mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-07-02 05:38:12 +00:00
ensure result_as_answer, hooks, and cache parodity
This commit is contained in:
lorenzejay
@@ -744,192 +744,297 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
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for tool in self.original_tools or []:
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original_tools_by_name[sanitize_tool_name(tool.name)] = tool
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# Reserve max-usage slots deterministically in call order.
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# This prevents race conditions when multiple parallel calls target the same tool.
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reserved_usage_by_tool: dict[str, int] = {}
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execution_plan: list[tuple[str, str, str | dict[str, Any], Any | None, bool]] = []
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for call_id, func_name, func_args in parsed_calls:
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original_tool = original_tools_by_name.get(func_name)
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should_execute = True
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if (
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original_tool
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and getattr(original_tool, "max_usage_count", None) is not None
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):
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current_usage = getattr(original_tool, "current_usage_count", 0)
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reserved = reserved_usage_by_tool.get(func_name, 0)
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if current_usage + reserved >= original_tool.max_usage_count:
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should_execute = False
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else:
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reserved_usage_by_tool[func_name] = reserved + 1
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execution_plan.append(
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(call_id, func_name, func_args, original_tool, should_execute)
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)
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assistant_message: LLMMessage = {
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": call_id,
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"type": "function",
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"function": {
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"name": func_name,
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"arguments": func_args
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if isinstance(func_args, str)
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else json.dumps(func_args),
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},
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}
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for call_id, func_name, func_args, _, _ in execution_plan
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],
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}
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self.messages.append(assistant_message)
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def _execute_one(
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idx: int,
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call_id: str,
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func_name: str,
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func_args: str | dict[str, Any],
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original_tool: Any | None,
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should_execute: bool,
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) -> tuple[int, str, str, str, Any | None]:
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if isinstance(func_args, str):
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try:
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args_dict = json.loads(func_args)
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except json.JSONDecodeError:
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args_dict = {}
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else:
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args_dict = func_args
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agent_key = (
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getattr(self.agent, "key", "unknown") if self.agent else "unknown"
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)
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started_at = datetime.now()
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crewai_event_bus.emit(
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self,
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event=ToolUsageStartedEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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agent_key=agent_key,
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),
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)
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track_delegation_if_needed(func_name, args_dict, self.task)
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error_event_emitted = False
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result: str = "Tool not found"
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if not should_execute and original_tool:
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result = (
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f"Tool '{func_name}' has reached its usage limit of "
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f"{original_tool.max_usage_count} times and cannot be used anymore."
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has_result_as_answer_in_batch = any(
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bool(
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original_tools_by_name.get(func_name)
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and getattr(
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original_tools_by_name.get(func_name), "result_as_answer", False
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)
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)
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for _, func_name, _ in parsed_calls
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)
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# Preserve sequential short-circuit behavior for result_as_answer tools.
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if has_result_as_answer_in_batch:
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logger.debug(
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"Skipping parallel native execution because batch includes result_as_answer tool"
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)
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else:
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# Reserve max-usage slots deterministically in call order.
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# This prevents race conditions when multiple parallel calls target the same tool.
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reserved_usage_by_tool: dict[str, int] = {}
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execution_plan: list[
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tuple[str, str, str | dict[str, Any], Any | None, bool]
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] = []
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for call_id, func_name, func_args in parsed_calls:
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original_tool = original_tools_by_name.get(func_name)
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should_execute = True
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if (
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original_tool
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and getattr(original_tool, "max_usage_count", None) is not None
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):
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current_usage = getattr(original_tool, "current_usage_count", 0)
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reserved = reserved_usage_by_tool.get(func_name, 0)
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if current_usage + reserved >= original_tool.max_usage_count:
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should_execute = False
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else:
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reserved_usage_by_tool[func_name] = reserved + 1
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execution_plan.append(
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(call_id, func_name, func_args, original_tool, should_execute)
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)
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elif func_name in available_functions:
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try:
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raw_result = available_functions[func_name](**args_dict)
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result = (
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str(raw_result)
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if not isinstance(raw_result, str)
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else raw_result
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)
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except Exception as e:
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result = f"Error executing tool: {e}"
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if self.task:
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self.task.increment_tools_errors()
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crewai_event_bus.emit(
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self,
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event=ToolUsageErrorEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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agent_key=agent_key,
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error=e,
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),
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)
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error_event_emitted = True
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if not error_event_emitted:
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assistant_message: LLMMessage = {
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": call_id,
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"type": "function",
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"function": {
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"name": func_name,
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"arguments": func_args
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if isinstance(func_args, str)
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else json.dumps(func_args),
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},
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}
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for call_id, func_name, func_args, _, _ in execution_plan
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],
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}
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self.messages.append(assistant_message)
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def _execute_one(
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idx: int,
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call_id: str,
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func_name: str,
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func_args: str | dict[str, Any],
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original_tool: Any | None,
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should_execute: bool,
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) -> tuple[int, str, str, str, Any | None]:
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if isinstance(func_args, str):
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try:
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args_dict = json.loads(func_args)
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except json.JSONDecodeError:
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args_dict = {}
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else:
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args_dict = func_args
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agent_key = (
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getattr(self.agent, "key", "unknown")
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if self.agent
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else "unknown"
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)
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started_at = datetime.now()
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crewai_event_bus.emit(
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self,
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event=ToolUsageFinishedEvent(
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output=result,
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event=ToolUsageStartedEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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agent_key=agent_key,
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started_at=started_at,
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finished_at=datetime.now(),
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),
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)
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return idx, call_id, func_name, result, original_tool
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track_delegation_if_needed(func_name, args_dict, self.task)
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max_workers = min(8, len(parsed_calls))
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ordered_results: list[tuple[int, str, str, str, Any | None] | None] = [
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None
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] * len(parsed_calls)
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with ThreadPoolExecutor(max_workers=max_workers) as pool:
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futures = {
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pool.submit(
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_execute_one,
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idx,
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call_id,
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func_name,
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func_args,
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original_tool,
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should_execute,
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): idx
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for idx, (
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call_id,
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func_name,
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func_args,
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original_tool,
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should_execute,
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) in enumerate(execution_plan)
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}
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for future in as_completed(futures):
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idx = futures[future]
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ordered_results[idx] = future.result()
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structured_tool: CrewStructuredTool | None = None
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for structured in self.tools or []:
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if sanitize_tool_name(structured.name) == func_name:
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structured_tool = structured
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break
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for record in ordered_results:
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if record is None:
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continue
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_, call_id, func_name, result, original_tool = record
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tool_message: LLMMessage = {
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"role": "tool",
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"tool_call_id": call_id,
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"name": func_name,
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"content": result,
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}
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self.messages.append(tool_message)
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if self.agent and self.agent.verbose:
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self._printer.print(
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content=f"Tool {func_name} executed with result: {result[:200]}...",
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color="green",
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hook_blocked = False
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before_hook_context = ToolCallHookContext(
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tool_name=func_name,
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tool_input=args_dict,
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tool=structured_tool, # type: ignore[arg-type]
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agent=self.agent,
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task=self.task,
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crew=self.crew,
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)
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before_hooks = get_before_tool_call_hooks()
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try:
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for hook in before_hooks:
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hook_result = hook(before_hook_context)
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if hook_result is False:
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hook_blocked = True
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break
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except Exception as hook_error:
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if self.agent.verbose:
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self._printer.print(
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content=f"Error in before_tool_call hook: {hook_error}",
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color="red",
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)
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if (
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original_tool
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and hasattr(original_tool, "result_as_answer")
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and original_tool.result_as_answer
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):
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return AgentFinish(
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thought="Tool result is the final answer",
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output=result,
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text=result,
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from_cache = False
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input_str = json.dumps(args_dict) if args_dict else ""
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if self.tools_handler and self.tools_handler.cache:
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cached_result = self.tools_handler.cache.read(
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tool=func_name, input=input_str
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)
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if cached_result is not None:
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result = (
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str(cached_result)
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if not isinstance(cached_result, str)
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else cached_result
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)
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from_cache = True
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error_event_emitted = False
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result: str = "Tool not found"
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if not should_execute and original_tool:
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result = (
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f"Tool '{func_name}' has reached its usage limit of "
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f"{original_tool.max_usage_count} times and cannot be used anymore."
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)
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elif hook_blocked:
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result = f"Tool execution blocked by hook. Tool: {func_name}"
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elif from_cache:
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pass
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elif func_name in available_functions:
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try:
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raw_result = available_functions[func_name](**args_dict)
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if self.tools_handler and self.tools_handler.cache:
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should_cache = True
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if (
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original_tool
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and hasattr(original_tool, "cache_function")
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and callable(original_tool.cache_function)
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):
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should_cache = original_tool.cache_function(
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args_dict, raw_result
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)
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if should_cache:
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self.tools_handler.cache.add(
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tool=func_name,
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input=input_str,
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output=raw_result,
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)
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result = (
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str(raw_result)
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if not isinstance(raw_result, str)
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else raw_result
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)
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except Exception as e:
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result = f"Error executing tool: {e}"
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if self.task:
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self.task.increment_tools_errors()
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crewai_event_bus.emit(
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self,
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event=ToolUsageErrorEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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agent_key=agent_key,
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error=e,
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),
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)
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error_event_emitted = True
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after_hook_context = ToolCallHookContext(
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tool_name=func_name,
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tool_input=args_dict,
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tool=structured_tool, # type: ignore[arg-type]
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agent=self.agent,
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task=self.task,
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crew=self.crew,
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tool_result=result,
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)
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after_hooks = get_after_tool_call_hooks()
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try:
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for after_hook in after_hooks:
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after_hook_result = after_hook(after_hook_context)
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if after_hook_result is not None:
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result = after_hook_result
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after_hook_context.tool_result = result
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except Exception as hook_error:
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if self.agent.verbose:
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self._printer.print(
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content=f"Error in after_tool_call hook: {hook_error}",
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color="red",
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)
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reasoning_prompt = self._i18n.slice("post_tool_reasoning")
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reasoning_message: LLMMessage = {
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"role": "user",
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"content": reasoning_prompt,
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}
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self.messages.append(reasoning_message)
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return None
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if not error_event_emitted:
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crewai_event_bus.emit(
|
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self,
|
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event=ToolUsageFinishedEvent(
|
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output=result,
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
|
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from_task=self.task,
|
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agent_key=agent_key,
|
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started_at=started_at,
|
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finished_at=datetime.now(),
|
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),
|
||||
)
|
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|
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return idx, call_id, func_name, result, original_tool
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|
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max_workers = min(8, len(parsed_calls))
|
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ordered_results: list[tuple[int, str, str, str, Any | None] | None] = [
|
||||
None
|
||||
] * len(parsed_calls)
|
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with ThreadPoolExecutor(max_workers=max_workers) as pool:
|
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futures = {
|
||||
pool.submit(
|
||||
_execute_one,
|
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idx,
|
||||
call_id,
|
||||
func_name,
|
||||
func_args,
|
||||
original_tool,
|
||||
should_execute,
|
||||
): idx
|
||||
for idx, (
|
||||
call_id,
|
||||
func_name,
|
||||
func_args,
|
||||
original_tool,
|
||||
should_execute,
|
||||
) in enumerate(execution_plan)
|
||||
}
|
||||
for future in as_completed(futures):
|
||||
idx = futures[future]
|
||||
ordered_results[idx] = future.result()
|
||||
|
||||
for record in ordered_results:
|
||||
if record is None:
|
||||
continue
|
||||
_, call_id, func_name, result, original_tool = record
|
||||
|
||||
tool_message: LLMMessage = {
|
||||
"role": "tool",
|
||||
"tool_call_id": call_id,
|
||||
"name": func_name,
|
||||
"content": result,
|
||||
}
|
||||
self.messages.append(tool_message)
|
||||
|
||||
if self.agent and self.agent.verbose:
|
||||
self._printer.print(
|
||||
content=f"Tool {func_name} executed with result: {result[:200]}...",
|
||||
color="green",
|
||||
)
|
||||
|
||||
if (
|
||||
original_tool
|
||||
and hasattr(original_tool, "result_as_answer")
|
||||
and original_tool.result_as_answer
|
||||
):
|
||||
return AgentFinish(
|
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thought="Tool result is the final answer",
|
||||
output=result,
|
||||
text=result,
|
||||
)
|
||||
|
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reasoning_prompt = self._i18n.slice("post_tool_reasoning")
|
||||
reasoning_message: LLMMessage = {
|
||||
"role": "user",
|
||||
"content": reasoning_prompt,
|
||||
}
|
||||
self.messages.append(reasoning_message)
|
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return None
|
||||
|
||||
# Only process the FIRST tool call for sequential execution with reflection
|
||||
tool_call = tool_calls[0]
|
||||
|
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@@ -1,7 +1,7 @@
|
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from __future__ import annotations
|
||||
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from collections.abc import Callable, Coroutine
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from datetime import datetime
|
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import json
|
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import threading
|
||||
@@ -699,9 +699,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
"content": None,
|
||||
"tool_calls": tool_calls_to_report,
|
||||
}
|
||||
if all(
|
||||
type(tc).__qualname__ == "Part" for tc in pending_tool_calls
|
||||
):
|
||||
if all(type(tc).__qualname__ == "Part" for tc in pending_tool_calls):
|
||||
assistant_message["raw_tool_call_parts"] = list(pending_tool_calls)
|
||||
self.state.messages.append(assistant_message)
|
||||
|
||||
@@ -722,9 +720,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
pool.submit(self._execute_single_native_tool_call, tool_call): idx
|
||||
for idx, tool_call in enumerate(runnable_tool_calls)
|
||||
}
|
||||
ordered_results: list[dict[str, Any] | None] = [
|
||||
None
|
||||
] * len(runnable_tool_calls)
|
||||
ordered_results: list[dict[str, Any] | None] = [None] * len(
|
||||
runnable_tool_calls
|
||||
)
|
||||
for future in as_completed(future_to_idx):
|
||||
idx = future_to_idx[future]
|
||||
ordered_results[idx] = future.result()
|
||||
@@ -732,10 +730,46 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
result for result in ordered_results if result is not None
|
||||
]
|
||||
else:
|
||||
execution_results = [
|
||||
self._execute_single_native_tool_call(tool_call)
|
||||
for tool_call in runnable_tool_calls
|
||||
]
|
||||
# Execute sequentially so result_as_answer tools can short-circuit
|
||||
# immediately without running remaining calls.
|
||||
for tool_call in runnable_tool_calls:
|
||||
execution_result = self._execute_single_native_tool_call(tool_call)
|
||||
call_id = cast(str, execution_result["call_id"])
|
||||
func_name = cast(str, execution_result["func_name"])
|
||||
result = cast(str, execution_result["result"])
|
||||
from_cache = cast(bool, execution_result["from_cache"])
|
||||
original_tool = execution_result["original_tool"]
|
||||
|
||||
tool_message: LLMMessage = {
|
||||
"role": "tool",
|
||||
"tool_call_id": call_id,
|
||||
"name": func_name,
|
||||
"content": result,
|
||||
}
|
||||
self.state.messages.append(tool_message)
|
||||
|
||||
# Log the tool execution
|
||||
if self.agent and self.agent.verbose:
|
||||
cache_info = " (from cache)" if from_cache else ""
|
||||
self._printer.print(
|
||||
content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
|
||||
color="green",
|
||||
)
|
||||
|
||||
if (
|
||||
original_tool
|
||||
and hasattr(original_tool, "result_as_answer")
|
||||
and original_tool.result_as_answer
|
||||
):
|
||||
self.state.current_answer = AgentFinish(
|
||||
thought="Tool result is the final answer",
|
||||
output=result,
|
||||
text=result,
|
||||
)
|
||||
self.state.is_finished = True
|
||||
return "tool_result_is_final"
|
||||
|
||||
return "native_tool_completed"
|
||||
|
||||
for execution_result in execution_results:
|
||||
call_id = cast(str, execution_result["call_id"])
|
||||
@@ -843,7 +877,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
from_cache = False
|
||||
input_str = json.dumps(args_dict) if args_dict else ""
|
||||
if self.tools_handler and self.tools_handler.cache:
|
||||
cached_result = self.tools_handler.cache.read(tool=func_name, input=input_str)
|
||||
cached_result = self.tools_handler.cache.read(
|
||||
tool=func_name, input=input_str
|
||||
)
|
||||
if cached_result is not None:
|
||||
result = (
|
||||
str(cached_result)
|
||||
@@ -920,7 +956,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
|
||||
|
||||
# Convert to string for message
|
||||
result = (
|
||||
str(raw_result) if not isinstance(raw_result, str) else raw_result
|
||||
str(raw_result)
|
||||
if not isinstance(raw_result, str)
|
||||
else raw_result
|
||||
)
|
||||
except Exception as e:
|
||||
result = f"Error executing tool: {e}"
|
||||
|
||||
@@ -586,3 +586,53 @@ class TestNativeToolExecution:
|
||||
assert elapsed < 0.8
|
||||
assert isinstance(executor.state.current_answer, AgentFinish)
|
||||
assert executor.state.current_answer.output == "one"
|
||||
|
||||
def test_execute_native_tool_result_as_answer_short_circuits_remaining_calls(
|
||||
self, mock_dependencies
|
||||
):
|
||||
executor = AgentExecutor(**mock_dependencies)
|
||||
call_counts = {"slow_one": 0, "slow_two": 0}
|
||||
|
||||
def slow_one() -> str:
|
||||
call_counts["slow_one"] += 1
|
||||
time.sleep(0.2)
|
||||
return "one"
|
||||
|
||||
def slow_two() -> str:
|
||||
call_counts["slow_two"] += 1
|
||||
time.sleep(0.2)
|
||||
return "two"
|
||||
|
||||
result_tool = Mock()
|
||||
result_tool.name = "slow_one"
|
||||
result_tool.result_as_answer = True
|
||||
result_tool.max_usage_count = None
|
||||
result_tool.current_usage_count = 0
|
||||
|
||||
executor.original_tools = [result_tool]
|
||||
executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two}
|
||||
executor.state.pending_tool_calls = [
|
||||
{
|
||||
"id": "call_1",
|
||||
"function": {"name": "slow_one", "arguments": "{}"},
|
||||
},
|
||||
{
|
||||
"id": "call_2",
|
||||
"function": {"name": "slow_two", "arguments": "{}"},
|
||||
},
|
||||
]
|
||||
|
||||
started = time.perf_counter()
|
||||
result = executor.execute_native_tool()
|
||||
elapsed = time.perf_counter() - started
|
||||
|
||||
assert result == "tool_result_is_final"
|
||||
assert isinstance(executor.state.current_answer, AgentFinish)
|
||||
assert executor.state.current_answer.output == "one"
|
||||
assert call_counts["slow_one"] == 1
|
||||
assert call_counts["slow_two"] == 0
|
||||
assert elapsed < 0.35
|
||||
|
||||
tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"]
|
||||
assert len(tool_messages) == 1
|
||||
assert tool_messages[0]["tool_call_id"] == "call_1"
|
||||
|
||||
@@ -9,6 +9,7 @@ from __future__ import annotations
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
from collections import Counter
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
@@ -17,6 +18,8 @@ from pydantic import BaseModel, Field
|
||||
from crewai import Agent, Crew, Task
|
||||
from crewai.events import crewai_event_bus
|
||||
from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
|
||||
from crewai.hooks import register_after_tool_call_hook, register_before_tool_call_hook
|
||||
from crewai.hooks.tool_hooks import ToolCallHookContext
|
||||
from crewai.llm import LLM
|
||||
from crewai.tools.base_tool import BaseTool
|
||||
|
||||
@@ -322,6 +325,136 @@ class TestOpenAINativeToolCalling:
|
||||
assert result is not None
|
||||
_assert_tools_overlapped()
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.timeout(180)
|
||||
def test_openai_parallel_native_tool_calling_tool_hook_parity_crew(
|
||||
self, parallel_tools: list[BaseTool]
|
||||
) -> None:
|
||||
ParallelProbe.reset()
|
||||
_attach_parallel_probe_handler()
|
||||
hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []}
|
||||
|
||||
def before_hook(context: ToolCallHookContext) -> bool | None:
|
||||
if context.tool_name.startswith("parallel_local_search_"):
|
||||
hook_calls["before"].append(
|
||||
{
|
||||
"tool_name": context.tool_name,
|
||||
"query": str(context.tool_input.get("query", "")),
|
||||
}
|
||||
)
|
||||
return None
|
||||
|
||||
def after_hook(context: ToolCallHookContext) -> str | None:
|
||||
if context.tool_name.startswith("parallel_local_search_"):
|
||||
hook_calls["after"].append(
|
||||
{
|
||||
"tool_name": context.tool_name,
|
||||
"query": str(context.tool_input.get("query", "")),
|
||||
}
|
||||
)
|
||||
return None
|
||||
|
||||
register_before_tool_call_hook(before_hook)
|
||||
register_after_tool_call_hook(after_hook)
|
||||
|
||||
try:
|
||||
agent = Agent(
|
||||
role="Parallel Tool Agent",
|
||||
goal="Use both tools exactly as instructed",
|
||||
backstory="You follow tool instructions precisely.",
|
||||
tools=parallel_tools,
|
||||
llm=LLM(model="gpt-5-nano", temperature=1),
|
||||
verbose=False,
|
||||
max_iter=3,
|
||||
)
|
||||
task = Task(
|
||||
description=_parallel_prompt(),
|
||||
expected_output="A one sentence summary of both tool outputs",
|
||||
agent=agent,
|
||||
)
|
||||
crew = Crew(agents=[agent], tasks=[task])
|
||||
result = crew.kickoff()
|
||||
|
||||
assert result is not None
|
||||
_assert_tools_overlapped()
|
||||
|
||||
before_names = [call["tool_name"] for call in hook_calls["before"]]
|
||||
after_names = [call["tool_name"] for call in hook_calls["after"]]
|
||||
assert len(before_names) >= 3, "Expected before hooks for all parallel calls"
|
||||
assert Counter(before_names) == Counter(after_names)
|
||||
assert all(call["query"] for call in hook_calls["before"])
|
||||
assert all(call["query"] for call in hook_calls["after"])
|
||||
finally:
|
||||
from crewai.hooks import (
|
||||
unregister_after_tool_call_hook,
|
||||
unregister_before_tool_call_hook,
|
||||
)
|
||||
|
||||
unregister_before_tool_call_hook(before_hook)
|
||||
unregister_after_tool_call_hook(after_hook)
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.timeout(180)
|
||||
def test_openai_parallel_native_tool_calling_tool_hook_parity_agent_kickoff(
|
||||
self, parallel_tools: list[BaseTool]
|
||||
) -> None:
|
||||
ParallelProbe.reset()
|
||||
_attach_parallel_probe_handler()
|
||||
hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []}
|
||||
|
||||
def before_hook(context: ToolCallHookContext) -> bool | None:
|
||||
if context.tool_name.startswith("parallel_local_search_"):
|
||||
hook_calls["before"].append(
|
||||
{
|
||||
"tool_name": context.tool_name,
|
||||
"query": str(context.tool_input.get("query", "")),
|
||||
}
|
||||
)
|
||||
return None
|
||||
|
||||
def after_hook(context: ToolCallHookContext) -> str | None:
|
||||
if context.tool_name.startswith("parallel_local_search_"):
|
||||
hook_calls["after"].append(
|
||||
{
|
||||
"tool_name": context.tool_name,
|
||||
"query": str(context.tool_input.get("query", "")),
|
||||
}
|
||||
)
|
||||
return None
|
||||
|
||||
register_before_tool_call_hook(before_hook)
|
||||
register_after_tool_call_hook(after_hook)
|
||||
|
||||
try:
|
||||
agent = Agent(
|
||||
role="Parallel Tool Agent",
|
||||
goal="Use both tools exactly as instructed",
|
||||
backstory="You follow tool instructions precisely.",
|
||||
tools=parallel_tools,
|
||||
llm=LLM(model="gpt-5-nano", temperature=1),
|
||||
verbose=False,
|
||||
max_iter=3,
|
||||
)
|
||||
result = agent.kickoff(_parallel_prompt())
|
||||
|
||||
assert result is not None
|
||||
_assert_tools_overlapped()
|
||||
|
||||
before_names = [call["tool_name"] for call in hook_calls["before"]]
|
||||
after_names = [call["tool_name"] for call in hook_calls["after"]]
|
||||
assert len(before_names) >= 3, "Expected before hooks for all parallel calls"
|
||||
assert Counter(before_names) == Counter(after_names)
|
||||
assert all(call["query"] for call in hook_calls["before"])
|
||||
assert all(call["query"] for call in hook_calls["after"])
|
||||
finally:
|
||||
from crewai.hooks import (
|
||||
unregister_after_tool_call_hook,
|
||||
unregister_before_tool_call_hook,
|
||||
)
|
||||
|
||||
unregister_before_tool_call_hook(before_hook)
|
||||
unregister_after_tool_call_hook(after_hook)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Anthropic Provider Tests
|
||||
|
||||
@@ -0,0 +1,264 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"messages":[{"role":"system","content":"You are Parallel Tool Agent. You
|
||||
follow tool instructions precisely.\nYour personal goal is: Use both tools exactly
|
||||
as instructed"},{"role":"user","content":"\nCurrent Task: This is a tool-calling
|
||||
compliance test. In your next assistant turn, emit exactly 3 tool calls in the
|
||||
same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest
|
||||
OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic
|
||||
model release notes''), 3) parallel_local_search_three(query=''latest Gemini
|
||||
model release notes''). Do not call any other tools and do not answer before
|
||||
those 3 tool calls are emitted. After the tool results return, provide a one
|
||||
paragraph summary."}],"model":"gpt-5-nano","temperature":1,"tool_choice":"auto","tools":[{"type":"function","function":{"name":"parallel_local_search_one","description":"Local
|
||||
search tool #1 for concurrency testing.","strict":true,"parameters":{"properties":{"query":{"description":"Search
|
||||
query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}},{"type":"function","function":{"name":"parallel_local_search_two","description":"Local
|
||||
search tool #2 for concurrency testing.","strict":true,"parameters":{"properties":{"query":{"description":"Search
|
||||
query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}},{"type":"function","function":{"name":"parallel_local_search_three","description":"Local
|
||||
search tool #3 for concurrency testing.","strict":true,"parameters":{"properties":{"query":{"description":"Search
|
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|
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follow tool instructions precisely.\nYour personal goal is: Use both tools exactly
|
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as instructed"},{"role":"user","content":"\nCurrent Task: This is a tool-calling
|
||||
compliance test. In your next assistant turn, emit exactly 3 tool calls in the
|
||||
same response (parallel tool calls), in this order: 1) parallel_local_search_one(query=''latest
|
||||
OpenAI model release notes''), 2) parallel_local_search_two(query=''latest Anthropic
|
||||
model release notes''), 3) parallel_local_search_three(query=''latest Gemini
|
||||
model release notes''). Do not call any other tools and do not answer before
|
||||
those 3 tool calls are emitted. After the tool results return, provide a one
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Reference in New Issue
Block a user