Pre-hooks and Post-hooks

from agno.agent import Agent
from agno.exceptions import CheckTrigger, InputCheckError

# Simple function we will use as a pre-hook
def validate_input_length(
    run_input: RunInput,
    session: AgentSession,
    user_id: Optional[str] = None,
    debug_mode: Optional[bool] = None,
) -> None:
    """Pre-hook to validate input length."""
    max_length = 1000
    if len(run_input.input_content) > max_length:
        raise InputCheckError(
            f"Input too long. Max {max_length} characters allowed",
            check_trigger=CheckTrigger.INPUT_NOT_ALLOWED,
        )

agent = Agent(
    name="My Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    # Provide the pre-hook to the Agent using the pre_hooks parameter
    pre_hooks=[validate_input_length],
)

You can see some complete examples of pre-hooks in the [Examples](/examples/concepts/agents/pre-hooks-and-post-hooks) section.

## Pre-hooks Parameters

Pre-hooks run automatically during the Agent run. Some parameters will be injected automatically.

You can learn more about the parameters in the [Pre-hooks](/reference/hooks/pre-hooks) reference.


## Post-hooks

Post-hooks execute **after** your Agent generates a response, allowing you to validate, transform, or enrich the output before it reaches the user.

They're perfect to handle output filtering, compliance checks, or any other output transformation you need.

### Common Use Cases

**Output Validation**
- Validate response format, length, and content quality.
- Remove sensitive or inappropriate information from responses.
- Ensure compliance with business rules and regulations.

**Output Transformation**
- Add metadata or additional context to responses.
- Transform output format for different clients or use cases.
- Implement any other output transformation you need.

### Basic Example

Let's create a simple post-hook that validates the output length and raises an error if it's too long:

```python
from agno.exceptions import CheckTrigger, OutputCheckError
from agno.run.agent import RunOutput

# Simple function we will use as a post-hook
def validate_output_length(run_output: RunOutput) -> None:
    """Post-hook to validate output length."""
    max_length = 1000
    if len(run_output.content) > max_length:
        raise OutputCheckError(
            f"Output too long. Max {max_length} characters allowed",
            check_trigger=CheckTrigger.OUTPUT_NOT_ALLOWED,
        )

agent = Agent(
    name="My Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    # Provide the post-hook to the Agent using the post_hooks parameter
    post_hooks=[validate_output_length],
)