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wehub-resource-sync
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# Callback Sample
## Overview
This sample demonstrates how to use callbacks in ADK to intercept and handle events. Specifically, it shows:
1. **`before_tool_callback`**: Intercepts tool calls and conditionally short-circuits them.
1. **`before_model_callback`**: Intercepts requests to the LLM and conditionally short-circuits them.
1. **`after_model_callback`**: Runs after the model completes, allowing you to inspect or modify the response (e.g., appending token usage).
## Sample Inputs
- `What is the weather in Paris?`
*Calls the tool normally*
- `What is the weather in London?`
*Intercepted by the before_tool_callback and returns a mock response*
- `Hi`
*Intercepted by the before_model_callback and returns a direct response*
## How To
### Tool Callback
The sample defines a `before_tool_callback` function:
```python
def before_tool_callback(
tool: BaseTool,
args: dict[str, Any],
tool_context: ToolContext,
) -> dict[str, Any] | None:
# Intercept tool calls for London and return a mocked response
if args.get("city") == "London":
return {
"result": "Weather in London is always rainy (intercepted by callback)."
}
return None
```
If the function returns a dictionary with a `result` key (or any other response data), ADK uses that as the tool output and skips calling the actual tool.
### Model Callback
The sample also defines a `before_model_callback` function:
```python
def before_model_callback(
callback_context: CallbackContext,
llm_request: LlmRequest,
) -> LlmResponse | None:
# Short-circuit if the user simply says "Hi"
if llm_request.contents:
last_content = llm_request.contents[-1]
if last_content.parts:
last_part = last_content.parts[-1]
if last_part.text and last_part.text.strip().lower() == "hi":
return LlmResponse(
content=types.Content(
role="model",
parts=[
types.Part.from_text(
text="Hello from before_model callback!"
)
],
)
)
return None
```
If this function returns an `LlmResponse`, ADK skips calling the LLM and returns this response to the user.
### After Model Callback
The sample also defines an `after_model_callback` function:
```python
def after_model_callback(
callback_context: CallbackContext,
llm_response: LlmResponse,
) -> LlmResponse:
# Append token usage to the response text if available
if llm_response.usage_metadata:
usage = llm_response.usage_metadata
usage_text = f"\n\nafter_model_callback: [Token Usage: Input={usage.prompt_token_count}, Output={usage.candidates_token_count}]"
if not llm_response.content:
llm_response.content = types.Content(role="model", parts=[])
llm_response.content.parts.append(types.Part.from_text(text=usage_text))
return llm_response
```
This callback runs after the LLM returns a response. It checks if `usage_metadata` is available in the `llm_response`, constructs a string with input and output token counts, and appends it as a new part to the content.
All callbacks are registered in the `Agent` constructor:
```python
root_agent = Agent(
name="callback_demo_agent",
tools=[get_weather],
before_tool_callback=before_tool_callback,
before_model_callback=before_model_callback,
after_model_callback=after_model_callback,
)
```
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# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import agent
@@ -0,0 +1,118 @@
# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Any
from google.adk.agents import Agent
from google.adk.agents.callback_context import CallbackContext
from google.adk.models.llm_request import LlmRequest
from google.adk.models.llm_response import LlmResponse
from google.adk.tools import BaseTool
from google.adk.tools import ToolContext
from google.genai import types
def get_weather(city: str) -> str:
return f"The weather in {city} is sunny."
def before_tool_callback(
tool: BaseTool,
args: dict[str, Any],
tool_context: ToolContext,
) -> dict[str, Any] | None:
"""A callback that runs before a tool is called.
Args:
tool: The tool instance being called.
args: The arguments passed to the tool.
tool_context: The context for the tool execution.
Returns:
A dict containing the mock response if the call should be short-circuited,
or None to proceed with the actual tool call.
"""
# Intercept tool calls for London and return a mocked response
if args.get("city") == "London":
return {
"result": "Weather in London is always rainy (intercepted by callback)."
}
return None
def before_model_callback(
callback_context: CallbackContext,
llm_request: LlmRequest,
) -> LlmResponse | None:
"""A callback that runs before the model is called.
Args:
callback_context: The context for the callback.
llm_request: The request that is about to be sent to the model.
Returns:
An LlmResponse to short-circuit the model call, or None to proceed.
"""
# Short-circuit if the user simply says "Hi"
if llm_request.contents:
last_content = llm_request.contents[-1]
if last_content.parts:
last_part = last_content.parts[-1]
if last_part.text and last_part.text.strip().lower() == "hi":
return LlmResponse(
content=types.Content(
role="model",
parts=[
types.Part.from_text(
text="Hello from before_model callback!"
)
],
)
)
return None
def after_model_callback(
callback_context: CallbackContext,
llm_response: LlmResponse,
) -> LlmResponse:
"""A callback that runs after the model is called."""
if llm_response.usage_metadata:
usage = llm_response.usage_metadata
usage_text = (
"\n\nafter_model_callback: [Token Usage:"
f" Input={usage.prompt_token_count},"
f" Output={usage.candidates_token_count}]"
)
if not llm_response.content:
llm_response.content = types.Content(role="model", parts=[])
llm_response.content.parts.append(types.Part.from_text(text=usage_text))
print(llm_response.content)
return llm_response
root_agent = Agent(
name="callback_demo_agent",
tools=[get_weather],
before_tool_callback=before_tool_callback,
before_model_callback=before_model_callback,
after_model_callback=after_model_callback,
)
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{
"events": [
{
"author": "user",
"content": {
"parts": [
{
"text": "Hi"
}
],
"role": "user"
},
"id": "e-1",
"invocationId": "i-1",
"nodeInfo": {
"path": ""
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"text": "Hello from before_model callback!"
}
],
"role": "model"
},
"id": "e-2",
"invocationId": "i-1",
"nodeInfo": {
"path": "callback_demo_agent@1"
}
}
]
}
@@ -0,0 +1,89 @@
{
"events": [
{
"author": "user",
"content": {
"parts": [
{
"text": "weather in London"
}
],
"role": "user"
},
"id": "e-1",
"invocationId": "i-1",
"nodeInfo": {
"path": ""
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"functionCall": {
"args": {
"city": "London"
},
"id": "fc-1",
"name": "get_weather"
}
},
{
"text": "\n\nafter_model_callback: [Token Usage: Input=22, Output=5]"
}
],
"role": "model"
},
"finishReason": "STOP",
"id": "e-2",
"invocationId": "i-1",
"longRunningToolIds": [],
"nodeInfo": {
"path": "callback_demo_agent@1"
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"functionResponse": {
"id": "fc-1",
"name": "get_weather",
"response": {
"result": "Weather in London is always rainy (intercepted by callback)."
}
}
}
],
"role": "user"
},
"id": "e-3",
"invocationId": "i-1",
"nodeInfo": {
"path": "callback_demo_agent@1"
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"text": "The weather in London is always rainy (intercepted by callback)."
},
{
"text": "\n\nafter_model_callback: [Token Usage: Input=133, Output=13]"
}
],
"role": "model"
},
"finishReason": "STOP",
"id": "e-4",
"invocationId": "i-1",
"nodeInfo": {
"path": "callback_demo_agent@1"
}
}
]
}
@@ -0,0 +1,89 @@
{
"events": [
{
"author": "user",
"content": {
"parts": [
{
"text": "weather in Sunnyvale"
}
],
"role": "user"
},
"id": "e-1",
"invocationId": "i-1",
"nodeInfo": {
"path": ""
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"functionCall": {
"args": {
"city": "Sunnyvale"
},
"id": "fc-1",
"name": "get_weather"
}
},
{
"text": "\n\nafter_model_callback: [Token Usage: Input=23, Output=6]"
}
],
"role": "model"
},
"finishReason": "STOP",
"id": "e-2",
"invocationId": "i-1",
"longRunningToolIds": [],
"nodeInfo": {
"path": "callback_demo_agent@1"
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"functionResponse": {
"id": "fc-1",
"name": "get_weather",
"response": {
"result": "The weather in Sunnyvale is sunny."
}
}
}
],
"role": "user"
},
"id": "e-3",
"invocationId": "i-1",
"nodeInfo": {
"path": "callback_demo_agent@1"
}
},
{
"author": "callback_demo_agent",
"content": {
"parts": [
{
"text": "The weather in Sunnyvale is sunny."
},
{
"text": "\n\nafter_model_callback: [Token Usage: Input=108, Output=8]"
}
],
"role": "model"
},
"finishReason": "STOP",
"id": "e-4",
"invocationId": "i-1",
"nodeInfo": {
"path": "callback_demo_agent@1"
}
}
]
}