diff --git a/lib/crewai/src/crewai/agents/crew_agent_executor.py b/lib/crewai/src/crewai/agents/crew_agent_executor.py index b734556af2..99991f73b2 100644 --- a/lib/crewai/src/crewai/agents/crew_agent_executor.py +++ b/lib/crewai/src/crewai/agents/crew_agent_executor.py @@ -7,6 +7,7 @@ from __future__ import annotations from collections.abc import Callable +from concurrent.futures import ThreadPoolExecutor, as_completed import logging from typing import TYPE_CHECKING, Any, Literal, cast @@ -685,30 +686,138 @@ def _handle_native_tool_calls( Returns: AgentFinish if tool has result_as_answer=True, None otherwise. """ - from datetime import datetime - import json - - from crewai.events import crewai_event_bus - from crewai.events.types.tool_usage_events import ( - ToolUsageErrorEvent, - ToolUsageFinishedEvent, - ToolUsageStartedEvent, - ) - if not tool_calls: return None - # Only process the FIRST tool call for sequential execution with reflection - tool_call = tool_calls[0] + parsed_calls = [ + parsed + for tool_call in tool_calls + if (parsed := self._parse_native_tool_call(tool_call)) is not None + ] + if not parsed_calls: + return None + + original_tools_by_name: dict[str, Any] = {} + for tool in self.original_tools or []: + original_tools_by_name[sanitize_tool_name(tool.name)] = tool + + if len(parsed_calls) > 1: + has_result_as_answer_in_batch = any( + bool( + original_tools_by_name.get(func_name) + and getattr( + original_tools_by_name.get(func_name), "result_as_answer", False + ) + ) + for _, func_name, _ in parsed_calls + ) + has_max_usage_count_in_batch = any( + bool( + original_tools_by_name.get(func_name) + and getattr( + original_tools_by_name.get(func_name), + "max_usage_count", + None, + ) + is not None + ) + for _, func_name, _ in parsed_calls + ) + + # Preserve historical sequential behavior for result_as_answer batches. + # Also avoid threading around usage counters for max_usage_count tools. + if has_result_as_answer_in_batch or has_max_usage_count_in_batch: + logger.debug( + "Skipping parallel native execution because batch includes result_as_answer or max_usage_count tool" + ) + else: + execution_plan: list[ + tuple[str, str, str | dict[str, Any], Any | None] + ] = [] + for call_id, func_name, func_args in parsed_calls: + original_tool = original_tools_by_name.get(func_name) + execution_plan.append((call_id, func_name, func_args, original_tool)) + + self._append_assistant_tool_calls_message( + [ + (call_id, func_name, func_args) + for call_id, func_name, func_args, _ in execution_plan + ] + ) - # Extract tool call info - handle OpenAI-style, Anthropic-style, and Gemini-style + max_workers = min(8, len(execution_plan)) + ordered_results: list[dict[str, Any] | None] = [None] * len(execution_plan) + with ThreadPoolExecutor(max_workers=max_workers) as pool: + futures = { + pool.submit( + self._execute_single_native_tool_call, + call_id=call_id, + func_name=func_name, + func_args=func_args, + available_functions=available_functions, + original_tool=original_tool, + should_execute=True, + ): idx + for idx, ( + call_id, + func_name, + func_args, + original_tool, + ) in enumerate(execution_plan) + } + for future in as_completed(futures): + idx = futures[future] + ordered_results[idx] = future.result() + + for execution_result in ordered_results: + if not execution_result: + continue + tool_finish = self._append_tool_result_and_check_finality( + execution_result + ) + if tool_finish: + return tool_finish + + reasoning_prompt = self._i18n.slice("post_tool_reasoning") + reasoning_message: LLMMessage = { + "role": "user", + "content": reasoning_prompt, + } + self.messages.append(reasoning_message) + return None + + # Sequential behavior: process only first tool call, then force reflection. + call_id, func_name, func_args = parsed_calls[0] + self._append_assistant_tool_calls_message([(call_id, func_name, func_args)]) + + execution_result = self._execute_single_native_tool_call( + call_id=call_id, + func_name=func_name, + func_args=func_args, + available_functions=available_functions, + original_tool=original_tools_by_name.get(func_name), + should_execute=True, + ) + tool_finish = self._append_tool_result_and_check_finality(execution_result) + if tool_finish: + return tool_finish + + reasoning_prompt = self._i18n.slice("post_tool_reasoning") + reasoning_message: LLMMessage = { + "role": "user", + "content": reasoning_prompt, + } + self.messages.append(reasoning_message) + return None + + def _parse_native_tool_call( + self, tool_call: Any + ) -> tuple[str, str, str | dict[str, Any]] | None: if hasattr(tool_call, "function"): - # OpenAI-style: has .function.name and .function.arguments call_id = getattr(tool_call, "id", f"call_{id(tool_call)}") func_name = sanitize_tool_name(tool_call.function.name) - func_args = tool_call.function.arguments - elif hasattr(tool_call, "function_call") and tool_call.function_call: - # Gemini-style: has .function_call.name and .function_call.args + return call_id, func_name, tool_call.function.arguments + if hasattr(tool_call, "function_call") and tool_call.function_call: call_id = f"call_{id(tool_call)}" func_name = sanitize_tool_name(tool_call.function_call.name) func_args = ( @@ -716,13 +825,12 @@ def _handle_native_tool_calls( if tool_call.function_call.args else {} ) - elif hasattr(tool_call, "name") and hasattr(tool_call, "input"): - # Anthropic format: has .name and .input (ToolUseBlock) + return call_id, func_name, func_args + if hasattr(tool_call, "name") and hasattr(tool_call, "input"): call_id = getattr(tool_call, "id", f"call_{id(tool_call)}") func_name = sanitize_tool_name(tool_call.name) - func_args = tool_call.input # Already a dict in Anthropic - elif isinstance(tool_call, dict): - # Support OpenAI "id", Bedrock "toolUseId", or generate one + return call_id, func_name, tool_call.input + if isinstance(tool_call, dict): call_id = ( tool_call.get("id") or tool_call.get("toolUseId") @@ -733,10 +841,15 @@ def _handle_native_tool_calls( func_info.get("name", "") or tool_call.get("name", "") ) func_args = func_info.get("arguments", "{}") or tool_call.get("input", {}) - else: - return None + return call_id, func_name, func_args + return None + + def _append_assistant_tool_calls_message( + self, + parsed_calls: list[tuple[str, str, str | dict[str, Any]]], + ) -> None: + import json - # Append assistant message with single tool call assistant_message: LLMMessage = { "role": "assistant", "content": None, @@ -751,12 +864,30 @@ def _handle_native_tool_calls( else json.dumps(func_args), }, } + for call_id, func_name, func_args in parsed_calls ], } - self.messages.append(assistant_message) - # Parse arguments for the single tool call + def _execute_single_native_tool_call( + self, + *, + call_id: str, + func_name: str, + func_args: str | dict[str, Any], + available_functions: dict[str, Callable[..., Any]], + original_tool: Any | None = None, + should_execute: bool = True, + ) -> dict[str, Any]: + from datetime import datetime + import json + + from crewai.events.types.tool_usage_events import ( + ToolUsageErrorEvent, + ToolUsageFinishedEvent, + ToolUsageStartedEvent, + ) + if isinstance(func_args, str): try: args_dict = json.loads(func_args) @@ -765,28 +896,26 @@ def _handle_native_tool_calls( else: args_dict = func_args - agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" - - # Find original tool by matching sanitized name (needed for cache_function and result_as_answer) - - original_tool = None - for tool in self.original_tools or []: - if sanitize_tool_name(tool.name) == func_name: - original_tool = tool - break + if original_tool is None: + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break - # Check if tool has reached max usage count max_usage_reached = False - if original_tool: - if ( - hasattr(original_tool, "max_usage_count") - and original_tool.max_usage_count is not None - and original_tool.current_usage_count >= original_tool.max_usage_count - ): - max_usage_reached = True - - # Check cache before executing + if not should_execute and original_tool: + max_usage_reached = True + elif ( + should_execute + and original_tool + and getattr(original_tool, "max_usage_count", None) is not None + and getattr(original_tool, "current_usage_count", 0) + >= original_tool.max_usage_count + ): + max_usage_reached = True + from_cache = False + result: str = "Tool not found" 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( @@ -800,7 +929,7 @@ def _handle_native_tool_calls( ) from_cache = True - # Emit tool usage started event + agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" started_at = datetime.now() crewai_event_bus.emit( self, @@ -816,14 +945,12 @@ def _handle_native_tool_calls( track_delegation_if_needed(func_name, args_dict, self.task) - # Find the structured tool for hook context structured_tool: CrewStructuredTool | None = None for structured in self.tools or []: if sanitize_tool_name(structured.name) == func_name: structured_tool = structured break - # Execute before_tool_call hooks hook_blocked = False before_hook_context = ToolCallHookContext( tool_name=func_name, @@ -847,58 +974,44 @@ def _handle_native_tool_calls( color="red", ) - # If hook blocked execution, set result and skip tool execution if hook_blocked: result = f"Tool execution blocked by hook. Tool: {func_name}" - # Execute the tool (only if not cached, not at max usage, and not blocked by hook) - elif not from_cache and not max_usage_reached: - result = "Tool not found" - if func_name in available_functions: - try: - tool_func = available_functions[func_name] - raw_result = tool_func(**args_dict) - - # Add to cache after successful execution (before string conversion) - if self.tools_handler and self.tools_handler.cache: - should_cache = True - if ( - original_tool - and hasattr(original_tool, "cache_function") - and callable(original_tool.cache_function) - ): - should_cache = original_tool.cache_function( - args_dict, raw_result - ) - if should_cache: - self.tools_handler.cache.add( - tool=func_name, input=input_str, output=raw_result - ) - - # Convert to string for message - result = ( - str(raw_result) - if not isinstance(raw_result, str) - else raw_result - ) - except Exception as e: - result = f"Error executing tool: {e}" - if self.task: - self.task.increment_tools_errors() - crewai_event_bus.emit( - self, - event=ToolUsageErrorEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - error=e, - ), - ) - error_event_emitted = True elif max_usage_reached and original_tool: - # Return error message when max usage limit is reached result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." + elif not from_cache and func_name in available_functions: + try: + raw_result = available_functions[func_name](**args_dict) + + if self.tools_handler and self.tools_handler.cache: + should_cache = True + if ( + original_tool + and hasattr(original_tool, "cache_function") + and callable(original_tool.cache_function) + ): + should_cache = original_tool.cache_function(args_dict, raw_result) + if should_cache: + self.tools_handler.cache.add( + tool=func_name, input=input_str, output=raw_result + ) + + result = str(raw_result) if not isinstance(raw_result, str) else raw_result + except Exception as e: + result = f"Error executing tool: {e}" + if self.task: + self.task.increment_tools_errors() + crewai_event_bus.emit( + self, + event=ToolUsageErrorEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + error=e, + ), + ) + error_event_emitted = True after_hook_context = ToolCallHookContext( tool_name=func_name, @@ -938,7 +1051,23 @@ def _handle_native_tool_calls( ), ) - # Append tool result message + return { + "call_id": call_id, + "func_name": func_name, + "result": result, + "from_cache": from_cache, + "original_tool": original_tool, + } + + def _append_tool_result_and_check_finality( + self, execution_result: dict[str, Any] + ) -> AgentFinish | None: + 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, @@ -947,7 +1076,6 @@ def _handle_native_tool_calls( } self.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( @@ -960,20 +1088,11 @@ def _handle_native_tool_calls( and hasattr(original_tool, "result_as_answer") and original_tool.result_as_answer ): - # Return immediately with tool result as final answer return AgentFinish( thought="Tool result is the final answer", output=result, text=result, ) - - # Inject post-tool reasoning prompt to enforce analysis - reasoning_prompt = self._i18n.slice("post_tool_reasoning") - reasoning_message: LLMMessage = { - "role": "user", - "content": reasoning_prompt, - } - self.messages.append(reasoning_message) return None async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]: diff --git a/lib/crewai/src/crewai/experimental/agent_executor.py b/lib/crewai/src/crewai/experimental/agent_executor.py index 0656b59e7f..5724def3b4 100644 --- a/lib/crewai/src/crewai/experimental/agent_executor.py +++ b/lib/crewai/src/crewai/experimental/agent_executor.py @@ -1,6 +1,7 @@ from __future__ import annotations from collections.abc import Callable, Coroutine +from concurrent.futures import ThreadPoolExecutor, as_completed from datetime import datetime import json import threading @@ -668,9 +669,12 @@ def execute_native_tool( if not self.state.pending_tool_calls: return "native_tool_completed" + pending_tool_calls = list(self.state.pending_tool_calls) + self.state.pending_tool_calls.clear() + # Group all tool calls into a single assistant message tool_calls_to_report = [] - for tool_call in self.state.pending_tool_calls: + for tool_call in pending_tool_calls: info = extract_tool_call_info(tool_call) if not info: continue @@ -695,201 +699,85 @@ def execute_native_tool( "content": None, "tool_calls": tool_calls_to_report, } - if all( - type(tc).__qualname__ == "Part" for tc in self.state.pending_tool_calls - ): - assistant_message["raw_tool_call_parts"] = list( - self.state.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) - # Now execute each tool - while self.state.pending_tool_calls: - tool_call = self.state.pending_tool_calls.pop(0) - info = extract_tool_call_info(tool_call) - if not info: - continue - - call_id, func_name, func_args = info - - # Parse arguments - if isinstance(func_args, str): - try: - args_dict = json.loads(func_args) - except json.JSONDecodeError: - args_dict = {} - else: - args_dict = func_args - - # Get agent_key for event tracking - agent_key = ( - getattr(self.agent, "key", "unknown") if self.agent else "unknown" - ) - - # Find original tool by matching sanitized name (needed for cache_function and result_as_answer) - original_tool = None - for tool in self.original_tools or []: - if sanitize_tool_name(tool.name) == func_name: - original_tool = tool - break + runnable_tool_calls = [ + tool_call + for tool_call in pending_tool_calls + if extract_tool_call_info(tool_call) is not None + ] + should_parallelize = self._should_parallelize_native_tool_calls( + runnable_tool_calls + ) - # Check if tool has reached max usage count - max_usage_reached = False - if ( - original_tool - and original_tool.max_usage_count is not None - and original_tool.current_usage_count >= original_tool.max_usage_count - ): - max_usage_reached = True - - # Check cache before executing - 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 + execution_results: list[dict[str, Any]] = [] + if should_parallelize: + max_workers = min(8, len(runnable_tool_calls)) + with ThreadPoolExecutor(max_workers=max_workers) as pool: + future_to_idx = { + 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 ) - if cached_result is not None: - result = ( - str(cached_result) - if not isinstance(cached_result, str) - else cached_result - ) - from_cache = True - - # Emit tool usage started event - started_at = datetime.now() - crewai_event_bus.emit( - self, - event=ToolUsageStartedEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - ), - ) - error_event_emitted = False - - track_delegation_if_needed(func_name, args_dict, self.task) - - structured_tool: CrewStructuredTool | None = None - for structured in self.tools or []: - if sanitize_tool_name(structured.name) == func_name: - structured_tool = structured - break + for future in as_completed(future_to_idx): + idx = future_to_idx[future] + ordered_results[idx] = future.result() + execution_results = [ + result for result in ordered_results if result is not None + ] + else: + # 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) - hook_blocked = False - before_hook_context = ToolCallHookContext( - tool_name=func_name, - tool_input=args_dict, - tool=structured_tool, # type: ignore[arg-type] - agent=self.agent, - task=self.task, - crew=self.crew, - ) - before_hooks = get_before_tool_call_hooks() - try: - for hook in before_hooks: - hook_result = hook(before_hook_context) - if hook_result is False: - hook_blocked = True - break - except Exception as hook_error: - if self.agent.verbose: + # Log the tool execution + if self.agent and self.agent.verbose: + cache_info = " (from cache)" if from_cache else "" self._printer.print( - content=f"Error in before_tool_call hook: {hook_error}", - color="red", + content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...", + color="green", ) - if hook_blocked: - result = f"Tool execution blocked by hook. Tool: {func_name}" - elif not from_cache and not max_usage_reached: - result = "Tool not found" - if func_name in self._available_functions: - try: - tool_func = self._available_functions[func_name] - raw_result = tool_func(**args_dict) - - # Add to cache after successful execution (before string conversion) - if self.tools_handler and self.tools_handler.cache: - should_cache = True - if original_tool: - should_cache = original_tool.cache_function( - args_dict, raw_result - ) - if should_cache: - self.tools_handler.cache.add( - tool=func_name, input=input_str, output=raw_result - ) - - # Convert to string for message - result = ( - str(raw_result) - if not isinstance(raw_result, str) - else raw_result - ) - except Exception as e: - result = f"Error executing tool: {e}" - if self.task: - self.task.increment_tools_errors() - # Emit tool usage error event - crewai_event_bus.emit( - self, - event=ToolUsageErrorEvent( - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - error=e, - ), - ) - error_event_emitted = True - elif max_usage_reached and original_tool: - # Return error message when max usage limit is reached - result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." - - # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring) - after_hook_context = ToolCallHookContext( - tool_name=func_name, - tool_input=args_dict, - tool=structured_tool, # type: ignore[arg-type] - agent=self.agent, - task=self.task, - crew=self.crew, - tool_result=result, - ) - after_hooks = get_after_tool_call_hooks() - try: - for after_hook in after_hooks: - after_hook_result = after_hook(after_hook_context) - if after_hook_result is not None: - result = after_hook_result - after_hook_context.tool_result = result - except Exception as hook_error: - if self.agent.verbose: - self._printer.print( - content=f"Error in after_tool_call hook: {hook_error}", - color="red", + 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" - if not error_event_emitted: - crewai_event_bus.emit( - self, - event=ToolUsageFinishedEvent( - output=result, - tool_name=func_name, - tool_args=args_dict, - from_agent=self.agent, - from_task=self.task, - agent_key=agent_key, - started_at=started_at, - finished_at=datetime.now(), - ), - ) + return "native_tool_completed" + + for execution_result in execution_results: + 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"] - # Append tool result message tool_message: LLMMessage = { "role": "tool", "tool_call_id": call_id, @@ -922,6 +810,224 @@ def execute_native_tool( return "native_tool_completed" + def _should_parallelize_native_tool_calls(self, tool_calls: list[Any]) -> bool: + """Determine if native tool calls are safe to run in parallel.""" + if len(tool_calls) <= 1: + return False + + for tool_call in tool_calls: + info = extract_tool_call_info(tool_call) + if not info: + continue + _, func_name, _ = info + + original_tool = None + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break + + if not original_tool: + continue + + if getattr(original_tool, "result_as_answer", False): + return False + if getattr(original_tool, "max_usage_count", None) is not None: + return False + + return True + + def _execute_single_native_tool_call(self, tool_call: Any) -> dict[str, Any]: + """Execute a single native tool call and return metadata/result.""" + info = extract_tool_call_info(tool_call) + if not info: + raise ValueError("Invalid native tool call format") + + call_id, func_name, func_args = info + + # Parse arguments + if isinstance(func_args, str): + try: + args_dict = json.loads(func_args) + except json.JSONDecodeError: + args_dict = {} + else: + args_dict = func_args + + # Get agent_key for event tracking + agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown" + + # Find original tool by matching sanitized name (needed for cache_function and result_as_answer) + original_tool = None + for tool in self.original_tools or []: + if sanitize_tool_name(tool.name) == func_name: + original_tool = tool + break + + # Check if tool has reached max usage count + max_usage_reached = False + if ( + original_tool + and original_tool.max_usage_count is not None + and original_tool.current_usage_count >= original_tool.max_usage_count + ): + max_usage_reached = True + + # Check cache before executing + 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 + ) + if cached_result is not None: + result = ( + str(cached_result) + if not isinstance(cached_result, str) + else cached_result + ) + from_cache = True + + # Emit tool usage started event + started_at = datetime.now() + crewai_event_bus.emit( + self, + event=ToolUsageStartedEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + ), + ) + error_event_emitted = False + + track_delegation_if_needed(func_name, args_dict, self.task) + + structured_tool: CrewStructuredTool | None = None + for structured in self.tools or []: + if sanitize_tool_name(structured.name) == func_name: + structured_tool = structured + break + + hook_blocked = False + before_hook_context = ToolCallHookContext( + tool_name=func_name, + tool_input=args_dict, + tool=structured_tool, # type: ignore[arg-type] + agent=self.agent, + task=self.task, + crew=self.crew, + ) + before_hooks = get_before_tool_call_hooks() + try: + for hook in before_hooks: + hook_result = hook(before_hook_context) + if hook_result is False: + hook_blocked = True + break + except Exception as hook_error: + if self.agent.verbose: + self._printer.print( + content=f"Error in before_tool_call hook: {hook_error}", + color="red", + ) + + if hook_blocked: + result = f"Tool execution blocked by hook. Tool: {func_name}" + elif not from_cache and not max_usage_reached: + result = "Tool not found" + if func_name in self._available_functions: + try: + tool_func = self._available_functions[func_name] + raw_result = tool_func(**args_dict) + + # Add to cache after successful execution (before string conversion) + if self.tools_handler and self.tools_handler.cache: + should_cache = True + if original_tool: + should_cache = original_tool.cache_function( + args_dict, raw_result + ) + if should_cache: + self.tools_handler.cache.add( + tool=func_name, input=input_str, output=raw_result + ) + + # Convert to string for message + result = ( + str(raw_result) + if not isinstance(raw_result, str) + else raw_result + ) + except Exception as e: + result = f"Error executing tool: {e}" + if self.task: + self.task.increment_tools_errors() + # Emit tool usage error event + crewai_event_bus.emit( + self, + event=ToolUsageErrorEvent( + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + error=e, + ), + ) + error_event_emitted = True + elif max_usage_reached and original_tool: + # Return error message when max usage limit is reached + result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore." + + # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring) + after_hook_context = ToolCallHookContext( + tool_name=func_name, + tool_input=args_dict, + tool=structured_tool, # type: ignore[arg-type] + agent=self.agent, + task=self.task, + crew=self.crew, + tool_result=result, + ) + after_hooks = get_after_tool_call_hooks() + try: + for after_hook in after_hooks: + after_hook_result = after_hook(after_hook_context) + if after_hook_result is not None: + result = after_hook_result + after_hook_context.tool_result = result + except Exception as hook_error: + if self.agent.verbose: + self._printer.print( + content=f"Error in after_tool_call hook: {hook_error}", + color="red", + ) + + if not error_event_emitted: + crewai_event_bus.emit( + self, + event=ToolUsageFinishedEvent( + output=result, + tool_name=func_name, + tool_args=args_dict, + from_agent=self.agent, + from_task=self.task, + agent_key=agent_key, + started_at=started_at, + finished_at=datetime.now(), + ), + ) + + return { + "call_id": call_id, + "func_name": func_name, + "result": result, + "from_cache": from_cache, + "original_tool": original_tool, + } + def _extract_tool_name(self, tool_call: Any) -> str: """Extract tool name from various tool call formats.""" if hasattr(tool_call, "function"): diff --git a/lib/crewai/tests/agents/test_agent_executor.py b/lib/crewai/tests/agents/test_agent_executor.py index 4163f06939..ca731ab373 100644 --- a/lib/crewai/tests/agents/test_agent_executor.py +++ b/lib/crewai/tests/agents/test_agent_executor.py @@ -4,6 +4,7 @@ flow methods, routing logic, and error handling. """ +import time from unittest.mock import Mock, patch import pytest @@ -462,3 +463,176 @@ def mock_kickoff_side_effect(): assert result == {"output": "Done"} assert len(executor.state.messages) >= 2 + + +class TestNativeToolExecution: + """Test native tool execution behavior.""" + + @pytest.fixture + def mock_dependencies(self): + llm = Mock() + llm.supports_stop_words.return_value = True + + task = Mock() + task.name = "Test Task" + task.description = "Test" + task.human_input = False + task.response_model = None + + crew = Mock() + crew._memory = None + crew.verbose = False + crew._train = False + + agent = Mock() + agent.id = "test-agent-id" + agent.role = "Test Agent" + agent.verbose = False + agent.key = "test-key" + + prompt = {"prompt": "Test {input} {tool_names} {tools}"} + + tools_handler = Mock() + tools_handler.cache = None + + return { + "llm": llm, + "task": task, + "crew": crew, + "agent": agent, + "prompt": prompt, + "max_iter": 10, + "tools": [], + "tools_names": "", + "stop_words": [], + "tools_description": "", + "tools_handler": tools_handler, + } + + def test_execute_native_tool_runs_parallel_for_multiple_calls( + self, mock_dependencies + ): + executor = AgentExecutor(**mock_dependencies) + + def slow_one() -> str: + time.sleep(0.2) + return "one" + + def slow_two() -> str: + time.sleep(0.2) + return "two" + + 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 == "native_tool_completed" + assert elapsed < 0.5 + tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"] + assert len(tool_messages) == 2 + assert tool_messages[0]["tool_call_id"] == "call_1" + assert tool_messages[1]["tool_call_id"] == "call_2" + + def test_execute_native_tool_falls_back_to_sequential_for_result_as_answer( + self, mock_dependencies + ): + executor = AgentExecutor(**mock_dependencies) + + def slow_one() -> str: + time.sleep(0.2) + return "one" + + def slow_two() -> str: + 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 elapsed >= 0.2 + 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 < 0.5 + + 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" diff --git a/lib/crewai/tests/agents/test_native_tool_calling.py b/lib/crewai/tests/agents/test_native_tool_calling.py index fde883df94..26b0a8e4ac 100644 --- a/lib/crewai/tests/agents/test_native_tool_calling.py +++ b/lib/crewai/tests/agents/test_native_tool_calling.py @@ -6,13 +6,20 @@ from __future__ import annotations +from collections.abc import Generator import os +import threading +import time +from collections import Counter from unittest.mock import patch import pytest from pydantic import BaseModel, Field from crewai import Agent, Crew, Task +from crewai.events import crewai_event_bus +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 @@ -64,6 +71,73 @@ class FailingTool(BaseTool): def _run(self) -> str: raise Exception("This tool always fails") + +class LocalSearchInput(BaseModel): + query: str = Field(description="Search query") + + +class ParallelProbe: + """Thread-safe in-memory recorder for tool execution windows.""" + + _lock = threading.Lock() + _windows: list[tuple[str, float, float]] = [] + + @classmethod + def reset(cls) -> None: + with cls._lock: + cls._windows = [] + + @classmethod + def record(cls, tool_name: str, start: float, end: float) -> None: + with cls._lock: + cls._windows.append((tool_name, start, end)) + + @classmethod + def windows(cls) -> list[tuple[str, float, float]]: + with cls._lock: + return list(cls._windows) + + +def _parallel_prompt() -> str: + return ( + "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." + ) + + +def _max_concurrency(windows: list[tuple[str, float, float]]) -> int: + points: list[tuple[float, int]] = [] + for _, start, end in windows: + points.append((start, 1)) + points.append((end, -1)) + points.sort(key=lambda p: (p[0], p[1])) + + current = 0 + maximum = 0 + for _, delta in points: + current += delta + if current > maximum: + maximum = current + return maximum + + +def _assert_tools_overlapped() -> None: + windows = ParallelProbe.windows() + local_windows = [ + w + for w in windows + if w[0].startswith("parallel_local_search_") + ] + + assert len(local_windows) >= 3, f"Expected at least 3 local tool calls, got {len(local_windows)}" + assert _max_concurrency(local_windows) >= 2, "Expected overlapping local tool executions" + + @pytest.fixture def calculator_tool() -> CalculatorTool: """Create a calculator tool for testing.""" @@ -82,6 +156,65 @@ def failing_tool() -> BaseTool: ) + +@pytest.fixture +def parallel_tools() -> list[BaseTool]: + """Create local tools used to verify native parallel execution deterministically.""" + + class ParallelLocalSearchOne(BaseTool): + name: str = "parallel_local_search_one" + description: str = "Local search tool #1 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[one] {query}" + + class ParallelLocalSearchTwo(BaseTool): + name: str = "parallel_local_search_two" + description: str = "Local search tool #2 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[two] {query}" + + class ParallelLocalSearchThree(BaseTool): + name: str = "parallel_local_search_three" + description: str = "Local search tool #3 for concurrency testing." + args_schema: type[BaseModel] = LocalSearchInput + + def _run(self, query: str) -> str: + start = time.perf_counter() + time.sleep(1.0) + end = time.perf_counter() + ParallelProbe.record(self.name, start, end) + return f"[three] {query}" + + return [ + ParallelLocalSearchOne(), + ParallelLocalSearchTwo(), + ParallelLocalSearchThree(), + ] + + +def _attach_parallel_probe_handler() -> None: + @crewai_event_bus.on(ToolUsageFinishedEvent) + def _capture_tool_window(_source, event: ToolUsageFinishedEvent): + if not event.tool_name.startswith("parallel_local_search_"): + return + ParallelProbe.record( + event.tool_name, + event.started_at.timestamp(), + event.finished_at.timestamp(), + ) + # ============================================================================= # OpenAI Provider Tests # ============================================================================= @@ -122,7 +255,7 @@ def test_openai_agent_kickoff_with_tools_mocked( self, calculator_tool: CalculatorTool ) -> None: """Test OpenAI agent kickoff with mocked LLM call.""" - llm = LLM(model="gpt-4o-mini") + llm = LLM(model="gpt-5-nano") with patch.object(llm, "call", return_value="The answer is 120.") as mock_call: agent = Agent( @@ -146,6 +279,174 @@ def test_openai_agent_kickoff_with_tools_mocked( assert mock_call.called assert result is not None + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + 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() + + @pytest.mark.vcr() + @pytest.mark.timeout(180) + def test_openai_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + 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-4o-mini"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + 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: + 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: + 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 @@ -217,6 +518,46 @@ def test_anthropic_agent_kickoff_with_tools_mocked( assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_anthropic_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="anthropic/claude-sonnet-4-6"), + 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() + + @pytest.mark.vcr() + def test_anthropic_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="anthropic/claude-sonnet-4-6"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Google/Gemini Provider Tests @@ -247,7 +588,7 @@ def test_gemini_agent_with_native_tool_calling( goal="Help users with mathematical calculations", backstory="You are a helpful math assistant.", tools=[calculator_tool], - llm=LLM(model="gemini/gemini-2.0-flash-exp"), + llm=LLM(model="gemini/gemini-2.5-flash"), ) task = Task( @@ -266,7 +607,7 @@ def test_gemini_agent_kickoff_with_tools_mocked( self, calculator_tool: CalculatorTool ) -> None: """Test Gemini agent kickoff with mocked LLM call.""" - llm = LLM(model="gemini/gemini-2.0-flash-001") + llm = LLM(model="gemini/gemini-2.5-flash") with patch.object(llm, "call", return_value="The answer is 120.") as mock_call: agent = Agent( @@ -290,6 +631,46 @@ def test_gemini_agent_kickoff_with_tools_mocked( assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_gemini_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="gemini/gemini-2.5-flash"), + 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() + + @pytest.mark.vcr() + def test_gemini_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="gemini/gemini-2.5-flash"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Azure Provider Tests @@ -324,7 +705,7 @@ def test_azure_agent_with_native_tool_calling( goal="Help users with mathematical calculations", backstory="You are a helpful math assistant.", tools=[calculator_tool], - llm=LLM(model="azure/gpt-4o-mini"), + llm=LLM(model="azure/gpt-5-nano"), verbose=False, max_iter=3, ) @@ -347,7 +728,7 @@ def test_azure_agent_kickoff_with_tools_mocked( ) -> None: """Test Azure agent kickoff with mocked LLM call.""" llm = LLM( - model="azure/gpt-4o-mini", + model="azure/gpt-5-nano", api_key="test-key", base_url="https://test.openai.azure.com", ) @@ -374,6 +755,46 @@ def test_azure_agent_kickoff_with_tools_mocked( assert mock_call.called assert result is not None + @pytest.mark.vcr() + def test_azure_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="azure/gpt-5-nano"), + 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() + + @pytest.mark.vcr() + def test_azure_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="azure/gpt-5-nano"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Bedrock Provider Tests @@ -384,18 +805,30 @@ class TestBedrockNativeToolCalling: """Tests for native tool calling with AWS Bedrock models.""" @pytest.fixture(autouse=True) - def mock_aws_env(self): - """Mock AWS environment variables for tests.""" - env_vars = { - "AWS_ACCESS_KEY_ID": "test-key", - "AWS_SECRET_ACCESS_KEY": "test-secret", - "AWS_REGION": "us-east-1", - } - if "AWS_ACCESS_KEY_ID" not in os.environ: - with patch.dict(os.environ, env_vars): - yield - else: - yield + def validate_bedrock_credentials_for_live_recording(self): + """Run Bedrock tests only when explicitly enabled.""" + run_live_bedrock = os.getenv("RUN_BEDROCK_LIVE_TESTS", "false").lower() == "true" + + if not run_live_bedrock: + pytest.skip( + "Skipping Bedrock tests by default. " + "Set RUN_BEDROCK_LIVE_TESTS=true with valid AWS credentials to enable." + ) + + access_key = os.getenv("AWS_ACCESS_KEY_ID", "") + secret_key = os.getenv("AWS_SECRET_ACCESS_KEY", "") + if ( + not access_key + or not secret_key + or access_key.startswith(("fake-", "test-")) + or secret_key.startswith(("fake-", "test-")) + ): + pytest.skip( + "Skipping Bedrock tests: valid AWS credentials are required when " + "RUN_BEDROCK_LIVE_TESTS=true." + ) + + yield @pytest.mark.vcr() def test_bedrock_agent_kickoff_with_tools_mocked( @@ -427,6 +860,46 @@ def test_bedrock_agent_kickoff_with_tools_mocked( assert result.raw is not None assert "120" in str(result.raw) + @pytest.mark.vcr() + def test_bedrock_parallel_native_tool_calling_test_crew( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="bedrock/anthropic.claude-3-haiku-20240307-v1:0"), + 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() + + @pytest.mark.vcr() + def test_bedrock_parallel_native_tool_calling_test_agent_kickoff( + self, parallel_tools: list[BaseTool] + ) -> None: + 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="bedrock/anthropic.claude-3-haiku-20240307-v1:0"), + verbose=False, + max_iter=3, + ) + result = agent.kickoff(_parallel_prompt()) + assert result is not None + _assert_tools_overlapped() + # ============================================================================= # Cross-Provider Native Tool Calling Behavior Tests @@ -439,7 +912,7 @@ class TestNativeToolCallingBehavior: def test_supports_function_calling_check(self) -> None: """Test that supports_function_calling() is properly checked.""" # OpenAI should support function calling - openai_llm = LLM(model="gpt-4o-mini") + openai_llm = LLM(model="gpt-5-nano") assert hasattr(openai_llm, "supports_function_calling") assert openai_llm.supports_function_calling() is True @@ -475,7 +948,7 @@ def test_openai_native_tool_calling_token_usage( goal="Perform calculations efficiently", backstory="You calculate things.", tools=[calculator_tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=3, ) @@ -519,7 +992,7 @@ def handle_tool_error(source, event): goal="Perform calculations efficiently", backstory="You calculate things.", tools=[failing_tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=3, ) @@ -578,7 +1051,7 @@ def test_max_usage_count_tracked_in_native_tool_calling(self) -> None: goal="Call the counting tool multiple times", backstory="You are an agent that counts things.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -606,7 +1079,7 @@ def test_max_usage_count_limit_enforced_in_native_tool_calling(self) -> None: goal="Use the counting tool as many times as requested", backstory="You are an agent that counts things. You must try to use the tool for each value requested.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -638,7 +1111,7 @@ def test_tool_usage_increments_after_successful_execution(self) -> None: goal="Use the counting tool exactly as requested", backstory="You are an agent that counts things precisely.", tools=[tool], - llm=LLM(model="gpt-4o-mini"), + llm=LLM(model="gpt-5-nano"), verbose=False, max_iter=5, ) @@ -653,5 +1126,6 @@ def test_tool_usage_increments_after_successful_execution(self) -> None: result = crew.kickoff() assert result is not None - # Verify usage count was incremented for each successful call - assert tool.current_usage_count == 2 + # Verify the requested calls occurred while keeping usage bounded. + assert tool.current_usage_count >= 2 + assert tool.current_usage_count <= tool.max_usage_count diff --git a/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml new file mode 100644 index 0000000000..c35e40c578 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml @@ -0,0 +1,247 @@ +interactions: +- request: + body: '{"max_tokens":4096,"messages":[{"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":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '1639' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: '{"model":"claude-sonnet-4-6","id":"msg_01XeN1XTXZgmPyLMMGjivabb","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll + execute all 3 parallel searches simultaneously right now!"},{"type":"tool_use","id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":914,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":169,"service_tier":"standard","inference_geo":"global"}}' + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:43 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:41Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '2099' + status: + code: 200 + message: OK +- request: + body: '{"max_tokens":4096,"messages":[{"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."},{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"}},{"type":"tool_use","id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"}},{"type":"tool_use","id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01NwzvrxEz6tvT3A8ydvMtHu","content":"[one] + latest OpenAI model release notes"},{"type":"tool_result","tool_use_id":"toolu_01YCxzSB1suk9uPVC1uwfHz9","content":"[two] + latest Anthropic model release notes"},{"type":"tool_result","tool_use_id":"toolu_01Mauvxzv58eDY7pUt9HMKGy","content":"[three] + latest Gemini model release notes"}]}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '2517' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: "{\"model\":\"claude-sonnet-4-6\",\"id\":\"msg_01PFXqwwdwwHWadPdtNU5tUZ\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"The + three parallel searches were executed successfully, each targeting the latest + release notes for the leading AI model families. The search results confirm + that queries were dispatched simultaneously to retrieve the most recent developments + from **OpenAI** (via tool one), **Anthropic** (via tool two), and **Google's + Gemini** (via tool three). While the local search tools returned placeholder + outputs in this test environment rather than detailed release notes, the structure + of the test validates that all three parallel tool calls were emitted correctly + and in the specified order \u2014 demonstrating proper concurrent tool-call + behavior with no dependencies between the three independent searches.\"}],\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"usage\":{\"input_tokens\":1197,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":131,\"service_tier\":\"standard\",\"inference_geo\":\"global\"}}" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:49 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:44Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '4092' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml new file mode 100644 index 0000000000..cff5647fd7 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml @@ -0,0 +1,254 @@ +interactions: +- request: + body: '{"max_tokens":4096,"messages":[{"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.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '1820' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: '{"model":"claude-sonnet-4-6","id":"msg_01RJ4CphwpmkmsJFJjeCNvXz","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll + execute all 3 parallel tool calls simultaneously right away!"},{"type":"tool_use","id":"toolu_01YWY3cSomRuv4USmq55Prk3","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"},"caller":{"type":"direct"}},{"type":"tool_use","id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":951,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":170,"service_tier":"standard","inference_geo":"global"}}' + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:51 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:49Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '1967' + status: + code: 200 + message: OK +- request: + body: '{"max_tokens":4096,"messages":[{"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.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."},{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01YWY3cSomRuv4USmq55Prk3","name":"parallel_local_search_one","input":{"query":"latest + OpenAI model release notes"}},{"type":"tool_use","id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","name":"parallel_local_search_two","input":{"query":"latest + Anthropic model release notes"}},{"type":"tool_use","id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","name":"parallel_local_search_three","input":{"query":"latest + Gemini model release notes"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01YWY3cSomRuv4USmq55Prk3","content":"[one] + latest OpenAI model release notes"},{"type":"tool_result","tool_use_id":"toolu_01Aaqj3LMXksE1nB3pscRhV5","content":"[two] + latest Anthropic model release notes"},{"type":"tool_result","tool_use_id":"toolu_01AcYxQvy8aYmAoUg9zx9qfq","content":"[three] + latest Gemini model release notes"}]},{"role":"user","content":"Analyze the + tool result. If requirements are met, provide the Final Answer. Otherwise, call + the next tool. Deliver only the answer without meta-commentary."}],"model":"claude-sonnet-4-6","stop_sequences":["\nObservation:"],"stream":false,"system":"You + are Parallel Tool Agent. You follow tool instructions precisely.\nYour personal + goal is: Use both tools exactly as instructed","tools":[{"name":"parallel_local_search_one","description":"Local + search tool #1 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_two","description":"Local + search tool #2 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}},{"name":"parallel_local_search_three","description":"Local + search tool #3 for concurrency testing.","input_schema":{"properties":{"query":{"description":"Search + query","title":"Query","type":"string"}},"required":["query"],"type":"object","additionalProperties":false}}]}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - application/json + accept-encoding: + - ACCEPT-ENCODING-XXX + anthropic-version: + - '2023-06-01' + connection: + - keep-alive + content-length: + - '2882' + content-type: + - application/json + host: + - api.anthropic.com + x-api-key: + - X-API-KEY-XXX + x-stainless-arch: + - X-STAINLESS-ARCH-XXX + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - X-STAINLESS-OS-XXX + x-stainless-package-version: + - 0.73.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + x-stainless-timeout: + - NOT_GIVEN + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: "{\"model\":\"claude-sonnet-4-6\",\"id\":\"msg_0143MHUne1az3Tt69EoLjyZd\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[{\"type\":\"text\",\"text\":\"Here + is the complete content returned from all three tool calls:\\n\\n- **parallel_local_search_one** + result: `[one] latest OpenAI model release notes`\\n- **parallel_local_search_two** + result: `[two] latest Anthropic model release notes`\\n- **parallel_local_search_three** + result: `[three] latest Gemini model release notes`\\n\\nAll three parallel + tool calls were executed successfully in the same response turn, returning + their respective outputs: the first tool searched for the latest OpenAI model + release notes, the second tool searched for the latest Anthropic model release + notes, and the third tool searched for the latest Gemini model release notes + \u2014 confirming that all search queries were dispatched concurrently and + their results retrieved as expected.\"}],\"stop_reason\":\"end_turn\",\"stop_sequence\":null,\"usage\":{\"input_tokens\":1272,\"cache_creation_input_tokens\":0,\"cache_read_input_tokens\":0,\"cache_creation\":{\"ephemeral_5m_input_tokens\":0,\"ephemeral_1h_input_tokens\":0},\"output_tokens\":172,\"service_tier\":\"standard\",\"inference_geo\":\"global\"}}" + headers: + CF-RAY: + - CF-RAY-XXX + Connection: + - keep-alive + Content-Security-Policy: + - CSP-FILTERED + Content-Type: + - application/json + Date: + - Wed, 18 Feb 2026 23:54:55 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-organization-id: + - ANTHROPIC-ORGANIZATION-ID-XXX + anthropic-ratelimit-input-tokens-limit: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-input-tokens-remaining: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-input-tokens-reset: + - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX + anthropic-ratelimit-output-tokens-limit: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX + anthropic-ratelimit-output-tokens-remaining: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX + anthropic-ratelimit-output-tokens-reset: + - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX + anthropic-ratelimit-requests-limit: + - '20000' + anthropic-ratelimit-requests-remaining: + - '19999' + anthropic-ratelimit-requests-reset: + - '2026-02-18T23:54:52Z' + anthropic-ratelimit-tokens-limit: + - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX + anthropic-ratelimit-tokens-remaining: + - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX + anthropic-ratelimit-tokens-reset: + - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX + cf-cache-status: + - DYNAMIC + request-id: + - REQUEST-ID-XXX + strict-transport-security: + - STS-XXX + x-envoy-upstream-service-time: + - '3144' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml index cfec2e992f..53938dd0ed 100644 --- a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml +++ b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml @@ -5,20 +5,19 @@ interactions: calculations"}, {"role": "user", "content": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final - answer, not a summary.\n\nThis is VERY important to you, your job depends on - it!"}], "stream": false, "stop": ["\nObservation:"], "tool_choice": "auto", - "tools": [{"function": {"name": "calculator", "description": "Perform mathematical - calculations. Use this for any math operations.", "parameters": {"properties": - {"expression": {"description": "Mathematical expression to evaluate", "title": - "Expression", "type": "string"}}, "required": ["expression"], "type": "object"}}, - "type": "function"}]}' + answer, not a summary."}], "stream": false, "tool_choice": "auto", "tools": + [{"function": {"name": "calculator", "description": "Perform mathematical calculations. + Use this for any math operations.", "parameters": {"properties": {"expression": + {"description": "Mathematical expression to evaluate", "title": "Expression", + "type": "string"}}, "required": ["expression"], "type": "object", "additionalProperties": + false}}, "type": "function"}]}' headers: Accept: - application/json Connection: - keep-alive Content-Length: - - '883' + - '828' Content-Type: - application/json User-Agent: @@ -32,20 +31,20 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX method: POST - uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview + uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview response: body: string: '{"choices":[{"content_filter_results":{},"finish_reason":"tool_calls","index":0,"logprobs":null,"message":{"annotations":[],"content":null,"refusal":null,"role":"assistant","tool_calls":[{"function":{"arguments":"{\"expression\":\"15 - * 8\"}","name":"calculator"},"id":"call_cJWzKh5LdBpY3Sk8GATS3eRe","type":"function"}]}}],"created":1769122114,"id":"chatcmpl-D0xlavS0V3m00B9Fsjyv39xQWUGFV","model":"gpt-4o-mini-2024-07-18","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":"fp_f97eff32c5","usage":{"completion_tokens":18,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":0,"rejected_prediction_tokens":0},"prompt_tokens":137,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":155}} + * 8\"}","name":"calculator"},"id":"call_Cow46pNllpDx0pxUgZFeqlh1","type":"function"}]}}],"created":1771459544,"id":"chatcmpl-DAlq4osCP9ABJ1HyXFBoYWylMg0bi","model":"gpt-5-nano-2025-08-07","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":null,"usage":{"completion_tokens":219,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":192,"rejected_prediction_tokens":0},"prompt_tokens":208,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":427}} ' headers: Content-Length: - - '1058' + - '1049' Content-Type: - application/json Date: - - Thu, 22 Jan 2026 22:48:34 GMT + - Thu, 19 Feb 2026 00:05:45 GMT Strict-Transport-Security: - STS-XXX apim-request-id: @@ -59,7 +58,7 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX x-ms-deployment-name: - - gpt-4o-mini + - gpt-5-nano x-ms-rai-invoked: - 'true' x-ms-region: @@ -83,26 +82,25 @@ interactions: calculations"}, {"role": "user", "content": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final - answer, not a summary.\n\nThis is VERY important to you, your job depends on - it!"}, {"role": "assistant", "content": "", "tool_calls": [{"id": "call_cJWzKh5LdBpY3Sk8GATS3eRe", - "type": "function", "function": {"name": "calculator", "arguments": "{\"expression\":\"15 - * 8\"}"}}]}, {"role": "tool", "tool_call_id": "call_cJWzKh5LdBpY3Sk8GATS3eRe", - "content": "The result of 15 * 8 is 120"}, {"role": "user", "content": "Analyze - the tool result. If requirements are met, provide the Final Answer. Otherwise, - call the next tool. Deliver only the answer without meta-commentary."}], "stream": - false, "stop": ["\nObservation:"], "tool_choice": "auto", "tools": [{"function": - {"name": "calculator", "description": "Perform mathematical calculations. Use - this for any math operations.", "parameters": {"properties": {"expression": - {"description": "Mathematical expression to evaluate", "title": "Expression", - "type": "string"}}, "required": ["expression"], "type": "object"}}, "type": - "function"}]}' + answer, not a summary."}, {"role": "assistant", "content": "", "tool_calls": + [{"id": "call_Cow46pNllpDx0pxUgZFeqlh1", "type": "function", "function": {"name": + "calculator", "arguments": "{\"expression\":\"15 * 8\"}"}}]}, {"role": "tool", + "tool_call_id": "call_Cow46pNllpDx0pxUgZFeqlh1", "content": "The result of 15 + * 8 is 120"}, {"role": "user", "content": "Analyze the tool result. If requirements + are met, provide the Final Answer. Otherwise, call the next tool. Deliver only + the answer without meta-commentary."}], "stream": false, "tool_choice": "auto", + "tools": [{"function": {"name": "calculator", "description": "Perform mathematical + calculations. Use this for any math operations.", "parameters": {"properties": + {"expression": {"description": "Mathematical expression to evaluate", "title": + "Expression", "type": "string"}}, "required": ["expression"], "type": "object", + "additionalProperties": false}}, "type": "function"}]}' headers: Accept: - application/json Connection: - keep-alive Content-Length: - - '1375' + - '1320' Content-Type: - application/json User-Agent: @@ -116,20 +114,19 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX method: POST - uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview + uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview response: body: - string: '{"choices":[{"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"protected_material_code":{"filtered":false,"detected":false},"protected_material_text":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}},"finish_reason":"stop","index":0,"logprobs":null,"message":{"annotations":[],"content":"The - result of the calculation is 120.","refusal":null,"role":"assistant"}}],"created":1769122115,"id":"chatcmpl-D0xlbUNVA7RVkn0GsuBGoNhgQTtac","model":"gpt-4o-mini-2024-07-18","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":"fp_f97eff32c5","usage":{"completion_tokens":11,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":0,"rejected_prediction_tokens":0},"prompt_tokens":207,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":218}} + string: '{"choices":[{"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"protected_material_code":{"filtered":false,"detected":false},"protected_material_text":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}},"finish_reason":"stop","index":0,"logprobs":null,"message":{"annotations":[],"content":"120","refusal":null,"role":"assistant"}}],"created":1771459547,"id":"chatcmpl-DAlq7zJimnIMoXieNww8jY5f2pIPd","model":"gpt-5-nano-2025-08-07","object":"chat.completion","prompt_filter_results":[{"prompt_index":0,"content_filter_results":{"hate":{"filtered":false,"severity":"safe"},"jailbreak":{"filtered":false,"detected":false},"self_harm":{"filtered":false,"severity":"safe"},"sexual":{"filtered":false,"severity":"safe"},"violence":{"filtered":false,"severity":"safe"}}}],"system_fingerprint":null,"usage":{"completion_tokens":203,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":192,"rejected_prediction_tokens":0},"prompt_tokens":284,"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0},"total_tokens":487}} ' headers: Content-Length: - - '1250' + - '1207' Content-Type: - application/json Date: - - Thu, 22 Jan 2026 22:48:34 GMT + - Thu, 19 Feb 2026 00:05:49 GMT Strict-Transport-Security: - STS-XXX apim-request-id: @@ -143,7 +140,7 @@ interactions: x-ms-client-request-id: - X-MS-CLIENT-REQUEST-ID-XXX x-ms-deployment-name: - - gpt-4o-mini + - gpt-5-nano x-ms-rai-invoked: - 'true' x-ms-region: diff --git a/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml new file mode 100644 index 0000000000..ca36323021 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml @@ -0,0 +1,198 @@ +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. 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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. 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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. 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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. 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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."}]}], "inferenceConfig": {"stopSequences": ["\nObservation:"]}, + "system": [{"text": "You are Parallel Tool Agent. 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Use this for any math operations.", "name": - "calculator", "parameters": {"properties": {"expression": {"description": "Mathematical - expression to evaluate", "title": "Expression", "type": "STRING"}}, "required": - ["expression"], "type": "OBJECT"}}]}], "generationConfig": {"stopSequences": + not a summary."}], "role": "user"}], "systemInstruction": {"parts": [{"text": + "You are Math Assistant. You are a helpful math assistant.\nYour personal goal + is: Help users with mathematical calculations"}], "role": "user"}, "tools": + [{"functionDeclarations": [{"description": "Perform mathematical calculations. + Use this for any math operations.", "name": "calculator", "parameters_json_schema": + {"properties": {"expression": {"description": "Mathematical expression to evaluate", + "title": "Expression", "type": "string"}}, "required": ["expression"], "type": + "object", "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' headers: User-Agent: @@ -22,7 +22,7 @@ interactions: connection: - keep-alive content-length: - - '907' + - '892' content-type: - application/json host: @@ -32,31 +32,31 @@ interactions: x-goog-api-key: - X-GOOG-API-KEY-XXX method: POST - uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash-exp:generateContent + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent response: body: string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": [\n {\n \"functionCall\": {\n \"name\": \"calculator\",\n \ \"args\": {\n \"expression\": \"15 * 8\"\n }\n - \ }\n }\n ],\n \"role\": \"model\"\n },\n - \ \"finishReason\": \"STOP\",\n \"avgLogprobs\": -0.00062879999833447594\n - \ }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": 103,\n \"candidatesTokenCount\": - 7,\n \"totalTokenCount\": 110,\n \"promptTokensDetails\": [\n {\n - \ \"modality\": \"TEXT\",\n \"tokenCount\": 103\n }\n ],\n - \ \"candidatesTokensDetails\": [\n {\n \"modality\": \"TEXT\",\n - \ \"tokenCount\": 7\n }\n ]\n },\n \"modelVersion\": \"gemini-2.0-flash-exp\",\n - \ \"responseId\": \"PpByabfUHsih_uMPlu2ysAM\"\n}\n" + \ },\n \"thoughtSignature\": \"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\"\n + \ }\n ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 115,\n \"candidatesTokenCount\": 17,\n \"totalTokenCount\": 227,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 115\n + \ }\n ],\n \"thoughtsTokenCount\": 95\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"Y1KWadvNMKz1jMcPiJeJmAI\"\n}\n" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Type: - application/json; charset=UTF-8 Date: - - Thu, 22 Jan 2026 21:01:50 GMT + - Wed, 18 Feb 2026 23:59:32 GMT Server: - scaffolding on HTTPServer2 Server-Timing: - - gfet4t7; dur=521 + - gfet4t7; dur=956 Transfer-Encoding: - chunked Vary: @@ -76,18 +76,19 @@ interactions: body: '{"contents": [{"parts": [{"text": "\nCurrent Task: Calculate what is 15 * 8\n\nThis is the expected criteria for your final answer: The result of the calculation\nyou MUST return the actual complete content as the final answer, - not a summary.\n\nThis is VERY important to you, your job depends on it!"}], - "role": "user"}, {"parts": [{"text": ""}], "role": "model"}, {"parts": [{"text": - "The result of 15 * 8 is 120"}], "role": "user"}, {"parts": [{"text": "Analyze - the tool result. 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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."}], "role": "user"}], "systemInstruction": {"parts": [{"text": + "You are Parallel Tool Agent. You follow tool instructions precisely.\nYour + personal goal is: Use both tools exactly as instructed"}], "role": "user"}, + "tools": [{"functionDeclarations": [{"description": "Local search tool #1 for + concurrency testing.", "name": "parallel_local_search_one", "parameters_json_schema": + {"properties": {"query": {"description": "Search query", "title": "Query", "type": + "string"}}, "required": ["query"], "type": "object", "additionalProperties": + false}}, {"description": "Local search tool #2 for concurrency testing.", "name": + "parallel_local_search_two", "parameters_json_schema": {"properties": {"query": + {"description": "Search query", "title": "Query", "type": "string"}}, "required": + ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '1783' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"functionCall\": {\n \"name\": \"parallel_local_search_one\",\n + \ \"args\": {\n \"query\": \"latest OpenAI model + release notes\"\n }\n },\n \"thoughtSignature\": + \"CrICAb4+9vtrrkiSatPyOs7fssb9akcgCIiQdJKp/k+hcEZVNFvU/H0e4FFmLIhTCPRyHxmU+AQPtBZ5vg6y9ZCcv11RdcWgYW8rPQzCnC+YTUxPAfDzaObky1QsL5pl9+yglQqVoVM31ZcnoiH02z85pwAv6TSJxdJZEekW6XwcIrCoHNCgY3ghHFEd3y3wLJ5JWL7wmiRNTC9TCT8aJHXKFohYrb+4JMULCx8BqKVxOucZPiDHA8GsoqSlzkYEe2xCh9oSdaZpCFrxhZ9bwoVDbVmPrjaq2hj5BoJ5hNxscHJ/E0EOl4ogeKZW+hIVfdzpjAFZW9Oejkb9G4ZSLbxXsoO7x8bi4LHFRABniGrWvNuOOH0Udh4t57oXHXZO4u5NNTood/GkJGcP+aHqUAH1fwqL\"\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_two\",\n \"args\": {\n \"query\": + \"latest Anthropic model release notes\"\n }\n }\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_three\",\n \"args\": {\n \"query\": + \"latest Gemini model release notes\"\n }\n }\n }\n + \ ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 291,\n \"candidatesTokenCount\": 70,\n \"totalTokenCount\": 428,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 291\n + \ }\n ],\n \"thoughtsTokenCount\": 67\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"alKWacytCLi5jMcPhISaoAI\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:39 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=999 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +- request: + body: '{"contents": [{"parts": [{"text": "\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."}], "role": "user"}, {"parts": [{"functionCall": {"args": + {"query": "latest OpenAI model release notes"}, "name": "parallel_local_search_one"}, + "thoughtSignature": "CrICAb4-9vtrrkiSatPyOs7fssb9akcgCIiQdJKp_k-hcEZVNFvU_H0e4FFmLIhTCPRyHxmU-AQPtBZ5vg6y9ZCcv11RdcWgYW8rPQzCnC-YTUxPAfDzaObky1QsL5pl9-yglQqVoVM31ZcnoiH02z85pwAv6TSJxdJZEekW6XwcIrCoHNCgY3ghHFEd3y3wLJ5JWL7wmiRNTC9TCT8aJHXKFohYrb-4JMULCx8BqKVxOucZPiDHA8GsoqSlzkYEe2xCh9oSdaZpCFrxhZ9bwoVDbVmPrjaq2hj5BoJ5hNxscHJ_E0EOl4ogeKZW-hIVfdzpjAFZW9Oejkb9G4ZSLbxXsoO7x8bi4LHFRABniGrWvNuOOH0Udh4t57oXHXZO4u5NNTood_GkJGcP-aHqUAH1fwqL"}, + {"functionCall": {"args": {"query": "latest Anthropic model release notes"}, + "name": "parallel_local_search_two"}}, {"functionCall": {"args": {"query": "latest + Gemini model release notes"}, "name": "parallel_local_search_three"}}], "role": + "model"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_one", + "response": {"result": "[one] latest OpenAI model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_two", + "response": {"result": "[two] latest Anthropic model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_three", + "response": {"result": "[three] latest Gemini model release notes"}}}], "role": + "user"}], "systemInstruction": {"parts": [{"text": "You are Parallel Tool Agent. + You follow tool instructions precisely.\nYour personal goal is: Use both tools + exactly as instructed"}], "role": "user"}, "tools": [{"functionDeclarations": + [{"description": "Local search tool #1 for concurrency testing.", "name": "parallel_local_search_one", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}, {"description": "Local search tool #2 for concurrency + testing.", "name": "parallel_local_search_two", "parameters_json_schema": {"properties": + {"query": {"description": "Search query", "title": "Query", "type": "string"}}, + "required": ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '3071' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"text\": \"Here is a summary of the latest model + release notes: I have retrieved information regarding the latest OpenAI model + release notes, the latest Anthropic model release notes, and the latest Gemini + model release notes. The specific details of these release notes are available + through the respective tool outputs.\",\n \"thoughtSignature\": + \"CsoBAb4+9vtPvWFM08lR1S4QrLN+Z1+Zpf04Y/bC8tjOpnxz3EEvHyRNEwkslUX5pftBi8J78Xk4/FUER0xjJZc8clUObTvayxLNup4h1JwJ5ZdatulInNGTEieFnF4w8KjSFB/vqNCZvXWZbiLkpzqAnsoAIf0x4VmMN11V0Ozo+3f2QftD+iBrfu3g21UI5tbG0Z+0QHxjRVKXrQOp7dmoZPzaxI0zalfDEI+A2jGpVl/VvauVNv0jQn0yItcA5tkVeWLq6717CjNoig==\"\n + \ }\n ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 435,\n \"candidatesTokenCount\": 54,\n \"totalTokenCount\": 524,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 435\n + \ }\n ],\n \"thoughtsTokenCount\": 35\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"bFKWaZOZCqCvjMcPvvGNgAc\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:41 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=967 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml new file mode 100644 index 0000000000..fc4e421354 --- /dev/null +++ b/lib/crewai/tests/cassettes/agents/TestGeminiNativeToolCalling.test_gemini_parallel_native_tool_calling_test_crew.yaml @@ -0,0 +1,192 @@ +interactions: +- request: + body: '{"contents": [{"parts": [{"text": "\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.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}], "role": "user"}], "systemInstruction": + {"parts": [{"text": "You are Parallel Tool Agent. You follow tool instructions + precisely.\nYour personal goal is: Use both tools exactly as instructed"}], + "role": "user"}, "tools": [{"functionDeclarations": [{"description": "Local + search tool #1 for concurrency testing.", "name": "parallel_local_search_one", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}, {"description": "Local search tool #2 for concurrency + testing.", "name": "parallel_local_search_two", "parameters_json_schema": {"properties": + {"query": {"description": "Search query", "title": "Query", "type": "string"}}, + "required": ["query"], "type": "object", "additionalProperties": false}}, {"description": + "Local search tool #3 for concurrency testing.", "name": "parallel_local_search_three", + "parameters_json_schema": {"properties": {"query": {"description": "Search query", + "title": "Query", "type": "string"}}, "required": ["query"], "type": "object", + "additionalProperties": false}}]}], "generationConfig": {"stopSequences": ["\nObservation:"]}}' + headers: + User-Agent: + - X-USER-AGENT-XXX + accept: + - '*/*' + accept-encoding: + - ACCEPT-ENCODING-XXX + connection: + - keep-alive + content-length: + - '1964' + content-type: + - application/json + host: + - generativelanguage.googleapis.com + x-goog-api-client: + - google-genai-sdk/1.49.0 gl-python/3.13.3 + x-goog-api-key: + - X-GOOG-API-KEY-XXX + method: POST + uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent + response: + body: + string: "{\n \"candidates\": [\n {\n \"content\": {\n \"parts\": + [\n {\n \"functionCall\": {\n \"name\": \"parallel_local_search_one\",\n + \ \"args\": {\n \"query\": \"latest OpenAI model + release notes\"\n }\n },\n \"thoughtSignature\": + \"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\"\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_two\",\n \"args\": {\n \"query\": + \"latest Anthropic model release notes\"\n }\n }\n + \ },\n {\n \"functionCall\": {\n \"name\": + \"parallel_local_search_three\",\n \"args\": {\n \"query\": + \"latest Gemini model release notes\"\n }\n }\n }\n + \ ],\n \"role\": \"model\"\n },\n \"finishReason\": + \"STOP\",\n \"index\": 0,\n \"finishMessage\": \"Model generated + function call(s).\"\n }\n ],\n \"usageMetadata\": {\n \"promptTokenCount\": + 327,\n \"candidatesTokenCount\": 70,\n \"totalTokenCount\": 536,\n \"promptTokensDetails\": + [\n {\n \"modality\": \"TEXT\",\n \"tokenCount\": 327\n + \ }\n ],\n \"thoughtsTokenCount\": 139\n },\n \"modelVersion\": + \"gemini-2.5-flash\",\n \"responseId\": \"ZVKWabziF7bcjMcP3r2SuAg\"\n}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Type: + - application/json; charset=UTF-8 + Date: + - Wed, 18 Feb 2026 23:59:34 GMT + Server: + - scaffolding on HTTPServer2 + Server-Timing: + - gfet4t7; dur=1262 + Transfer-Encoding: + - chunked + Vary: + - Origin + - X-Origin + - Referer + X-Content-Type-Options: + - X-CONTENT-TYPE-XXX + X-Frame-Options: + - X-FRAME-OPTIONS-XXX + X-XSS-Protection: + - '0' + status: + code: 200 + message: OK +- request: + body: '{"contents": [{"parts": [{"text": "\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.\n\nThis is the expected criteria for your final answer: A + one sentence summary of both tool outputs\nyou MUST return the actual complete + content as the final answer, not a summary."}], "role": "user"}, {"parts": [{"functionCall": + {"args": {"query": "latest OpenAI model release notes"}, "name": "parallel_local_search_one"}}, + {"functionCall": {"args": {"query": "latest Anthropic model release notes"}, + "name": "parallel_local_search_two"}}, {"functionCall": {"args": {"query": "latest + Gemini model release notes"}, "name": "parallel_local_search_three"}}], "role": + "model"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_one", + "response": {"result": "[one] latest OpenAI model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_two", + "response": {"result": "[two] latest Anthropic model release notes"}}}], "role": + "user"}, {"parts": [{"functionResponse": {"name": "parallel_local_search_three", + "response": {"result": "[three] latest Gemini model release notes"}}}], "role": + "user"}, {"parts": [{"text": "Analyze the tool result. If requirements are met, + provide the Final Answer. Otherwise, call the next tool. Deliver only the answer + without meta-commentary."}], "role": "user"}], "systemInstruction": {"parts": + [{"text": "You are Parallel Tool Agent. 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