When the loop will not stop

A model that keeps asking for tools would go round forever. ADK counts the model calls, and you decide the limit.

An agent with nothing to stop it

def ping() -> dict:
    """Ping."""
    return {"status": "success"}


agent = LlmAgent(
    name="looper",
    model=PretendModel(replies=[call("ping")]),
    instruction="Keep going.",
    tools=[ping],
)

One scripted reply, which the stand-in repeats once the list runs out. That is exactly what a confused model does: ask for the same tool again and again.

Running it with a limit

from google.adk.agents.run_config import RunConfig
from google.adk.agents.invocation_context import LlmCallsLimitExceededError

runner = InMemoryRunner(agent=agent, app_name="demo")
session = await runner.session_service.create_session(app_name="demo", user_id="u1")
message = types.Content(role="user", parts=[types.Part(text="go")])

RunConfig carries the settings for one run. The one that matters on day one is max_llm_calls.

steps = 0
try:
    async for event in runner.run_async(user_id="u1", session_id=session.id,
                                        new_message=message,
                                        run_config=RunConfig(max_llm_calls=3)):
        steps += 1
except LlmCallsLimitExceededError as error:
    print("stopped:", error)

print("events before it stopped:", steps)

Three calls in, the run was abandoned with an error that names the limit rather than going quiet or spending your money.

What to do when you see it

Raising the number is almost never the fix. The error says the agent cannot make progress, and a bigger limit only delays the same ending.

• Read the events. Which tool did it keep calling, and what did that tool keep returning?
• Look at the tool result. A tool that answers the wrong question makes the model try again.
• Look at the instruction. An agent told to keep checking until it is sure will keep checking.