Adk Callbacks Hitl

ADK Callbacks and Human-in-the-Loop

Critical Rule: Exact Parameter Names

ADK callbacks use keyword argument matching. The framework inspects parameter names to inject the correct objects. Using wrong parameter names causes silent failures.

Callback Signatures

before_model_callback

Called before every LLM call. Return None to proceed normally, or return an LlmResponse to short-circuit the LLM call.

from google.adk.agents.callback_context import CallbackContext
from google.genai.types import Content, Part
from google.adk.models import LlmRequest, LlmResponse

def before_model_callback(
    callback_context: CallbackContext,
    llm_request: LlmRequest,
) -> LlmResponse | None:
    # Return None → proceed with LLM call
    # Return LlmResponse → skip the LLM call, use this response instead
    return None

after_model_callback

Called after every LLM response. Return None to use the original response, or return a modified LlmResponse.

def after_model_callback(
    callback_context: CallbackContext,
    llm_response: LlmResponse,
) -> LlmResponse | None:
    # Inspect or modify the response
    return None  # Use original response

before_tool_callback

Called before a tool executes. Return None to allow the tool to run, or return a dict to skip the tool and use the dict as the result.

from google.adk.tools import BaseTool

def before_tool_callback(
    tool: BaseTool,
    args: dict[str, Any],
    tool_context: CallbackContext,
) -> dict | None:
    # Return None → tool runs normally
    # Return dict → tool is skipped, dict is used as result
    return None

after_tool_callback

Called after a tool executes. Return None to use the original result, or return a modified dict.

def after_tool_callback(
    tool: BaseTool,
    args: dict[str, Any],
    tool_context: CallbackContext,
    result: dict,
) -> dict | None:
    return None  # Use original result

Registering Callbacks on an Agent

from google.adk.agents import LlmAgent

agent = LlmAgent(
    name="guarded_agent",
    model="gemini-2.0-flash",
    instruction="You are a helpful assistant.",
    before_model_callback=my_before_model_cb,
    after_model_callback=my_after_model_cb,
    before_tool_callback=my_before_tool_cb,
    after_tool_callback=my_after_tool_cb,
)

Common Callback Patterns

Input Guardrail (before_model_callback)

Block harmful or off-topic requests before they reach the LLM:

BLOCKED_TOPICS = ["illegal", "harmful", "exploit"]

def input_guardrail(
    callback_context: CallbackContext,
    llm_request: LlmRequest,
) -> LlmResponse | None:
    last_message = llm_request.contents[-1].parts[0].text.lower()
    for topic in BLOCKED_TOPICS:
        if topic in last_message:
            return LlmResponse(
                content=Content(parts=[Part(text="I cannot help with that topic.")])
            )
    return None

Tool Approval Gate (before_tool_callback)

Require approval before dangerous tools execute — see the hitl-patterns reference for the full implementation.

Response Logging (after_model_callback)

import logging
logger = logging.getLogger(__name__)

def log_responses(
    callback_context: CallbackContext,
    llm_response: LlmResponse,
) -> LlmResponse | None:
    for part in llm_response.content.parts:
        if hasattr(part, "text") and part.text:
            logger.info("[RESPONSE] %s", part.text[:200])
    return None

State Management Rules

In Callbacks: Direct Modification is Safe

def my_callback(callback_context: CallbackContext, **kwargs):
    # This is safe in callbacks
    callback_context.state["counter"] = callback_context.state.get("counter", 0) + 1

In BaseAgent Subclasses: Use EventActions.state_delta

When writing custom agents that subclass BaseAgent, you must use state_delta for state updates. Direct modification may be lost.

from google.adk.events import EventActions

# CORRECT — in a BaseAgent subclass
actions = EventActions(state_delta={"counter": new_value})

# WRONG — in a BaseAgent subclass (changes may be lost)
# session.state["counter"] = new_value

State Prefixes

Callbacks already installed by the generated plugin chain

A generated agent doesn't start from a blank callback slate — the shipped plugin chain already registers real callbacks the reader will encounter before writing their own:

• GuardrailsPlugin — before_model_callback (the halt-latch consumer), after_model_callback (NoProgressGuard, a no-progress/response-loop detector), and after_tool_callback (RepeatedFailureGuard, a repeated-failure detector).
• exfil_guard — a before_tool_callback that scans tool arguments for secret-shaped values and can block the call.
• CronIsolationPlugin — a before_tool_callback that is a no-op on ordinary turns and only engages during a scheduled cron run.

For the full mechanism behind the first two, load adk-long-horizon-guardrails; for the cron-specific one, load adk-cron-isolation.

Worked example: the halt latch as a state-key pattern

The halt guards are a concrete instance of the "write a reason to state, then short-circuit while it's set" pattern this skill teaches in the abstract:

# after_tool_callback (or after_model_callback) — writes the reason
def latch_halt(state, reason: str) -> bool:
    if state.get("halt_reason"):
        return False  # already halted — first reason wins
    state["halt_reason"] = reason
    return True

# before_model_callback — consumes it
async def halt_consumer_callback(*, callback_context, llm_request=None):
    reason = callback_context.state.get("halt_reason")
    if not reason:
        return None                       # not halted — model call proceeds
    callback_context.state["__halt_handoff_delivered__"] = True
    return LlmResponse(content=halt_content(reason))

Note what the shipped version does not do: nothing in the default plugin chain calls acknowledge_halt / reset_halt_handoff to clear the latch, so a halted session stays halted until something explicitly clears it at a turn boundary — a latch does not self-clear. See adk-long-horizon-guardrails for that clearing contract in full.

References

• Load callback-signatures for every callback signature with WRONG vs CORRECT examples.
• Load hitl-patterns for complete HITL gate implementations including PlanApprovalGate.
• Load state-management for detailed state patterns and output_key usage.