Hooks

Hooks are a composable extensibility mechanism for extending agent functionality by subscribing to events throughout the agent lifecycle. The hook system enables both built-in components and user code to react to or modify agent behavior through strongly-typed event callbacks.

Overview

The hooks system is a composable, type-safe system that supports multiple subscribers per event type.

A Hook Event is a specific event in the lifecycle that callbacks can be associated with. A Hook Callback is a callback function that is invoked when the hook event is emitted.

Hooks enable use cases such as:

• Monitoring agent execution and tool usage
• Modifying tool execution behavior
• Adding validation and error handling
• Monitoring multi-agent execution flow and node transitions
• Debugging complex orchestration patterns
• Implementing custom logging and metrics collection

Basic Usage

Hook callbacks are registered against specific event types and receive strongly-typed event objects when those events occur during agent execution. Each event carries relevant data for that stage of the agent lifecycle - for example, BeforeInvocationEvent includes agent and request details, while BeforeToolCallEvent provides tool information and parameters.

Registering Individual Hook Callbacks

You can register callbacks for specific events using agent.hooks after the fact:

PythonTypeScript

agent = Agent()

# Register individual callbacks
def my_callback(event: BeforeInvocationEvent) -> None:
    print("Custom callback triggered")

agent.hooks.add_callback(BeforeInvocationEvent, my_callback)

const agent = new Agent()

// Register individual callback
const myCallback = (event: BeforeInvocationEvent) => {
  console.log('Custom callback triggered')
}

agent.hooks.addCallback(BeforeInvocationEvent, myCallback)

For multi-agent orchestrators, you can register callbacks for orchestration events:

PythonTypeScript

# Create your orchestrator (Graph or Swarm)
orchestrator = Graph(...)

# Register individual callbacks
def my_callback(event: BeforeNodeCallEvent) -> None:
    print(f"Custom callback triggered")

orchestrator.hooks.add_callback(BeforeNodeCallEvent, my_callback)

// This feature is not yet available in TypeScript SDK

Creating a Hook Provider

The HookProvider protocol allows a single object to register callbacks for multiple events. This pattern works for both single-agent and multi-agent orchestrators:

PythonTypeScript

class LoggingHook(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeInvocationEvent, self.log_start)
        registry.add_callback(AfterInvocationEvent, self.log_end)

    def log_start(self, event: BeforeInvocationEvent) -> None:
        print(f"Request started for agent: {event.agent.name}")

    def log_end(self, event: AfterInvocationEvent) -> None:
        print(f"Request completed for agent: {event.agent.name}")

# Passed in via the hooks parameter
agent = Agent(hooks=[LoggingHook()])

# Or added after the fact
agent.hooks.add_hook(LoggingHook())

class LoggingHook implements HookProvider {
  registerCallbacks(registry: HookRegistry): void {
    registry.addCallback(BeforeInvocationEvent, (ev) => this.logStart(ev))
    registry.addCallback(AfterInvocationEvent, (ev) => this.logEnd(ev))
  }

  private logStart(event: BeforeInvocationEvent): void {
    console.log('Request started')
  }

  private logEnd(event: AfterInvocationEvent): void {
    console.log('Request completed')
  }
}

// Passed in via the hooks parameter
const agent = new Agent({ hooks: [new LoggingHook()] })

// Or added after the fact
agent.hooks.addHook(new LoggingHook())

Hook Event Lifecycle

Single-Agent Lifecycle

The following diagram shows when hook events are emitted during a typical agent invocation where tools are invoked:

flowchart LR
 subgraph Start["Request Start Events"]
    direction TB
        BeforeInvocationEvent["BeforeInvocationEvent"]
        StartMessage["MessageAddedEvent"]
        BeforeInvocationEvent --> StartMessage
  end
 subgraph Model["Model Events"]
    direction TB
        AfterModelCallEvent["AfterModelCallEvent"]
        BeforeModelCallEvent["BeforeModelCallEvent"]
        ModelMessage["MessageAddedEvent"]
        BeforeModelCallEvent --> AfterModelCallEvent
        AfterModelCallEvent --> ModelMessage
  end
  subgraph Tool["Tool Events"]
    direction TB
        AfterToolCallEvent["AfterToolCallEvent"]
        BeforeToolCallEvent["BeforeToolCallEvent"]
        ToolMessage["MessageAddedEvent"]
        BeforeToolCallEvent --> AfterToolCallEvent
        AfterToolCallEvent --> ToolMessage
  end
  subgraph End["Request End Events"]
    direction TB
        AfterInvocationEvent["AfterInvocationEvent"]
  end
Start --> Model
Model <--> Tool
Tool --> End

Multi-Agent Lifecycle

The following diagram shows when multi-agent hook events are emitted during orchestrator execution:

flowchart LR
subgraph Init["Initialization"]
    direction TB
    MultiAgentInitializedEvent["MultiAgentInitializedEvent"]
end
subgraph Invocation["Invocation Lifecycle"]
    direction TB
    BeforeMultiAgentInvocationEvent["BeforeMultiAgentInvocationEvent"]
    AfterMultiAgentInvocationEvent["AfterMultiAgentInvocationEvent"]
    BeforeMultiAgentInvocationEvent --> NodeExecution
    NodeExecution --> AfterMultiAgentInvocationEvent
end
subgraph NodeExecution["Node Execution (Repeated)"]
    direction TB
    BeforeNodeCallEvent["BeforeNodeCallEvent"]
    AfterNodeCallEvent["AfterNodeCallEvent"]
    BeforeNodeCallEvent --> AfterNodeCallEvent
end
Init --> Invocation

Available Events

The hooks system provides events for different stages of execution. Events marked (Python only) are specific to multi-agent orchestrators and are not available in TypeScript.

Event	Description
AgentInitializedEvent	Triggered when an agent has been constructed and finished initialization at the end of the agent constructor.
BeforeInvocationEvent	Triggered at the beginning of a new agent invocation request
AfterInvocationEvent	Triggered at the end of an agent request, regardless of success or failure. Uses reverse callback ordering
MessageAddedEvent	Triggered when a message is added to the agent's conversation history
BeforeModelCallEvent	Triggered before the model is invoked for inference
AfterModelCallEvent	Triggered after model invocation completes. Uses reverse callback ordering
BeforeToolCallEvent	Triggered before a tool is invoked.
AfterToolCallEvent	Triggered after tool invocation completes. Uses reverse callback ordering
BeforeToolsEvent (TypeScript only)	Triggered before tools are executed in a batch.
AfterToolsEvent (TypeScript only)	Triggered after tools are executed in a batch. Uses reverse callback ordering
MultiAgentInitializedEvent (Python only)	Triggered when multi-agent orchestrator is initialized
BeforeMultiAgentInvocationEvent (Python only)	Triggered before orchestrator execution starts
AfterMultiAgentInvocationEvent (Python only)	Triggered after orchestrator execution completes. Uses reverse callback ordering
BeforeNodeCallEvent (Python only)	Triggered before individual node execution starts
AfterNodeCallEvent (Python only)	Triggered after individual node execution completes. Uses reverse callback ordering

Hook Behaviors

Event Properties

Most event properties are read-only to prevent unintended modifications. However, certain properties can be modified to influence agent behavior:

PythonTypeScript

• AfterModelCallEvent

  • retry - Request a retry of the model invocation. See Model Call Retry.

• BeforeToolCallEvent

  • cancel_tool - Cancel tool execution with a message. See Limit Tool Counts.
  • selected_tool - Replace the tool to be executed. See Tool Interception.
  • tool_use - Modify tool parameters before execution. See Fixed Tool Arguments.

• AfterToolCallEvent

  • result - Modify the tool result. See Result Modification.

• AfterModelCallEvent
  • retryModelCall - Request a retry of the model invocation (typically after reducing context size).

Callback Ordering

Some events come in pairs, such as Before/After events. The After event callbacks are always called in reverse order from the Before event callbacks to ensure proper cleanup semantics.

Advanced Usage

Accessing Invocation State in Hooks

Invocation state provides configuration and context data passed through the agent or orchestrator invocation. This is particularly useful for:

1. Custom Objects: Access database client objects, connection pools, or other Python objects
2. Request Context: Access session IDs, user information, settings, or request-specific data
3. Multi-Agent Shared State: In multi-agent patterns, access state shared across all agents - see Shared State Across Multi-Agent Patterns
4. Custom Parameters: Pass any additional data that hooks might need

PythonTypeScript

from strands.hooks import BeforeToolCallEvent
import logging

def log_with_context(event: BeforeToolCallEvent) -> None:
    """Log tool invocations with context from invocation state."""
    # Access invocation state from the event
    user_id = event.invocation_state.get("user_id", "unknown")
    session_id = event.invocation_state.get("session_id")

    # Access non-JSON serializable objects like database connections
    db_connection = event.invocation_state.get("database_connection")
    logger_instance = event.invocation_state.get("custom_logger")

    # Use custom logger if provided, otherwise use default
    logger = logger_instance if logger_instance else logging.getLogger(__name__)

    logger.info(
        f"User {user_id} in session {session_id} "
        f"invoking tool: {event.tool_use['name']} "
        f"with DB connection: {db_connection is not None}"
    )

# Register the hook
agent = Agent(tools=[my_tool])
agent.hooks.add_callback(BeforeToolCallEvent, log_with_context)

# Execute with context including non-serializable objects
import sqlite3
custom_logger = logging.getLogger("custom")
db_conn = sqlite3.connect(":memory:")

result = agent(
    "Process the data",
    user_id="user123",
    session_id="sess456",
    database_connection=db_conn,  # Non-JSON serializable object
    custom_logger=custom_logger   # Non-JSON serializable object
)

// This feature is not yet available in TypeScript SDK

Multi-agent hook events provide access to:

• source: The multi-agent orchestrator instance (for example: Graph/Swarm)
• node_id: Identifier of the node being executed (for node-level events)
• invocation_state: Configuration and context data passed through the orchestrator invocation

Multi-agent hooks provide configuration and context data passed through the orchestrator's lifecycle.

Tool Interception

Modify or replace tools before execution:

PythonTypeScript

class ToolInterceptor(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeToolCallEvent, self.intercept_tool)

    def intercept_tool(self, event: BeforeToolCallEvent) -> None:
        if event.tool_use.name == "sensitive_tool":
            # Replace with a safer alternative
            event.selected_tool = self.safe_alternative_tool
            event.tool_use["name"] = "safe_tool"

// Changing of tools is not yet available in TypeScript SDK

Result Modification

Modify tool results after execution:

PythonTypeScript

class ResultProcessor(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(AfterToolCallEvent, self.process_result)

    def process_result(self, event: AfterToolCallEvent) -> None:
        if event.tool_use.name == "calculator":
            # Add formatting to calculator results
            original_content = event.result["content"][0]["text"]
            event.result["content"][0]["text"] = f"Result: {original_content}"

// Changing of tool results is not yet available in TypeScript SDK

Conditional Node Execution

Implement custom logic to modify orchestration behavior in multi-agent systems:

PythonTypeScript

class ConditionalExecutionHook(HookProvider):
    def __init__(self, skip_conditions: dict[str, callable]):
        self.skip_conditions = skip_conditions

    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeNodeCallEvent, self.check_execution_conditions)

    def check_execution_conditions(self, event: BeforeNodeCallEvent) -> None:
        node_id = event.node_id
        if node_id in self.skip_conditions:
            condition_func = self.skip_conditions[node_id]
            if condition_func(event.invocation_state):
                print(f"Skipping node {node_id} due to condition")
                # Note: Actual node skipping would require orchestrator-specific implementation

// This feature is not yet available in TypeScript SDK

Best Practices

Composability

Design hooks to be composable and reusable:

PythonTypeScript

class RequestLoggingHook(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeInvocationEvent, self.log_request)
        registry.add_callback(AfterInvocationEvent, self.log_response)
        registry.add_callback(BeforeToolCallEvent, self.log_tool_use)

    ...

// Changing of tools is not yet available in TypeScript SDK

Event Property Modifications

When modifying event properties, log the changes for debugging and audit purposes:

PythonTypeScript

class ResultProcessor(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(AfterToolCallEvent, self.process_result)

    def process_result(self, event: AfterToolCallEvent) -> None:
        if event.tool_use.name == "calculator":
            original_content = event.result["content"][0]["text"]
            logger.info(f"Modifying calculator result: {original_content}")
            event.result["content"][0]["text"] = f"Result: {original_content}"

// Changing of tools is not yet available in TypeScript SDK

Orchestrator-Agnostic Design

Design multi-agent hooks to work with different orchestrator types:

PythonTypeScript

class UniversalMultiAgentHook(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeNodeCallEvent, self.handle_node_execution)

    def handle_node_execution(self, event: BeforeNodeCallEvent) -> None:
        orchestrator_type = type(event.source).__name__
        print(f"Executing node {event.node_id} in {orchestrator_type} orchestrator")

        # Handle orchestrator-specific logic if needed
        if orchestrator_type == "Graph":
            self.handle_graph_node(event)
        elif orchestrator_type == "Swarm":
            self.handle_swarm_node(event)

    def handle_graph_node(self, event: BeforeNodeCallEvent) -> None:
        # Graph-specific handling
        pass

    def handle_swarm_node(self, event: BeforeNodeCallEvent) -> None:
        # Swarm-specific handling
        pass

// This feature is not yet available in TypeScript SDK

Integration with Multi-Agent Systems

Multi-agent hooks complement single-agent hooks. Individual agents within the orchestrator can still have their own hooks, creating a layered monitoring and customization system:

PythonTypeScript

# Single-agent hook for individual agents
class AgentLevelHook(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeToolCallEvent, self.log_tool_use)

    def log_tool_use(self, event: BeforeToolCallEvent) -> None:
        print(f"Agent tool call: {event.tool_use['name']}")

# Multi-agent hook for orchestrator
class OrchestratorLevelHook(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeNodeCallEvent, self.log_node_execution)

    def log_node_execution(self, event: BeforeNodeCallEvent) -> None:
        print(f"Orchestrator node execution: {event.node_id}")

# Create agents with individual hooks
agent1 = Agent(tools=[tool1], hooks=[AgentLevelHook()])
agent2 = Agent(tools=[tool2], hooks=[AgentLevelHook()])

# Create orchestrator with multi-agent hooks
orchestrator = Graph(
    agents={"agent1": agent1, "agent2": agent2},
    hooks=[OrchestratorLevelHook()]
)

// This feature is not yet available in TypeScript SDK

This layered approach provides comprehensive observability and control across both individual agent execution and orchestrator-level coordination.

Cookbook

This section contains practical hook implementations for common use cases.

Fixed Tool Arguments

Useful for enforcing security policies, maintaining consistency, or overriding agent decisions with system-level requirements. This hook ensures specific tools always use predetermined parameter values regardless of what the agent specifies.

PythonTypeScript

from typing import Any
from strands.hooks import HookProvider, HookRegistry, BeforeToolCallEvent

class ConstantToolArguments(HookProvider):
    """Use constant argument values for specific parameters of a tool."""

    def __init__(self, fixed_tool_arguments: dict[str, dict[str, Any]]):
        """
        Initialize fixed parameter values for tools.

        Args:
            fixed_tool_arguments: A dictionary mapping tool names to dictionaries of 
                parameter names and their fixed values. These values will override any 
                values provided by the agent when the tool is invoked.
        """
        self._tools_to_fix = fixed_tool_arguments

    def register_hooks(self, registry: HookRegistry, **kwargs: Any) -> None:
        registry.add_callback(BeforeToolCallEvent, self._fix_tool_arguments)

    def _fix_tool_arguments(self, event: BeforeToolCallEvent):
        # If the tool is in our list of parameters, then use those parameters
        if parameters_to_fix := self._tools_to_fix.get(event.tool_use["name"]):
            tool_input: dict[str, Any] = event.tool_use["input"]
            tool_input.update(parameters_to_fix)

// Changing of tools is not yet available in TypeScript SDK

For example, to always force the calculator tool to use precision of 1 digit:

PythonTypeScript

fix_parameters = ConstantToolArguments({
    "calculator": {
        "precision": 1,
    }
})

agent = Agent(tools=[calculator], hooks=[fix_parameters])
result = agent("What is 2 / 3?")

// Changing of tools is not yet available in TypeScript SDK

Limit Tool Counts

Useful for preventing runaway tool usage, implementing rate limiting, or enforcing usage quotas. This hook tracks tool invocations per request and replaces tools with error messages when limits are exceeded.

PythonTypeScript

from strands import tool
from strands.hooks import HookRegistry, HookProvider, BeforeToolCallEvent, BeforeInvocationEvent
from threading import Lock

class LimitToolCounts(HookProvider):
    """Limits the number of times tools can be called per agent invocation"""

    def __init__(self, max_tool_counts: dict[str, int]):
        """
        Initializer.

        Args:
            max_tool_counts: A dictionary mapping tool names to max call counts for 
                tools. If a tool is not specified in it, the tool can be called as many
                times as desired
        """
        self.max_tool_counts = max_tool_counts
        self.tool_counts = {}
        self._lock = Lock()

    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeInvocationEvent, self.reset_counts)
        registry.add_callback(BeforeToolCallEvent, self.intercept_tool)

    def reset_counts(self, event: BeforeInvocationEvent) -> None:
        with self._lock:
            self.tool_counts = {}

    def intercept_tool(self, event: BeforeToolCallEvent) -> None:
        tool_name = event.tool_use["name"]
        with self._lock:
            max_tool_count = self.max_tool_counts.get(tool_name)
            tool_count = self.tool_counts.get(tool_name, 0) + 1
            self.tool_counts[tool_name] = tool_count

        if max_tool_count and tool_count > max_tool_count:
            event.cancel_tool = (
                f"Tool '{tool_name}' has been invoked too many and is now being throttled. "
                f"DO NOT CALL THIS TOOL ANYMORE "
            )

// This feature is not yet available in TypeScript SDK

For example, to limit the sleep tool to 3 invocations per invocation:

PythonTypeScript

limit_hook = LimitToolCounts(max_tool_counts={"sleep": 3})

agent = Agent(tools=[sleep], hooks=[limit_hook])

# This call will only have 3 successful sleeps
agent("Sleep 5 times for 10ms each or until you can't anymore")
# This will sleep successfully again because the count resets every invocation
agent("Sleep once")

// This feature is not yet available in TypeScript SDK

Model Call Retry

Useful for implementing custom retry logic for model invocations. The AfterModelCallEvent.retry field allows hooks to request retries based on any criteria—exceptions, response validation, content quality checks, or any custom logic. This example demonstrates retrying on exceptions with exponential backoff:

PythonTypeScript

import asyncio
import logging
from strands.hooks import HookProvider, HookRegistry, BeforeInvocationEvent, AfterModelCallEvent

logger = logging.getLogger(__name__)

class RetryOnServiceUnavailable(HookProvider):
    """Retry model calls when ServiceUnavailable errors occur."""

    def __init__(self, max_retries: int = 3):
        self.max_retries = max_retries
        self.retry_count = 0

    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(BeforeInvocationEvent, self.reset_counts)
        registry.add_callback(AfterModelCallEvent, self.handle_retry)

    def reset_counts(self, event: BeforeInvocationEvent = None) -> None:
        self.retry_count = 0

    async def handle_retry(self, event: AfterModelCallEvent) -> None:
        if event.exception:
            if "ServiceUnavailable" in str(event.exception):
                logger.info("ServiceUnavailable encountered")
                if self.retry_count < self.max_retries:
                    logger.info("Retrying model call")
                    self.retry_count += 1
                    event.retry = True
                    await asyncio.sleep(2 ** self.retry_count)  # Exponential backoff
        else:
            # Reset counts on successful call
            self.reset_counts()

// This feature is not yet available in TypeScript SDK

For example, to retry up to 3 times on service unavailable errors:

PythonTypeScript

from strands import Agent

retry_hook = RetryOnServiceUnavailable(max_retries=3)
agent = Agent(hooks=[retry_hook])

result = agent("What is the capital of France?")

// This feature is not yet available in TypeScript SDK