The maximum limit of 128 auto invocations per user request has been reached. Auto invocation is now disabled.
Nothing throws when Semantic Kernel hits its auto-invoke ceiling. In .NET it writes the line above to the logger, at Debug, and turns auto-invocation off for the rest of the request; in Python the loop simply runs out and makes one more call. Either way the request returns normally, which is why the first thing to establish is whether you hit the ceiling at all.
It is a source-generated log message, declared on one method:
[LoggerMessage(EventId = 0, Level = LogLevel.Debug, Message = "The maximum limit of {MaxNumberOfAutoInvocations} auto invocations per user request has been reached. Auto invocation is now disabled.")] public static partial void LogMaximumNumberOfAutoInvocationsPerUserRequestReached(this ILogger logger, int maxNumberOfAutoInvocations);
From dotnet/src/InternalUtilities/connectors/AI/FunctionCalling/FunctionCallsProcessorLoggerExtensions.cs at commit 25e1215a. The number is interpolated; the 128 at the top of this page is the value it is called with when auto-invocation is on.
The call site, in the processor the connectors ask for their function-calling configuration on every round:
// Disable auto invocation if we've exceeded the allowed auto-invoke limit. int maximumAutoInvokeAttempts = configuration.AutoInvoke ? MaximumAutoInvokeAttempts : 0; if (requestIndex >= maximumAutoInvokeAttempts) { configuration.AutoInvoke = false; this._logger.LogMaximumNumberOfAutoInvocationsPerUserRequestReached(maximumAutoInvokeAttempts); }
From FunctionCalling/FunctionCallsProcessor.cs at commit b7ae840d, where the ceiling is declared as internal const int MaximumAutoInvokeAttempts = 128; and described as “a safeguard against possible runaway execution if the model routinely re-requests the same function over and over.”
Three details in those lines are worth more than the message itself.
It is a log line at Debug, not an exception. You see it only if your logger emits Debug for this category. The request is not failed; a configuration flag is set to false and the request carries on.
A “0” means something else entirely. When auto-invocation is already off for the request, the ceiling is computed as 0, and requestIndex >= 0 is always true — so the same line is written with 0 in it. If your log says “limit of 0”, you did not hit a ceiling; auto-invocation was never on for that request.
The ceiling is a constant. MaximumAutoInvokeAttempts is internal const, so on this path there is no setting of yours that changes the 128.
What you get back is a normal return. In the .NET OpenAI connector, the request loop asks the model again after the flag is off, and then does this:
// If we don't want to attempt to invoke any functions or there is nothing to call, just return the result. if (!functionCallingConfig.AutoInvoke || chatCompletion.ToolCalls.Count == 0) { return [chatMessageContent]; }
From dotnet/src/Connectors/Connectors.OpenAI/Core/ClientCore.ChatCompletion.cs at commit c781da13. In the same file, the tool list sent with that request is built from the configuration’s functions without reference to AutoInvoke, so the model can still ask for a tool on this last round — and if it does, those tool calls come back to you in the message, not run. The same file has a second wording for the older ToolCallBehavior settings: this.Logger.LogDebug("Maximum auto-invoke ({MaximumAutoInvoke}) reached.", maximumAutoInvokeAttempts); — same ceiling logic, also Debug, also no exception.
Python has no message at all. The auto-invoke loop is a for over range(settings.function_choice_behavior.maximum_auto_invoke_attempts), and when it runs out:
else: # Do a final call, without function calling when the max has been reached. self._reset_function_choice_settings(settings) return await self._inner_get_chat_message_contents(chat_history, settings)
From python/semantic_kernel/connectors/ai/chat_completion_client_base.py at commit de54193a; there is no logger call in that branch. In the base class, _reset_function_choice_settings is documented “Override this method to reset the settings” and itself just returns, so whether that last call really goes out without tools is up to your connector’s override, which we have not read for this page. The Python default is DEFAULT_MAX_AUTO_INVOKE_ATTEMPTS = 5, in function_choice_behavior.py at commit 966fa8ca.
In .NET, on the path above, you cannot raise it: the 128 is a constant. In Python you can, and if the job genuinely needed seven rounds and you had five, that is the right fix and you are done.
What a bigger number does not change is what the request does at the ceiling. Neither language fails the request; .NET hands back what the model said last, and Python makes one more call and returns that. The constant’s own comment names the case it exists for — a model that “routinely re-requests the same function over and over” — and in that case more rounds only make the same ending later and more expensive.
Give the model one tool that takes the whole list. If every function you exposed takes one item, the round count tracks the item count and the ceiling is a question of list length. A function that accepts the array turns many rounds into one.
Check the ending yourself. Because nothing throws, the check has to be yours: in .NET, treat unrun function-call content on the returned message as a failed request; in Python, compare the round count to the limit. Either turns a silent ceiling into an error at the place it happened.
Write results out from inside the functions. The work your functions finished before the ceiling is the work. If each function persists what it produced as it runs, a request that hits the wall still leaves its finished items behind.
There is a written guide: the iteration arithmetic as a formula you can run against a job before you launch it, the reasons raising a cap does not finish the job, and batch.py, one standard-library file that collapses a per-item loop into a single pass. It is $19. One working way to pay today is 19 USDC on Base, and delivery is manual: you email the transaction hash and the files come back as a reply. This page is free, ungated, and sells nothing on its own.
The short version: The maximum limit of 128 auto invocations per user request has been reached. is a Debug log line in Semantic Kernel for .NET, not an exception; it turns auto-invocation off, and the OpenAI connector then returns the model’s last message, tool calls included, without running them. A “0” in the line means auto-invocation was never on. Python logs nothing and makes one final call after its default of five rounds. Check the ending yourself, and give the model a tool that takes the whole list.
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