMBER`
`EUGPS_COORDINATES`
`EMAIL`
`CREDIT_CARD_NUMBER`
`INTERNATIONAL_BANKING_ACCOUNT_NUMBER`
`SWIFT_CODE`
`ABA_ROUTING_NUMBER`
`US_DRIVERS_LICENSE_NUMBER`
`UK_DRIVERS_LICENSE_NUMBER`
`US_INDIVIDUAL_TAXPAYER_IDENTIFICATION`
`UK_UNIQUE_TAXPAYER_NUMBER`
`US_BANK_ACCOUNT_NUMBER`
`US_SOCIAL_SECURITY_NUMBER`
`USUK_PASSPORT_NUMBER`
`URL`
`IP_ADDRESS`

HarmfulContentEntityType¶

Imported from uipath_langchain.guardrails.enums. Wrap each value in HarmfulContentEntity(entity_type, threshold=2) — threshold must be one of 0, 2, 4, or 6 (higher = less sensitive).

Harmful content entity types supported by UiPath guardrails.

Value
`HATE`
`SELF_HARM`
`SEXUAL`
`VIOLENCE`

IntellectualPropertyEntityType¶

Imported from uipath_langchain.guardrails.enums. Pass values directly in entities=[...] — no wrapper model class.

Intellectual property entity types supported by UiPath guardrails.

Value
`TEXT`
`CODE`