mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-22 14:48:13 +00:00
Refactor AgentExecutor to support batch execution of native tool calls
- Updated the method to process all tools from in a single batch, enhancing efficiency and reducing the number of interactions with the LLM. - Introduced a new utility function to streamline the extraction of tool call details, improving compatibility with various tool formats. - Removed the parameter, simplifying the initialization of the . - Enhanced logging and message handling to provide clearer insights during tool execution. - This refactor improves the overall performance and usability of the agent execution flow.
This commit is contained in:
lorenzejay
@@ -50,6 +50,7 @@ from crewai.utilities.agent_utils import (
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is_context_length_exceeded,
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is_inside_event_loop,
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process_llm_response,
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extract_tool_call_info,
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)
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from crewai.utilities.constants import TRAINING_DATA_FILE
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from crewai.utilities.i18n import I18N, get_i18n
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@@ -124,7 +125,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
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callbacks: list[Any] | None = None,
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response_model: type[BaseModel] | None = None,
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i18n: I18N | None = None,
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max_tools_per_turn: int = 10,
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) -> None:
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"""Initialize the flow-based agent executor.
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@@ -169,7 +169,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
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self.respect_context_window = respect_context_window
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self.request_within_rpm_limit = request_within_rpm_limit
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self.response_model = response_model
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self.max_tools_per_turn = max_tools_per_turn
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self.log_error_after = 3
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self._console: Console = Console()
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@@ -482,6 +481,7 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
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if isinstance(answer, list) and answer and self._is_tool_call_list(answer):
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# Store tool calls for sequential processing
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self.state.pending_tool_calls = list(answer)
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iteration_elapsed = time.time() - iteration_start
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print(
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f"[{time.strftime('%H:%M:%S')}] -> Routing to native_tool_calls ({len(answer)} tools)"
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@@ -608,55 +608,23 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
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@listen("native_tool_calls")
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def execute_native_tool(self) -> Literal["native_tool_completed"]:
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"""Execute a SINGLE native tool call with reflection after.
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"""Execute native tool calls in a batch.
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Processes only the first tool from pending_tool_calls, then asks
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the LLM if it can answer the task. Remaining tools stay in the queue
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for potential execution on next iteration.
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Processes all tools from pending_tool_calls, executes them,
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and appends results to the conversation history.
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"""
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if not self.state.pending_tool_calls:
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return "native_tool_completed"
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# Pop just the first tool (leave the rest in queue for potential continuation)
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tool_call = self.state.pending_tool_calls.pop(0)
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print(
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f"Executing 1 tool, {len(self.state.pending_tool_calls)} remaining in queue"
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)
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# Group all tool calls into a single assistant message
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tool_calls_to_report = []
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for tool_call in self.state.pending_tool_calls:
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info = extract_tool_call_info(tool_call)
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if not info:
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continue
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# Extract tool call info - handle OpenAI, Anthropic, and Gemini formats
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if hasattr(tool_call, "function"):
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# OpenAI format: has .function.name and .function.arguments
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call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
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func_name = tool_call.function.name
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func_args = tool_call.function.arguments
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elif hasattr(tool_call, "function_call") and tool_call.function_call:
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# Gemini format: has .function_call.name and .function_call.args
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call_id = f"call_{id(tool_call)}"
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func_name = tool_call.function_call.name
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func_args = (
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dict(tool_call.function_call.args)
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if tool_call.function_call.args
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else {}
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)
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elif hasattr(tool_call, "name") and hasattr(tool_call, "input"):
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# Anthropic format: has .name and .input (ToolUseBlock)
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call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
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func_name = tool_call.name
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func_args = tool_call.input # Already a dict in Anthropic
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elif isinstance(tool_call, dict):
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call_id = tool_call.get("id", f"call_{id(tool_call)}")
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func_info = tool_call.get("function", {})
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func_name = func_info.get("name", "") or tool_call.get("name", "")
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func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
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else:
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# Unrecognized format - skip and try next
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return "native_tool_completed"
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# Append assistant message with single tool call
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assistant_message: LLMMessage = {
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"role": "assistant",
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"content": None,
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"tool_calls": [
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call_id, func_name, func_args = info
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tool_calls_to_report.append(
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{
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"id": call_id,
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"type": "function",
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@@ -667,90 +635,97 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
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else json.dumps(func_args),
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},
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}
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],
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}
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self.state.messages.append(assistant_message)
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# Parse arguments for the single tool call
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if isinstance(func_args, str):
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try:
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args_dict = json.loads(func_args)
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except json.JSONDecodeError:
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args_dict = {}
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else:
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args_dict = func_args
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# Emit tool usage started event
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started_at = datetime.now()
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crewai_event_bus.emit(
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self,
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event=ToolUsageStartedEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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),
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)
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# Execute the tool
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result = "Tool not found"
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if func_name in self._available_functions:
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try:
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tool_func = self._available_functions[func_name]
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result = tool_func(**args_dict)
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if not isinstance(result, str):
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result = str(result)
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except Exception as e:
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result = f"Error executing tool: {e}"
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# Emit tool usage finished event
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crewai_event_bus.emit(
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self,
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event=ToolUsageFinishedEvent(
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output=result,
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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started_at=started_at,
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finished_at=datetime.now(),
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),
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)
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# Append tool result message
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tool_message: LLMMessage = {
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"role": "tool",
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"tool_call_id": call_id,
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"content": result,
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}
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self.state.messages.append(tool_message)
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# Log the tool execution
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if self.agent and self.agent.verbose:
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self._printer.print(
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content=f"Tool {func_name} executed with result: {result[:200]}...",
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color="green",
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)
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# Only add reflection prompt if there are still pending tools
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# If no pending tools, skip reflection - LLM will naturally continue
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if self.state.pending_tool_calls:
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print("--------------------------------")
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print(
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f"REFLECTION: {len(self.state.pending_tool_calls)} tools pending - asking LLM to decide"
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)
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print("--------------------------------")
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reasoning_prompt = self._i18n.slice("post_tool_reasoning")
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reasoning_message: LLMMessage = {
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"role": "user",
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"content": reasoning_prompt,
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if tool_calls_to_report:
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assistant_message: LLMMessage = {
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"role": "assistant",
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"content": None,
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"tool_calls": tool_calls_to_report,
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}
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self.state.messages.append(reasoning_message)
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else:
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print("--------------------------------")
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print("SKIPPING REFLECTION: No pending tools - LLM will continue naturally")
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print("--------------------------------")
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self.state.messages.append(assistant_message)
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# Now execute each tool
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while self.state.pending_tool_calls:
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tool_call = self.state.pending_tool_calls.pop(0)
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info = extract_tool_call_info(tool_call)
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if not info:
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continue
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call_id, func_name, func_args = info
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# Parse arguments
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if isinstance(func_args, str):
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try:
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args_dict = json.loads(func_args)
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except json.JSONDecodeError:
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args_dict = {}
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else:
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args_dict = func_args
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# Emit tool usage started event
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started_at = datetime.now()
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crewai_event_bus.emit(
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self,
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event=ToolUsageStartedEvent(
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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),
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)
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# Execute the tool
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result = "Tool not found"
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if func_name in self._available_functions:
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try:
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tool_func = self._available_functions[func_name]
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result = tool_func(**args_dict)
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if not isinstance(result, str):
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result = str(result)
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except Exception as e:
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result = f"Error executing tool: {e}"
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# Emit tool usage finished event
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crewai_event_bus.emit(
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self,
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event=ToolUsageFinishedEvent(
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output=result,
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tool_name=func_name,
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tool_args=args_dict,
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from_agent=self.agent,
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from_task=self.task,
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started_at=started_at,
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finished_at=datetime.now(),
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),
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)
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# Append tool result message
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tool_message: LLMMessage = {
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"role": "tool",
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"tool_call_id": call_id,
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"content": result,
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}
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self.state.messages.append(tool_message)
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# Log the tool execution
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if self.agent and self.agent.verbose:
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self._printer.print(
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content=f"Tool {func_name} executed with result: {result[:200]}...",
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color="green",
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)
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# Add reflection prompt once after all tools in the batch
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print("--------------------------------")
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print("BATCH COMPLETED: All pending tools executed - adding reflection prompt")
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print("--------------------------------")
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reasoning_prompt = self._i18n.slice("post_tool_reasoning")
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reasoning_message: LLMMessage = {
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"role": "user",
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"content": reasoning_prompt,
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}
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self.state.messages.append(reasoning_message)
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return "native_tool_completed"
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