Guardrails

Guardrails help you build safe, compliant AI applications by validating and filtering content at key points in your agent's execution. They can detect sensitive information, enforce content policies, validate outputs, and prevent unsafe behaviors before they cause problems.

Common use cases include:

• Preventing PII leakage
• Detecting and blocking prompt injection attacks
• Blocking inappropriate or harmful content
• Enforcing business rules and compliance requirements
• Validating output quality and accuracy

You can implement guardrails using middleware (opens in a new tab) to intercept execution at strategic points - before the agent starts, after it completes, or around model and tool calls.

Guardrails can be implemented using two complementary approaches:

• Deterministic guardrails:Use rule-based logic like regex patterns, keyword matching, or explicit checks. Fast, predictable, and cost-effective, but may miss nuanced violations.
• Model-based guardrails:Use LLMs or classifiers to evaluate content with semantic understanding. Catch subtle issues that rules miss, but are slower and more expensive.

LangChain provides both built-in guardrails (e.g., PII detection, human-in-the-loop) and a flexible middleware system for building custom guardrails using either approach.

Built-in guardrails

PII detection

LangChain provides built-in middleware for detecting and handling Personally Identifiable Information (PII) in conversations. This middleware can detect common PII types like emails, credit cards, IP addresses, and more.

PII detection middleware is helpful for cases such as health care and financial applications with compliance requirements, customer service agents that need to sanitize logs, and generally any application handling sensitive user data.

The PII middleware supports multiple strategies for handling detected PII:

With apply_to_output=True, PIIMiddleware also redacts streamed wire output—text deltas, tool-call args, tool outputs, and state snapshots—via a registered stream transformer. Requires langchain>=1.3.2. See Register transformers on middleware (opens in a new tab).

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware
 
 
agent = create_agent(
    model="gpt-5.5",
    tools=[customer_service_tool, email_tool],
    middleware=[
        # Redact emails in user input before sending to model
        PIIMiddleware(
            "email",
            strategy="redact",
            apply_to_input=True,
        ),
        # Mask credit cards in user input
        PIIMiddleware(
            "credit_card",
            strategy="mask",
            apply_to_input=True,
        ),
        # Block API keys - raise error if detected
        PIIMiddleware(
            "api_key",
            detector=r"sk-[a-zA-Z0-9]{32}",
            strategy="block",
            apply_to_input=True,
        ),
    ],
)
 
# When user provides PII, it will be handled according to the strategy
result = agent.invoke({
    "messages": [{"role": "user", "content": "My email is [email protected] and card is 5105-1051-0510-5100"}]
})

Built-in PII types and configuration

Built-in PII types:

• email - Email addresses
• credit_card - Credit card numbers (Luhn validated)
• ip - IP addresses
• mac_address - MAC addresses
• url - URLs

Configuration options:

See the middleware documentation (opens in a new tab) for complete details on PII detection capabilities.

Human-in-the-loop

LangChain provides built-in middleware for requiring human approval before executing sensitive operations. This is one of the most effective guardrails for high-stakes decisions.

Human-in-the-loop middleware is helpful for cases such as financial transactions and transfers, deleting or modifying production data, sending communications to external parties, and any operation with significant business impact.

from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command
 
 
agent = create_agent(
    model="gpt-5.5",
    tools=[search_tool, send_email_tool, delete_database_tool],
    middleware=[
        HumanInTheLoopMiddleware(
            interrupt_on={
                # Require approval for sensitive operations
                "send_email": True,
                "delete_database": True,
                # Auto-approve safe operations
                "search": False,
            }
        ),
    ],
    # Persist the state across interrupts
    checkpointer=InMemorySaver(),
)
 
# Human-in-the-loop requires a thread ID for persistence
config = {"configurable": {"thread_id": "some_id"}}
 
# Agent will pause and wait for approval before executing sensitive tools
result = agent.invoke(
    {"messages": [{"role": "user", "content": "Send an email to the team"}]},
    config=config
)
 
result = agent.invoke(
    Command(resume={"decisions": [{"type": "approve"}]}),
    config=config  # Same thread ID to resume the paused conversation
)

See the [human-in-the-loop documentation](https://docs.langchain.com/oss/langchain/human-in-the-loop) for complete details on implementing approval workflows.

Custom guardrails

For more sophisticated guardrails, you can create custom middleware that runs before or after the agent executes. This gives you full control over validation logic, content filtering, and safety checks.

Before agent guardrails

Use "before agent" hooks to validate requests once at the start of each invocation. This is useful for session-level checks like authentication, rate limiting, or blocking inappropriate requests before any processing begins.

title="Decorator syntax"

After agent guardrails

Use "after agent" hooks to validate final outputs once before returning to the user. This is useful for model-based safety checks, quality validation, or final compliance scans on the complete agent response.

title="Decorator syntax"

Combine multiple guardrails

You can stack multiple guardrails by adding them to the middleware array. They execute in order, allowing you to build layered protection:

from langchain.agents import create_agent
from langchain.agents.middleware import PIIMiddleware, HumanInTheLoopMiddleware
 
agent = create_agent(
    model="gpt-5.5",
    tools=[search_tool, send_email_tool],
    middleware=[
        # Layer 1: Deterministic input filter (before agent)
        ContentFilterMiddleware(banned_keywords=["hack", "exploit"]),
 
        # Layer 2: PII protection (before and after model)
        PIIMiddleware("email", strategy="redact", apply_to_input=True),
        PIIMiddleware("email", strategy="redact", apply_to_output=True),
 
        # Layer 3: Human approval for sensitive tools
        HumanInTheLoopMiddleware(interrupt_on={"send_email": True}),
 
        # Layer 4: Model-based safety check (after agent)
        SafetyGuardrailMiddleware(),
    ],
)

Additional resources

• Middleware documentation (opens in a new tab) - Complete guide to custom middleware
• Middleware API reference (opens in a new tab) - Complete guide to custom middleware
• Human-in-the-loop (opens in a new tab) - Add human review for sensitive operations
• Testing agents (opens in a new tab) - Strategies for testing safety mechanisms