From b9dd166a6b1697e7bb61bf181d6812693ddbc19f Mon Sep 17 00:00:00 2001 From: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Date: Sun, 28 Dec 2025 10:21:32 -0800 Subject: [PATCH] Lorenze/agent executor flow pattern (#3975) * WIP gh pr refactor: update agent executor handling and introduce flow-based executor * wip * refactor: clean up comments and improve code clarity in agent executor flow - Removed outdated comments and unnecessary explanations in and classes to enhance code readability. - Simplified parameter updates in the agent executor to avoid confusion regarding executor recreation. - Improved clarity in the method to ensure proper handling of non-final answers without raising errors. * bumping pytest-randomly numpy * also bump versions of anthropic sdk * ensure flow logs are not passed if its on executor * revert anthropic bump * fix * refactor: update dependency markers in uv.lock for platform compatibility - Enhanced dependency markers for , , , and others to ensure compatibility across different platforms (Linux, Darwin, and architecture-specific conditions). - Removed unnecessary event emission in the class during kickoff. - Cleaned up commented-out code in the class for better readability and maintainability. * drop dupllicate * test: enhance agent executor creation and stop word assertions - Added calls to create_agent_executor in multiple test cases to ensure proper agent execution setup. - Updated assertions for stop words in the agent tests to remove unnecessary checks and improve clarity. - Ensured consistency in task handling by invoking create_agent_executor with the appropriate task parameter. * refactor: reorganize agent executor imports and introduce CrewAgentExecutorFlow - Removed the old import of CrewAgentExecutorFlow and replaced it with the new import from the experimental module. - Updated relevant references in the codebase to ensure compatibility with the new structure. - Enhanced the organization of imports in core.py and base_agent.py for better clarity and maintainability. * updating name * dropped usage of printer here for rich console and dropped non-added value logging * address i18n * Enhance concurrency control in CrewAgentExecutorFlow by introducing a threading lock to prevent concurrent executions. This change ensures that the executor instance cannot be invoked while already running, improving stability and reliability during flow execution. * string literal returns * string literal returns * Enhance CrewAgentExecutor initialization by allowing optional i18n parameter for improved internationalization support. This change ensures that the executor can utilize a provided i18n instance or fallback to the default, enhancing flexibility in multilingual contexts. --------- Co-authored-by: Greyson LaLonde --- lib/crewai/src/crewai/agent/core.py | 105 +- .../crewai/agents/agent_builder/base_agent.py | 2 - .../src/crewai/agents/crew_agent_executor.py | 3 +- .../src/crewai/experimental/__init__.py | 2 + .../experimental/crew_agent_executor_flow.py | 808 +++++++++++++++ .../evaluation/agent_evaluator.py | 9 +- .../experimental/evaluation/base_evaluator.py | 9 +- .../evaluation/experiment/runner.py | 10 +- .../evaluation/metrics/goal_metrics.py | 9 +- .../evaluation/metrics/reasoning_metrics.py | 9 +- .../metrics/semantic_quality_metrics.py | 9 +- .../evaluation/metrics/tools_metrics.py | 11 +- lib/crewai/src/crewai/flow/flow.py | 150 +-- lib/crewai/tests/agents/test_agent.py | 12 +- .../agents/test_crew_agent_executor_flow.py | 479 +++++++++ uv.lock | 978 +++++++++--------- 16 files changed, 2010 insertions(+), 595 deletions(-) create mode 100644 lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py create mode 100644 lib/crewai/tests/agents/test_crew_agent_executor_flow.py diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py index f59724343..3658a330b 100644 --- a/lib/crewai/src/crewai/agent/core.py +++ b/lib/crewai/src/crewai/agent/core.py @@ -1,7 +1,7 @@ from __future__ import annotations import asyncio -from collections.abc import Sequence +from collections.abc import Callable, Sequence import shutil import subprocess import time @@ -44,6 +44,7 @@ from crewai.events.types.memory_events import ( MemoryRetrievalCompletedEvent, MemoryRetrievalStartedEvent, ) +from crewai.experimental.crew_agent_executor_flow import CrewAgentExecutorFlow from crewai.knowledge.knowledge import Knowledge from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.lite_agent import LiteAgent @@ -105,7 +106,7 @@ class Agent(BaseAgent): The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents. Attributes: - agent_executor: An instance of the CrewAgentExecutor class. + agent_executor: An instance of the CrewAgentExecutor or CrewAgentExecutorFlow class. role: The role of the agent. goal: The objective of the agent. backstory: The backstory of the agent. @@ -221,6 +222,10 @@ class Agent(BaseAgent): default=None, description="A2A (Agent-to-Agent) configuration for delegating tasks to remote agents. Can be a single A2AConfig or a dict mapping agent IDs to configs.", ) + executor_class: type[CrewAgentExecutor] | type[CrewAgentExecutorFlow] = Field( + default=CrewAgentExecutor, + description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use CrewAgentExecutorFlow.", + ) @model_validator(mode="before") def validate_from_repository(cls, v: Any) -> dict[str, Any] | None | Any: # noqa: N805 @@ -721,29 +726,83 @@ class Agent(BaseAgent): self.response_template.split("{{ .Response }}")[1].strip() ) - self.agent_executor = CrewAgentExecutor( - llm=self.llm, # type: ignore[arg-type] - task=task, # type: ignore[arg-type] - agent=self, - crew=self.crew, - tools=parsed_tools, - prompt=prompt, - original_tools=raw_tools, - stop_words=stop_words, - max_iter=self.max_iter, - tools_handler=self.tools_handler, - tools_names=get_tool_names(parsed_tools), - tools_description=render_text_description_and_args(parsed_tools), - step_callback=self.step_callback, - function_calling_llm=self.function_calling_llm, - respect_context_window=self.respect_context_window, - request_within_rpm_limit=( - self._rpm_controller.check_or_wait if self._rpm_controller else None - ), - callbacks=[TokenCalcHandler(self._token_process)], - response_model=task.response_model if task else None, + rpm_limit_fn = ( + self._rpm_controller.check_or_wait if self._rpm_controller else None ) + if self.agent_executor is not None: + self._update_executor_parameters( + task=task, + tools=parsed_tools, + raw_tools=raw_tools, + prompt=prompt, + stop_words=stop_words, + rpm_limit_fn=rpm_limit_fn, + ) + else: + self.agent_executor = self.executor_class( + llm=cast(BaseLLM, self.llm), + task=task, + i18n=self.i18n, + agent=self, + crew=self.crew, + tools=parsed_tools, + prompt=prompt, + original_tools=raw_tools, + stop_words=stop_words, + max_iter=self.max_iter, + tools_handler=self.tools_handler, + tools_names=get_tool_names(parsed_tools), + tools_description=render_text_description_and_args(parsed_tools), + step_callback=self.step_callback, + function_calling_llm=self.function_calling_llm, + respect_context_window=self.respect_context_window, + request_within_rpm_limit=rpm_limit_fn, + callbacks=[TokenCalcHandler(self._token_process)], + response_model=task.response_model if task else None, + ) + + def _update_executor_parameters( + self, + task: Task | None, + tools: list, + raw_tools: list[BaseTool], + prompt: dict, + stop_words: list[str], + rpm_limit_fn: Callable | None, + ) -> None: + """Update executor parameters without recreating instance. + + Args: + task: Task to execute. + tools: Parsed tools. + raw_tools: Original tools. + prompt: Generated prompt. + stop_words: Stop words list. + rpm_limit_fn: RPM limit callback function. + """ + self.agent_executor.task = task + self.agent_executor.tools = tools + self.agent_executor.original_tools = raw_tools + self.agent_executor.prompt = prompt + self.agent_executor.stop = stop_words + self.agent_executor.tools_names = get_tool_names(tools) + self.agent_executor.tools_description = render_text_description_and_args(tools) + self.agent_executor.response_model = task.response_model if task else None + + self.agent_executor.tools_handler = self.tools_handler + self.agent_executor.request_within_rpm_limit = rpm_limit_fn + + if self.agent_executor.llm: + existing_stop = getattr(self.agent_executor.llm, "stop", []) + self.agent_executor.llm.stop = list( + set( + existing_stop + stop_words + if isinstance(existing_stop, list) + else stop_words + ) + ) + def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]: agent_tools = AgentTools(agents=agents) return agent_tools.tools() diff --git a/lib/crewai/src/crewai/agents/agent_builder/base_agent.py b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py index 6a3262bfb..93907c7c1 100644 --- a/lib/crewai/src/crewai/agents/agent_builder/base_agent.py +++ b/lib/crewai/src/crewai/agents/agent_builder/base_agent.py @@ -457,7 +457,6 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta): if self.cache: self.cache_handler = cache_handler self.tools_handler.cache = cache_handler - self.create_agent_executor() def set_rpm_controller(self, rpm_controller: RPMController) -> None: """Set the rpm controller for the agent. @@ -467,7 +466,6 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta): """ if not self._rpm_controller: self._rpm_controller = rpm_controller - self.create_agent_executor() def set_knowledge(self, crew_embedder: EmbedderConfig | None = None) -> None: pass diff --git a/lib/crewai/src/crewai/agents/crew_agent_executor.py b/lib/crewai/src/crewai/agents/crew_agent_executor.py index 580119a99..e11e3e80a 100644 --- a/lib/crewai/src/crewai/agents/crew_agent_executor.py +++ b/lib/crewai/src/crewai/agents/crew_agent_executor.py @@ -91,6 +91,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): request_within_rpm_limit: Callable[[], bool] | None = None, callbacks: list[Any] | None = None, response_model: type[BaseModel] | None = None, + i18n: I18N | None = None, ) -> None: """Initialize executor. @@ -114,7 +115,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin): callbacks: Optional callbacks list. response_model: Optional Pydantic model for structured outputs. """ - self._i18n: I18N = get_i18n() + self._i18n: I18N = i18n or get_i18n() self.llm = llm self.task = task self.agent = agent diff --git a/lib/crewai/src/crewai/experimental/__init__.py b/lib/crewai/src/crewai/experimental/__init__.py index 09e34820d..9507095ff 100644 --- a/lib/crewai/src/crewai/experimental/__init__.py +++ b/lib/crewai/src/crewai/experimental/__init__.py @@ -1,3 +1,4 @@ +from crewai.experimental.crew_agent_executor_flow import CrewAgentExecutorFlow from crewai.experimental.evaluation import ( AgentEvaluationResult, AgentEvaluator, @@ -23,6 +24,7 @@ __all__ = [ "AgentEvaluationResult", "AgentEvaluator", "BaseEvaluator", + "CrewAgentExecutorFlow", "EvaluationScore", "EvaluationTraceCallback", "ExperimentResult", diff --git a/lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py b/lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py new file mode 100644 index 000000000..7111c97ab --- /dev/null +++ b/lib/crewai/src/crewai/experimental/crew_agent_executor_flow.py @@ -0,0 +1,808 @@ +from __future__ import annotations + +from collections.abc import Callable +import threading +from typing import TYPE_CHECKING, Any, Literal, cast +from uuid import uuid4 + +from pydantic import BaseModel, Field, GetCoreSchemaHandler +from pydantic_core import CoreSchema, core_schema +from rich.console import Console +from rich.text import Text + +from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin +from crewai.agents.parser import ( + AgentAction, + AgentFinish, + OutputParserError, +) +from crewai.events.event_bus import crewai_event_bus +from crewai.events.types.logging_events import ( + AgentLogsExecutionEvent, + AgentLogsStartedEvent, +) +from crewai.flow.flow import Flow, listen, or_, router, start +from crewai.hooks.llm_hooks import ( + get_after_llm_call_hooks, + get_before_llm_call_hooks, +) +from crewai.utilities.agent_utils import ( + enforce_rpm_limit, + format_message_for_llm, + get_llm_response, + handle_agent_action_core, + handle_context_length, + handle_max_iterations_exceeded, + handle_output_parser_exception, + handle_unknown_error, + has_reached_max_iterations, + is_context_length_exceeded, + process_llm_response, +) +from crewai.utilities.constants import TRAINING_DATA_FILE +from crewai.utilities.i18n import I18N, get_i18n +from crewai.utilities.printer import Printer +from crewai.utilities.tool_utils import execute_tool_and_check_finality +from crewai.utilities.training_handler import CrewTrainingHandler +from crewai.utilities.types import LLMMessage + + +if TYPE_CHECKING: + from crewai.agent import Agent + from crewai.agents.tools_handler import ToolsHandler + from crewai.crew import Crew + from crewai.llms.base_llm import BaseLLM + from crewai.task import Task + from crewai.tools.base_tool import BaseTool + from crewai.tools.structured_tool import CrewStructuredTool + from crewai.tools.tool_types import ToolResult + from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult + + +class AgentReActState(BaseModel): + """Structured state for agent ReAct flow execution. + + Replaces scattered instance variables with validated immutable state. + Maps to: self.messages, self.iterations, formatted_answer in current executor. + """ + + messages: list[LLMMessage] = Field(default_factory=list) + iterations: int = Field(default=0) + current_answer: AgentAction | AgentFinish | None = Field(default=None) + is_finished: bool = Field(default=False) + ask_for_human_input: bool = Field(default=False) + + +class CrewAgentExecutorFlow(Flow[AgentReActState], CrewAgentExecutorMixin): + """Flow-based executor matching CrewAgentExecutor interface. + + Inherits from: + - Flow[AgentReActState]: Provides flow orchestration capabilities + - CrewAgentExecutorMixin: Provides memory methods (short/long/external term) + + Note: Multiple instances may be created during agent initialization + (cache setup, RPM controller setup, etc.) but only the final instance + should execute tasks via invoke(). + """ + + def __init__( + self, + llm: BaseLLM, + task: Task, + crew: Crew, + agent: Agent, + prompt: SystemPromptResult | StandardPromptResult, + max_iter: int, + tools: list[CrewStructuredTool], + tools_names: str, + stop_words: list[str], + tools_description: str, + tools_handler: ToolsHandler, + step_callback: Any = None, + original_tools: list[BaseTool] | None = None, + function_calling_llm: BaseLLM | Any | None = None, + respect_context_window: bool = False, + request_within_rpm_limit: Callable[[], bool] | None = None, + callbacks: list[Any] | None = None, + response_model: type[BaseModel] | None = None, + i18n: I18N | None = None, + ) -> None: + """Initialize the flow-based agent executor. + + Args: + llm: Language model instance. + task: Task to execute. + crew: Crew instance. + agent: Agent to execute. + prompt: Prompt templates. + max_iter: Maximum iterations. + tools: Available tools. + tools_names: Tool names string. + stop_words: Stop word list. + tools_description: Tool descriptions. + tools_handler: Tool handler instance. + step_callback: Optional step callback. + original_tools: Original tool list. + function_calling_llm: Optional function calling LLM. + respect_context_window: Respect context limits. + request_within_rpm_limit: RPM limit check function. + callbacks: Optional callbacks list. + response_model: Optional Pydantic model for structured outputs. + """ + self._i18n: I18N = i18n or get_i18n() + self.llm = llm + self.task = task + self.agent = agent + self.crew = crew + self.prompt = prompt + self.tools = tools + self.tools_names = tools_names + self.stop = stop_words + self.max_iter = max_iter + self.callbacks = callbacks or [] + self._printer: Printer = Printer() + self.tools_handler = tools_handler + self.original_tools = original_tools or [] + self.step_callback = step_callback + self.tools_description = tools_description + self.function_calling_llm = function_calling_llm + self.respect_context_window = respect_context_window + self.request_within_rpm_limit = request_within_rpm_limit + self.response_model = response_model + self.log_error_after = 3 + self._console: Console = Console() + + # Error context storage for recovery + self._last_parser_error: OutputParserError | None = None + self._last_context_error: Exception | None = None + + # Execution guard to prevent concurrent/duplicate executions + self._execution_lock = threading.Lock() + self._is_executing: bool = False + self._has_been_invoked: bool = False + self._flow_initialized: bool = False + + self._instance_id = str(uuid4())[:8] + + self.before_llm_call_hooks: list[Callable] = [] + self.after_llm_call_hooks: list[Callable] = [] + self.before_llm_call_hooks.extend(get_before_llm_call_hooks()) + self.after_llm_call_hooks.extend(get_after_llm_call_hooks()) + + if self.llm: + existing_stop = getattr(self.llm, "stop", []) + self.llm.stop = list( + set( + existing_stop + self.stop + if isinstance(existing_stop, list) + else self.stop + ) + ) + + self._state = AgentReActState() + + def _ensure_flow_initialized(self) -> None: + """Ensure Flow.__init__() has been called. + + This is deferred from __init__ to prevent FlowCreatedEvent emission + during agent setup when multiple executor instances are created. + Only the instance that actually executes via invoke() will emit events. + """ + if not self._flow_initialized: + # Now call Flow's __init__ which will replace self._state + # with Flow's managed state. Suppress flow events since this is + # an agent executor, not a user-facing flow. + super().__init__( + suppress_flow_events=True, + ) + self._flow_initialized = True + + @property + def use_stop_words(self) -> bool: + """Check to determine if stop words are being used. + + Returns: + bool: True if stop words should be used. + """ + return self.llm.supports_stop_words() if self.llm else False + + @property + def state(self) -> AgentReActState: + """Get state - returns temporary state if Flow not yet initialized. + + Flow initialization is deferred to prevent event emission during agent setup. + Returns the temporary state until invoke() is called. + """ + return self._state + + @property + def messages(self) -> list[LLMMessage]: + """Compatibility property for mixin - returns state messages.""" + return self._state.messages + + @property + def iterations(self) -> int: + """Compatibility property for mixin - returns state iterations.""" + return self._state.iterations + + @start() + def initialize_reasoning(self) -> Literal["initialized"]: + """Initialize the reasoning flow and emit agent start logs.""" + self._show_start_logs() + return "initialized" + + @listen("force_final_answer") + def force_final_answer(self) -> Literal["agent_finished"]: + """Force agent to provide final answer when max iterations exceeded.""" + formatted_answer = handle_max_iterations_exceeded( + formatted_answer=None, + printer=self._printer, + i18n=self._i18n, + messages=list(self.state.messages), + llm=self.llm, + callbacks=self.callbacks, + ) + + self.state.current_answer = formatted_answer + self.state.is_finished = True + + return "agent_finished" + + @listen("continue_reasoning") + def call_llm_and_parse(self) -> Literal["parsed", "parser_error", "context_error"]: + """Execute LLM call with hooks and parse the response. + + Returns routing decision based on parsing result. + """ + try: + enforce_rpm_limit(self.request_within_rpm_limit) + + answer = get_llm_response( + llm=self.llm, + messages=list(self.state.messages), + callbacks=self.callbacks, + printer=self._printer, + from_task=self.task, + from_agent=self.agent, + response_model=self.response_model, + executor_context=self, + ) + + # Parse the LLM response + formatted_answer = process_llm_response(answer, self.use_stop_words) + self.state.current_answer = formatted_answer + + if "Final Answer:" in answer and isinstance(formatted_answer, AgentAction): + warning_text = Text() + warning_text.append("⚠️ ", style="yellow bold") + warning_text.append( + f"LLM returned 'Final Answer:' but parsed as AgentAction (tool: {formatted_answer.tool})", + style="yellow", + ) + self._console.print(warning_text) + preview_text = Text() + preview_text.append("Answer preview: ", style="yellow") + preview_text.append(f"{answer[:200]}...", style="yellow dim") + self._console.print(preview_text) + + return "parsed" + + except OutputParserError as e: + # Store error context for recovery + self._last_parser_error = e or OutputParserError( + error="Unknown parser error" + ) + return "parser_error" + + except Exception as e: + if is_context_length_exceeded(e): + self._last_context_error = e + return "context_error" + if e.__class__.__module__.startswith("litellm"): + raise e + handle_unknown_error(self._printer, e) + raise + + @router(call_llm_and_parse) + def route_by_answer_type(self) -> Literal["execute_tool", "agent_finished"]: + """Route based on whether answer is AgentAction or AgentFinish.""" + if isinstance(self.state.current_answer, AgentAction): + return "execute_tool" + return "agent_finished" + + @listen("execute_tool") + def execute_tool_action(self) -> Literal["tool_completed", "tool_result_is_final"]: + """Execute the tool action and handle the result.""" + try: + action = cast(AgentAction, self.state.current_answer) + + # Extract fingerprint context for tool execution + fingerprint_context = {} + if ( + self.agent + and hasattr(self.agent, "security_config") + and hasattr(self.agent.security_config, "fingerprint") + ): + fingerprint_context = { + "agent_fingerprint": str(self.agent.security_config.fingerprint) + } + + # Execute the tool + tool_result = execute_tool_and_check_finality( + agent_action=action, + fingerprint_context=fingerprint_context, + tools=self.tools, + i18n=self._i18n, + agent_key=self.agent.key if self.agent else None, + agent_role=self.agent.role if self.agent else None, + tools_handler=self.tools_handler, + task=self.task, + agent=self.agent, + function_calling_llm=self.function_calling_llm, + crew=self.crew, + ) + + # Handle agent action and append observation to messages + result = self._handle_agent_action(action, tool_result) + self.state.current_answer = result + + # Invoke step callback if configured + self._invoke_step_callback(result) + + # Append result message to conversation state + if hasattr(result, "text"): + self._append_message_to_state(result.text) + + # Check if tool result became a final answer (result_as_answer flag) + if isinstance(result, AgentFinish): + self.state.is_finished = True + return "tool_result_is_final" + + return "tool_completed" + + except Exception as e: + error_text = Text() + error_text.append("❌ Error in tool execution: ", style="red bold") + error_text.append(str(e), style="red") + self._console.print(error_text) + raise + + @listen("initialized") + def continue_iteration(self) -> Literal["check_iteration"]: + """Bridge listener that connects iteration loop back to iteration check.""" + return "check_iteration" + + @router(or_(initialize_reasoning, continue_iteration)) + def check_max_iterations( + self, + ) -> Literal["force_final_answer", "continue_reasoning"]: + """Check if max iterations reached before proceeding with reasoning.""" + if has_reached_max_iterations(self.state.iterations, self.max_iter): + return "force_final_answer" + return "continue_reasoning" + + @router(execute_tool_action) + def increment_and_continue(self) -> Literal["initialized"]: + """Increment iteration counter and loop back for next iteration.""" + self.state.iterations += 1 + return "initialized" + + @listen(or_("agent_finished", "tool_result_is_final")) + def finalize(self) -> Literal["completed", "skipped"]: + """Finalize execution and emit completion logs.""" + if self.state.current_answer is None: + skip_text = Text() + skip_text.append("⚠️ ", style="yellow bold") + skip_text.append( + "Finalize called but no answer in state - skipping", style="yellow" + ) + self._console.print(skip_text) + return "skipped" + + if not isinstance(self.state.current_answer, AgentFinish): + skip_text = Text() + skip_text.append("⚠️ ", style="yellow bold") + skip_text.append( + f"Finalize called with {type(self.state.current_answer).__name__} instead of AgentFinish - skipping", + style="yellow", + ) + self._console.print(skip_text) + return "skipped" + + self.state.is_finished = True + + self._show_logs(self.state.current_answer) + + return "completed" + + @listen("parser_error") + def recover_from_parser_error(self) -> Literal["initialized"]: + """Recover from output parser errors and retry.""" + formatted_answer = handle_output_parser_exception( + e=self._last_parser_error, + messages=list(self.state.messages), + iterations=self.state.iterations, + log_error_after=self.log_error_after, + printer=self._printer, + ) + + if formatted_answer: + self.state.current_answer = formatted_answer + + self.state.iterations += 1 + + return "initialized" + + @listen("context_error") + def recover_from_context_length(self) -> Literal["initialized"]: + """Recover from context length errors and retry.""" + handle_context_length( + respect_context_window=self.respect_context_window, + printer=self._printer, + messages=self.state.messages, + llm=self.llm, + callbacks=self.callbacks, + i18n=self._i18n, + ) + + self.state.iterations += 1 + + return "initialized" + + def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]: + """Execute agent with given inputs. + + Args: + inputs: Input dictionary containing prompt variables. + + Returns: + Dictionary with agent output. + """ + self._ensure_flow_initialized() + + with self._execution_lock: + if self._is_executing: + raise RuntimeError( + "Executor is already running. " + "Cannot invoke the same executor instance concurrently." + ) + self._is_executing = True + self._has_been_invoked = True + + try: + # Reset state for fresh execution + self.state.messages.clear() + self.state.iterations = 0 + self.state.current_answer = None + self.state.is_finished = False + + if "system" in self.prompt: + prompt = cast("SystemPromptResult", self.prompt) + system_prompt = self._format_prompt(prompt["system"], inputs) + user_prompt = self._format_prompt(prompt["user"], inputs) + self.state.messages.append( + format_message_for_llm(system_prompt, role="system") + ) + self.state.messages.append(format_message_for_llm(user_prompt)) + else: + user_prompt = self._format_prompt(self.prompt["prompt"], inputs) + self.state.messages.append(format_message_for_llm(user_prompt)) + + self.state.ask_for_human_input = bool( + inputs.get("ask_for_human_input", False) + ) + + self.kickoff() + + formatted_answer = self.state.current_answer + + if not isinstance(formatted_answer, AgentFinish): + raise RuntimeError( + "Agent execution ended without reaching a final answer." + ) + + if self.state.ask_for_human_input: + formatted_answer = self._handle_human_feedback(formatted_answer) + + self._create_short_term_memory(formatted_answer) + self._create_long_term_memory(formatted_answer) + self._create_external_memory(formatted_answer) + + return {"output": formatted_answer.output} + + except AssertionError: + fail_text = Text() + fail_text.append("❌ ", style="red bold") + fail_text.append( + "Agent failed to reach a final answer. This is likely a bug - please report it.", + style="red", + ) + self._console.print(fail_text) + raise + except Exception as e: + handle_unknown_error(self._printer, e) + raise + finally: + self._is_executing = False + + def _handle_agent_action( + self, formatted_answer: AgentAction, tool_result: ToolResult + ) -> AgentAction | AgentFinish: + """Process agent action and tool execution result. + + Args: + formatted_answer: Agent's action to execute. + tool_result: Result from tool execution. + + Returns: + Updated action or final answer. + """ + add_image_tool = self._i18n.tools("add_image") + if ( + isinstance(add_image_tool, dict) + and formatted_answer.tool.casefold().strip() + == add_image_tool.get("name", "").casefold().strip() + ): + self.state.messages.append( + {"role": "assistant", "content": tool_result.result} + ) + return formatted_answer + + return handle_agent_action_core( + formatted_answer=formatted_answer, + tool_result=tool_result, + messages=self.state.messages, + step_callback=self.step_callback, + show_logs=self._show_logs, + ) + + def _invoke_step_callback( + self, formatted_answer: AgentAction | AgentFinish + ) -> None: + """Invoke step callback if configured. + + Args: + formatted_answer: Current agent response. + """ + if self.step_callback: + self.step_callback(formatted_answer) + + def _append_message_to_state( + self, text: str, role: Literal["user", "assistant", "system"] = "assistant" + ) -> None: + """Add message to state conversation history. + + Args: + text: Message content. + role: Message role (default: assistant). + """ + self.state.messages.append(format_message_for_llm(text, role=role)) + + def _show_start_logs(self) -> None: + """Emit agent start event.""" + if self.agent is None: + raise ValueError("Agent cannot be None") + + crewai_event_bus.emit( + self.agent, + AgentLogsStartedEvent( + agent_role=self.agent.role, + task_description=(self.task.description if self.task else "Not Found"), + verbose=self.agent.verbose + or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)), + ), + ) + + def _show_logs(self, formatted_answer: AgentAction | AgentFinish) -> None: + """Emit agent execution event. + + Args: + formatted_answer: Agent's response to log. + """ + if self.agent is None: + raise ValueError("Agent cannot be None") + + crewai_event_bus.emit( + self.agent, + AgentLogsExecutionEvent( + agent_role=self.agent.role, + formatted_answer=formatted_answer, + verbose=self.agent.verbose + or (hasattr(self, "crew") and getattr(self.crew, "verbose", False)), + ), + ) + + def _handle_crew_training_output( + self, result: AgentFinish, human_feedback: str | None = None + ) -> None: + """Save training data for crew training mode. + + Args: + result: Agent's final output. + human_feedback: Optional feedback from human. + """ + agent_id = str(self.agent.id) + train_iteration = ( + getattr(self.crew, "_train_iteration", None) if self.crew else None + ) + + if train_iteration is None or not isinstance(train_iteration, int): + train_error = Text() + train_error.append("❌ ", style="red bold") + train_error.append( + "Invalid or missing train iteration. Cannot save training data.", + style="red", + ) + self._console.print(train_error) + return + + training_handler = CrewTrainingHandler(TRAINING_DATA_FILE) + training_data = training_handler.load() or {} + + # Initialize or retrieve agent's training data + agent_training_data = training_data.get(agent_id, {}) + + if human_feedback is not None: + # Save initial output and human feedback + agent_training_data[train_iteration] = { + "initial_output": result.output, + "human_feedback": human_feedback, + } + else: + # Save improved output + if train_iteration in agent_training_data: + agent_training_data[train_iteration]["improved_output"] = result.output + else: + train_error = Text() + train_error.append("❌ ", style="red bold") + train_error.append( + f"No existing training data for agent {agent_id} and iteration " + f"{train_iteration}. Cannot save improved output.", + style="red", + ) + self._console.print(train_error) + return + + # Update the training data and save + training_data[agent_id] = agent_training_data + training_handler.save(training_data) + + @staticmethod + def _format_prompt(prompt: str, inputs: dict[str, str]) -> str: + """Format prompt template with input values. + + Args: + prompt: Template string. + inputs: Values to substitute. + + Returns: + Formatted prompt. + """ + prompt = prompt.replace("{input}", inputs["input"]) + prompt = prompt.replace("{tool_names}", inputs["tool_names"]) + return prompt.replace("{tools}", inputs["tools"]) + + def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish: + """Process human feedback and refine answer. + + Args: + formatted_answer: Initial agent result. + + Returns: + Final answer after feedback. + """ + human_feedback = self._ask_human_input(formatted_answer.output) + + if self._is_training_mode(): + return self._handle_training_feedback(formatted_answer, human_feedback) + + return self._handle_regular_feedback(formatted_answer, human_feedback) + + def _is_training_mode(self) -> bool: + """Check if training mode is active. + + Returns: + True if in training mode. + """ + return bool(self.crew and self.crew._train) + + def _handle_training_feedback( + self, initial_answer: AgentFinish, feedback: str + ) -> AgentFinish: + """Process training feedback and generate improved answer. + + Args: + initial_answer: Initial agent output. + feedback: Training feedback. + + Returns: + Improved answer. + """ + self._handle_crew_training_output(initial_answer, feedback) + self.state.messages.append( + format_message_for_llm( + self._i18n.slice("feedback_instructions").format(feedback=feedback) + ) + ) + + # Re-run flow for improved answer + self.state.iterations = 0 + self.state.is_finished = False + self.state.current_answer = None + + self.kickoff() + + # Get improved answer from state + improved_answer = self.state.current_answer + if not isinstance(improved_answer, AgentFinish): + raise RuntimeError( + "Training feedback iteration did not produce final answer" + ) + + self._handle_crew_training_output(improved_answer) + self.state.ask_for_human_input = False + return improved_answer + + def _handle_regular_feedback( + self, current_answer: AgentFinish, initial_feedback: str + ) -> AgentFinish: + """Process regular feedback iteratively until user is satisfied. + + Args: + current_answer: Current agent output. + initial_feedback: Initial user feedback. + + Returns: + Final answer after iterations. + """ + feedback = initial_feedback + answer = current_answer + + while self.state.ask_for_human_input: + if feedback.strip() == "": + self.state.ask_for_human_input = False + else: + answer = self._process_feedback_iteration(feedback) + feedback = self._ask_human_input(answer.output) + + return answer + + def _process_feedback_iteration(self, feedback: str) -> AgentFinish: + """Process a single feedback iteration and generate updated response. + + Args: + feedback: User feedback. + + Returns: + Updated agent response. + """ + self.state.messages.append( + format_message_for_llm( + self._i18n.slice("feedback_instructions").format(feedback=feedback) + ) + ) + + # Re-run flow + self.state.iterations = 0 + self.state.is_finished = False + self.state.current_answer = None + + self.kickoff() + + # Get answer from state + answer = self.state.current_answer + if not isinstance(answer, AgentFinish): + raise RuntimeError("Feedback iteration did not produce final answer") + + return answer + + @classmethod + def __get_pydantic_core_schema__( + cls, _source_type: Any, _handler: GetCoreSchemaHandler + ) -> CoreSchema: + """Generate Pydantic core schema for Protocol compatibility. + + Allows the executor to be used in Pydantic models without + requiring arbitrary_types_allowed=True. + """ + return core_schema.any_schema() diff --git a/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py b/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py index 7114e8c40..3b9610839 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py +++ b/lib/crewai/src/crewai/experimental/evaluation/agent_evaluator.py @@ -1,8 +1,9 @@ +from __future__ import annotations + from collections.abc import Sequence import threading -from typing import Any +from typing import TYPE_CHECKING, Any -from crewai.agent.core import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.events.event_bus import crewai_event_bus from crewai.events.types.agent_events import ( @@ -28,6 +29,10 @@ from crewai.experimental.evaluation.evaluation_listener import ( from crewai.task import Task +if TYPE_CHECKING: + from crewai.agent import Agent + + class ExecutionState: current_agent_id: str | None = None current_task_id: str | None = None diff --git a/lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py b/lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py index 69d1bb5c3..bcc8f9801 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py +++ b/lib/crewai/src/crewai/experimental/evaluation/base_evaluator.py @@ -1,17 +1,22 @@ +from __future__ import annotations + import abc import enum from enum import Enum -from typing import Any +from typing import TYPE_CHECKING, Any from pydantic import BaseModel, Field -from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.llm import BaseLLM from crewai.task import Task from crewai.utilities.llm_utils import create_llm +if TYPE_CHECKING: + from crewai.agent import Agent + + class MetricCategory(enum.Enum): GOAL_ALIGNMENT = "goal_alignment" SEMANTIC_QUALITY = "semantic_quality" diff --git a/lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py b/lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py index a01457e85..22a254053 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py +++ b/lib/crewai/src/crewai/experimental/evaluation/experiment/runner.py @@ -1,8 +1,9 @@ +from __future__ import annotations + from collections import defaultdict from hashlib import md5 -from typing import Any +from typing import TYPE_CHECKING, Any -from crewai import Agent, Crew from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.experimental.evaluation import AgentEvaluator, create_default_evaluator from crewai.experimental.evaluation.evaluation_display import ( @@ -17,6 +18,11 @@ from crewai.experimental.evaluation.experiment.result_display import ( ) +if TYPE_CHECKING: + from crewai.agent import Agent + from crewai.crew import Crew + + class ExperimentRunner: def __init__(self, dataset: list[dict[str, Any]]): self.dataset = dataset or [] diff --git a/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py index 9758a7fe4..0075c3c49 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/goal_metrics.py @@ -1,6 +1,7 @@ -from typing import Any +from __future__ import annotations + +from typing import TYPE_CHECKING, Any -from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.experimental.evaluation.base_evaluator import ( BaseEvaluator, @@ -12,6 +13,10 @@ from crewai.task import Task from crewai.utilities.types import LLMMessage +if TYPE_CHECKING: + from crewai.agent import Agent + + class GoalAlignmentEvaluator(BaseEvaluator): @property def metric_category(self) -> MetricCategory: diff --git a/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py index 4531103f5..67c272879 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/reasoning_metrics.py @@ -6,15 +6,16 @@ This module provides evaluator implementations for: - Thinking-to-action ratio """ +from __future__ import annotations + from collections.abc import Sequence from enum import Enum import logging import re -from typing import Any +from typing import TYPE_CHECKING, Any import numpy as np -from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.experimental.evaluation.base_evaluator import ( BaseEvaluator, @@ -27,6 +28,10 @@ from crewai.tasks.task_output import TaskOutput from crewai.utilities.types import LLMMessage +if TYPE_CHECKING: + from crewai.agent import Agent + + class ReasoningPatternType(Enum): EFFICIENT = "efficient" # Good reasoning flow LOOP = "loop" # Agent is stuck in a loop diff --git a/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py index 7a0bd488a..692c4f84a 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/semantic_quality_metrics.py @@ -1,6 +1,7 @@ -from typing import Any +from __future__ import annotations + +from typing import TYPE_CHECKING, Any -from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.experimental.evaluation.base_evaluator import ( BaseEvaluator, @@ -12,6 +13,10 @@ from crewai.task import Task from crewai.utilities.types import LLMMessage +if TYPE_CHECKING: + from crewai.agent import Agent + + class SemanticQualityEvaluator(BaseEvaluator): @property def metric_category(self) -> MetricCategory: diff --git a/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py b/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py index baf7c69c5..99580dc3f 100644 --- a/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py +++ b/lib/crewai/src/crewai/experimental/evaluation/metrics/tools_metrics.py @@ -1,7 +1,8 @@ -import json -from typing import Any +from __future__ import annotations + +import json +from typing import TYPE_CHECKING, Any -from crewai.agent import Agent from crewai.agents.agent_builder.base_agent import BaseAgent from crewai.experimental.evaluation.base_evaluator import ( BaseEvaluator, @@ -13,6 +14,10 @@ from crewai.task import Task from crewai.utilities.types import LLMMessage +if TYPE_CHECKING: + from crewai.agent import Agent + + class ToolSelectionEvaluator(BaseEvaluator): @property def metric_category(self) -> MetricCategory: diff --git a/lib/crewai/src/crewai/flow/flow.py b/lib/crewai/src/crewai/flow/flow.py index 26b8c7190..ade5b6f1b 100644 --- a/lib/crewai/src/crewai/flow/flow.py +++ b/lib/crewai/src/crewai/flow/flow.py @@ -459,7 +459,10 @@ class FlowMeta(type): ): routers.add(attr_name) # Get router paths from the decorator attribute - if hasattr(attr_value, "__router_paths__") and attr_value.__router_paths__: + if ( + hasattr(attr_value, "__router_paths__") + and attr_value.__router_paths__ + ): router_paths[attr_name] = attr_value.__router_paths__ else: possible_returns = get_possible_return_constants(attr_value) @@ -501,6 +504,7 @@ class Flow(Generic[T], metaclass=FlowMeta): self, persistence: FlowPersistence | None = None, tracing: bool | None = None, + suppress_flow_events: bool = False, **kwargs: Any, ) -> None: """Initialize a new Flow instance. @@ -508,6 +512,7 @@ class Flow(Generic[T], metaclass=FlowMeta): Args: persistence: Optional persistence backend for storing flow states tracing: Whether to enable tracing. True=always enable, False=always disable, None=check environment/user settings + suppress_flow_events: Whether to suppress flow event emissions (internal use) **kwargs: Additional state values to initialize or override """ # Initialize basic instance attributes @@ -526,6 +531,7 @@ class Flow(Generic[T], metaclass=FlowMeta): self.human_feedback_history: list[HumanFeedbackResult] = [] self.last_human_feedback: HumanFeedbackResult | None = None self._pending_feedback_context: PendingFeedbackContext | None = None + self.suppress_flow_events: bool = suppress_flow_events # Initialize state with initial values self._state = self._create_initial_state() @@ -539,13 +545,14 @@ class Flow(Generic[T], metaclass=FlowMeta): if kwargs: self._initialize_state(kwargs) - crewai_event_bus.emit( - self, - FlowCreatedEvent( - type="flow_created", - flow_name=self.name or self.__class__.__name__, - ), - ) + if not self.suppress_flow_events: + crewai_event_bus.emit( + self, + FlowCreatedEvent( + type="flow_created", + flow_name=self.name or self.__class__.__name__, + ), + ) # Register all flow-related methods for method_name in dir(self): @@ -672,6 +679,7 @@ class Flow(Generic[T], metaclass=FlowMeta): result = flow.resume(feedback) return result + # In an async handler, use resume_async instead: async def handle_feedback_async(flow_id: str, feedback: str): flow = MyFlow.from_pending(flow_id) @@ -1307,19 +1315,20 @@ class Flow(Generic[T], metaclass=FlowMeta): self._initialize_state(filtered_inputs) # Emit FlowStartedEvent and log the start of the flow. - future = crewai_event_bus.emit( - self, - FlowStartedEvent( - type="flow_started", - flow_name=self.name or self.__class__.__name__, - inputs=inputs, - ), - ) - if future: - self._event_futures.append(future) - self._log_flow_event( - f"Flow started with ID: {self.flow_id}", color="bold magenta" - ) + if not self.suppress_flow_events: + future = crewai_event_bus.emit( + self, + FlowStartedEvent( + type="flow_started", + flow_name=self.name or self.__class__.__name__, + inputs=inputs, + ), + ) + if future: + self._event_futures.append(future) + self._log_flow_event( + f"Flow started with ID: {self.flow_id}", color="bold magenta" + ) if inputs is not None and "id" not in inputs: self._initialize_state(inputs) @@ -1391,17 +1400,18 @@ class Flow(Generic[T], metaclass=FlowMeta): final_output = self._method_outputs[-1] if self._method_outputs else None - future = crewai_event_bus.emit( - self, - FlowFinishedEvent( - type="flow_finished", - flow_name=self.name or self.__class__.__name__, - result=final_output, - state=self._copy_and_serialize_state(), - ), - ) - if future: - self._event_futures.append(future) + if not self.suppress_flow_events: + future = crewai_event_bus.emit( + self, + FlowFinishedEvent( + type="flow_finished", + flow_name=self.name or self.__class__.__name__, + result=final_output, + state=self._copy_and_serialize_state(), + ), + ) + if future: + self._event_futures.append(future) if self._event_futures: await asyncio.gather( @@ -1537,18 +1547,19 @@ class Flow(Generic[T], metaclass=FlowMeta): kwargs or {} ) - future = crewai_event_bus.emit( - self, - MethodExecutionStartedEvent( - type="method_execution_started", - method_name=method_name, - flow_name=self.name or self.__class__.__name__, - params=dumped_params, - state=self._copy_and_serialize_state(), - ), - ) - if future: - self._event_futures.append(future) + if not self.suppress_flow_events: + future = crewai_event_bus.emit( + self, + MethodExecutionStartedEvent( + type="method_execution_started", + method_name=method_name, + flow_name=self.name or self.__class__.__name__, + params=dumped_params, + state=self._copy_and_serialize_state(), + ), + ) + if future: + self._event_futures.append(future) result = ( await method(*args, **kwargs) @@ -1563,41 +1574,32 @@ class Flow(Generic[T], metaclass=FlowMeta): self._completed_methods.add(method_name) - future = crewai_event_bus.emit( - self, - MethodExecutionFinishedEvent( - type="method_execution_finished", - method_name=method_name, - flow_name=self.name or self.__class__.__name__, - state=self._copy_and_serialize_state(), - result=result, - ), - ) - if future: - self._event_futures.append(future) - - return result - except Exception as e: - # Check if this is a HumanFeedbackPending exception (paused, not failed) - from crewai.flow.async_feedback.types import HumanFeedbackPending - - if isinstance(e, HumanFeedbackPending): - # Emit paused event instead of failed + if not self.suppress_flow_events: future = crewai_event_bus.emit( self, - MethodExecutionPausedEvent( - type="method_execution_paused", + MethodExecutionFinishedEvent( + type="method_execution_finished", method_name=method_name, flow_name=self.name or self.__class__.__name__, state=self._copy_and_serialize_state(), - flow_id=e.context.flow_id, - message=e.context.message, - emit=e.context.emit, + result=result, ), ) if future: self._event_futures.append(future) - raise e + + return result + except Exception as e: + if not self.suppress_flow_events: + # Check if this is a HumanFeedbackPending exception (paused, not failed) + from crewai.flow.async_feedback.types import HumanFeedbackPending + + if isinstance(e, HumanFeedbackPending): + # Auto-save pending feedback (create default persistence if needed) + if self._persistence is None: + from crewai.flow.persistence import SQLiteFlowPersistence + + self._persistence = SQLiteFlowPersistence() # Regular failure future = crewai_event_bus.emit( @@ -1644,7 +1646,9 @@ class Flow(Generic[T], metaclass=FlowMeta): """ # First, handle routers repeatedly until no router triggers anymore router_results = [] - router_result_to_feedback: dict[str, Any] = {} # Map outcome -> HumanFeedbackResult + router_result_to_feedback: dict[ + str, Any + ] = {} # Map outcome -> HumanFeedbackResult current_trigger = trigger_method current_result = result # Track the result to pass to each router @@ -1963,7 +1967,9 @@ class Flow(Generic[T], metaclass=FlowMeta): # Show message and prompt for feedback formatter.console.print(message, style="yellow") - formatter.console.print("(Press Enter to skip, or type your feedback)\n", style="cyan") + formatter.console.print( + "(Press Enter to skip, or type your feedback)\n", style="cyan" + ) feedback = input("Your feedback: ").strip() diff --git a/lib/crewai/tests/agents/test_agent.py b/lib/crewai/tests/agents/test_agent.py index 26aec7252..d2ec33617 100644 --- a/lib/crewai/tests/agents/test_agent.py +++ b/lib/crewai/tests/agents/test_agent.py @@ -1178,6 +1178,7 @@ def test_system_and_prompt_template(): {{ .Response }}<|eot_id|>""", ) + agent.create_agent_executor() expected_prompt = """<|start_header_id|>system<|end_header_id|> @@ -1442,6 +1443,8 @@ def test_agent_max_retry_limit(): human_input=True, ) + agent.create_agent_executor(task=task) + error_message = "Error happening while sending prompt to model." with patch.object( CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke @@ -1503,9 +1506,8 @@ def test_agent_with_custom_stop_words(): ) assert isinstance(agent.llm, BaseLLM) - assert set(agent.llm.stop) == set([*stop_words, "\nObservation:"]) + assert set(agent.llm.stop) == set(stop_words) assert all(word in agent.llm.stop for word in stop_words) - assert "\nObservation:" in agent.llm.stop def test_agent_with_callbacks(): @@ -1629,6 +1631,8 @@ def test_handle_context_length_exceeds_limit_cli_no(): ) task = Task(description="test task", agent=agent, expected_output="test output") + agent.create_agent_executor(task=task) + with patch.object( CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke ) as private_mock: @@ -1679,8 +1683,8 @@ def test_agent_with_all_llm_attributes(): assert agent.llm.temperature == 0.7 assert agent.llm.top_p == 0.9 # assert agent.llm.n == 1 - assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"]) - assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"]) + assert set(agent.llm.stop) == set(["STOP", "END"]) + assert all(word in agent.llm.stop for word in ["STOP", "END"]) assert agent.llm.max_tokens == 100 assert agent.llm.presence_penalty == 0.1 assert agent.llm.frequency_penalty == 0.1 diff --git a/lib/crewai/tests/agents/test_crew_agent_executor_flow.py b/lib/crewai/tests/agents/test_crew_agent_executor_flow.py new file mode 100644 index 000000000..36fd887eb --- /dev/null +++ b/lib/crewai/tests/agents/test_crew_agent_executor_flow.py @@ -0,0 +1,479 @@ +"""Unit tests for CrewAgentExecutorFlow. + +Tests the Flow-based agent executor implementation including state management, +flow methods, routing logic, and error handling. +""" + +from unittest.mock import Mock, patch + +import pytest + +from crewai.experimental.crew_agent_executor_flow import ( + AgentReActState, + CrewAgentExecutorFlow, +) +from crewai.agents.parser import AgentAction, AgentFinish + +class TestAgentReActState: + """Test AgentReActState Pydantic model.""" + + def test_state_initialization(self): + """Test AgentReActState initialization with defaults.""" + state = AgentReActState() + assert state.iterations == 0 + assert state.messages == [] + assert state.current_answer is None + assert state.is_finished is False + assert state.ask_for_human_input is False + + def test_state_with_values(self): + """Test AgentReActState initialization with values.""" + messages = [{"role": "user", "content": "test"}] + state = AgentReActState( + messages=messages, + iterations=5, + current_answer=AgentFinish(thought="thinking", output="done", text="final"), + is_finished=True, + ask_for_human_input=True, + ) + assert state.messages == messages + assert state.iterations == 5 + assert isinstance(state.current_answer, AgentFinish) + assert state.is_finished is True + assert state.ask_for_human_input is True + + +class TestCrewAgentExecutorFlow: + """Test CrewAgentExecutorFlow class.""" + + @pytest.fixture + def mock_dependencies(self): + """Create mock dependencies for executor.""" + llm = Mock() + llm.supports_stop_words.return_value = True + + task = Mock() + task.description = "Test task" + task.human_input = False + task.response_model = None + + crew = Mock() + 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 prompt with {input}, {tool_names}, {tools}"} + + tools = [] + tools_handler = Mock() + + return { + "llm": llm, + "task": task, + "crew": crew, + "agent": agent, + "prompt": prompt, + "max_iter": 10, + "tools": tools, + "tools_names": "", + "stop_words": ["Observation"], + "tools_description": "", + "tools_handler": tools_handler, + } + + def test_executor_initialization(self, mock_dependencies): + """Test CrewAgentExecutorFlow initialization.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + + assert executor.llm == mock_dependencies["llm"] + assert executor.task == mock_dependencies["task"] + assert executor.agent == mock_dependencies["agent"] + assert executor.crew == mock_dependencies["crew"] + assert executor.max_iter == 10 + assert executor.use_stop_words is True + + def test_initialize_reasoning(self, mock_dependencies): + """Test flow entry point.""" + with patch.object( + CrewAgentExecutorFlow, "_show_start_logs" + ) as mock_show_start: + executor = CrewAgentExecutorFlow(**mock_dependencies) + result = executor.initialize_reasoning() + + assert result == "initialized" + mock_show_start.assert_called_once() + + def test_check_max_iterations_not_reached(self, mock_dependencies): + """Test routing when iterations < max.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.iterations = 5 + + result = executor.check_max_iterations() + assert result == "continue_reasoning" + + def test_check_max_iterations_reached(self, mock_dependencies): + """Test routing when iterations >= max.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.iterations = 10 + + result = executor.check_max_iterations() + assert result == "force_final_answer" + + def test_route_by_answer_type_action(self, mock_dependencies): + """Test routing for AgentAction.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.current_answer = AgentAction( + thought="thinking", tool="search", tool_input="query", text="action text" + ) + + result = executor.route_by_answer_type() + assert result == "execute_tool" + + def test_route_by_answer_type_finish(self, mock_dependencies): + """Test routing for AgentFinish.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.current_answer = AgentFinish( + thought="final thoughts", output="Final answer", text="complete" + ) + + result = executor.route_by_answer_type() + assert result == "agent_finished" + + def test_continue_iteration(self, mock_dependencies): + """Test iteration continuation.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + + result = executor.continue_iteration() + + assert result == "check_iteration" + + def test_finalize_success(self, mock_dependencies): + """Test finalize with valid AgentFinish.""" + with patch.object(CrewAgentExecutorFlow, "_show_logs") as mock_show_logs: + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.current_answer = AgentFinish( + thought="final thinking", output="Done", text="complete" + ) + + result = executor.finalize() + + assert result == "completed" + assert executor.state.is_finished is True + mock_show_logs.assert_called_once() + + def test_finalize_failure(self, mock_dependencies): + """Test finalize skips when given AgentAction instead of AgentFinish.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.current_answer = AgentAction( + thought="thinking", tool="search", tool_input="query", text="action text" + ) + + result = executor.finalize() + + # Should return "skipped" and not set is_finished + assert result == "skipped" + assert executor.state.is_finished is False + + def test_format_prompt(self, mock_dependencies): + """Test prompt formatting.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + inputs = {"input": "test input", "tool_names": "tool1, tool2", "tools": "desc"} + + result = executor._format_prompt("Prompt {input} {tool_names} {tools}", inputs) + + assert "test input" in result + assert "tool1, tool2" in result + assert "desc" in result + + def test_is_training_mode_false(self, mock_dependencies): + """Test training mode detection when not in training.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + assert executor._is_training_mode() is False + + def test_is_training_mode_true(self, mock_dependencies): + """Test training mode detection when in training.""" + mock_dependencies["crew"]._train = True + executor = CrewAgentExecutorFlow(**mock_dependencies) + assert executor._is_training_mode() is True + + def test_append_message_to_state(self, mock_dependencies): + """Test message appending to state.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + initial_count = len(executor.state.messages) + + executor._append_message_to_state("test message") + + assert len(executor.state.messages) == initial_count + 1 + assert executor.state.messages[-1]["content"] == "test message" + + def test_invoke_step_callback(self, mock_dependencies): + """Test step callback invocation.""" + callback = Mock() + mock_dependencies["step_callback"] = callback + + executor = CrewAgentExecutorFlow(**mock_dependencies) + answer = AgentFinish(thought="thinking", output="test", text="final") + + executor._invoke_step_callback(answer) + + callback.assert_called_once_with(answer) + + def test_invoke_step_callback_none(self, mock_dependencies): + """Test step callback when none provided.""" + mock_dependencies["step_callback"] = None + executor = CrewAgentExecutorFlow(**mock_dependencies) + + # Should not raise error + executor._invoke_step_callback( + AgentFinish(thought="thinking", output="test", text="final") + ) + + @patch("crewai.experimental.crew_agent_executor_flow.handle_output_parser_exception") + def test_recover_from_parser_error( + self, mock_handle_exception, mock_dependencies + ): + """Test recovery from OutputParserError.""" + from crewai.agents.parser import OutputParserError + + mock_handle_exception.return_value = None + + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor._last_parser_error = OutputParserError("test error") + initial_iterations = executor.state.iterations + + result = executor.recover_from_parser_error() + + assert result == "initialized" + assert executor.state.iterations == initial_iterations + 1 + mock_handle_exception.assert_called_once() + + @patch("crewai.experimental.crew_agent_executor_flow.handle_context_length") + def test_recover_from_context_length( + self, mock_handle_context, mock_dependencies + ): + """Test recovery from context length error.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor._last_context_error = Exception("context too long") + initial_iterations = executor.state.iterations + + result = executor.recover_from_context_length() + + assert result == "initialized" + assert executor.state.iterations == initial_iterations + 1 + mock_handle_context.assert_called_once() + + def test_use_stop_words_property(self, mock_dependencies): + """Test use_stop_words property.""" + mock_dependencies["llm"].supports_stop_words.return_value = True + executor = CrewAgentExecutorFlow(**mock_dependencies) + assert executor.use_stop_words is True + + mock_dependencies["llm"].supports_stop_words.return_value = False + executor = CrewAgentExecutorFlow(**mock_dependencies) + assert executor.use_stop_words is False + + def test_compatibility_properties(self, mock_dependencies): + """Test compatibility properties for mixin.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.messages = [{"role": "user", "content": "test"}] + executor.state.iterations = 5 + + # Test that compatibility properties return state values + assert executor.messages == executor.state.messages + assert executor.iterations == executor.state.iterations + + +class TestFlowErrorHandling: + """Test error handling in flow methods.""" + + @pytest.fixture + def mock_dependencies(self): + """Create mock dependencies.""" + llm = Mock() + llm.supports_stop_words.return_value = True + + task = Mock() + task.description = "Test task" + + crew = Mock() + agent = Mock() + agent.role = "Test Agent" + agent.verbose = False + + prompt = {"prompt": "Test {input}"} + + return { + "llm": llm, + "task": task, + "crew": crew, + "agent": agent, + "prompt": prompt, + "max_iter": 10, + "tools": [], + "tools_names": "", + "stop_words": [], + "tools_description": "", + "tools_handler": Mock(), + } + + @patch("crewai.experimental.crew_agent_executor_flow.get_llm_response") + @patch("crewai.experimental.crew_agent_executor_flow.enforce_rpm_limit") + def test_call_llm_parser_error( + self, mock_enforce_rpm, mock_get_llm, mock_dependencies + ): + """Test call_llm_and_parse handles OutputParserError.""" + from crewai.agents.parser import OutputParserError + + mock_enforce_rpm.return_value = None + mock_get_llm.side_effect = OutputParserError("parse failed") + + executor = CrewAgentExecutorFlow(**mock_dependencies) + result = executor.call_llm_and_parse() + + assert result == "parser_error" + assert executor._last_parser_error is not None + + @patch("crewai.experimental.crew_agent_executor_flow.get_llm_response") + @patch("crewai.experimental.crew_agent_executor_flow.enforce_rpm_limit") + @patch("crewai.experimental.crew_agent_executor_flow.is_context_length_exceeded") + def test_call_llm_context_error( + self, + mock_is_context_exceeded, + mock_enforce_rpm, + mock_get_llm, + mock_dependencies, + ): + """Test call_llm_and_parse handles context length error.""" + mock_enforce_rpm.return_value = None + mock_get_llm.side_effect = Exception("context length") + mock_is_context_exceeded.return_value = True + + executor = CrewAgentExecutorFlow(**mock_dependencies) + result = executor.call_llm_and_parse() + + assert result == "context_error" + assert executor._last_context_error is not None + + +class TestFlowInvoke: + """Test the invoke method that maintains backward compatibility.""" + + @pytest.fixture + def mock_dependencies(self): + """Create mock dependencies.""" + llm = Mock() + task = Mock() + task.description = "Test" + task.human_input = False + + crew = Mock() + crew._short_term_memory = None + crew._long_term_memory = None + crew._entity_memory = None + crew._external_memory = None + + agent = Mock() + agent.role = "Test" + agent.verbose = False + + prompt = {"prompt": "Test {input} {tool_names} {tools}"} + + return { + "llm": llm, + "task": task, + "crew": crew, + "agent": agent, + "prompt": prompt, + "max_iter": 10, + "tools": [], + "tools_names": "", + "stop_words": [], + "tools_description": "", + "tools_handler": Mock(), + } + + @patch.object(CrewAgentExecutorFlow, "kickoff") + @patch.object(CrewAgentExecutorFlow, "_create_short_term_memory") + @patch.object(CrewAgentExecutorFlow, "_create_long_term_memory") + @patch.object(CrewAgentExecutorFlow, "_create_external_memory") + def test_invoke_success( + self, + mock_external_memory, + mock_long_term_memory, + mock_short_term_memory, + mock_kickoff, + mock_dependencies, + ): + """Test successful invoke without human feedback.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + + # Mock kickoff to set the final answer in state + def mock_kickoff_side_effect(): + executor.state.current_answer = AgentFinish( + thought="final thinking", output="Final result", text="complete" + ) + + mock_kickoff.side_effect = mock_kickoff_side_effect + + inputs = {"input": "test", "tool_names": "", "tools": ""} + result = executor.invoke(inputs) + + assert result == {"output": "Final result"} + mock_kickoff.assert_called_once() + mock_short_term_memory.assert_called_once() + mock_long_term_memory.assert_called_once() + mock_external_memory.assert_called_once() + + @patch.object(CrewAgentExecutorFlow, "kickoff") + def test_invoke_failure_no_agent_finish(self, mock_kickoff, mock_dependencies): + """Test invoke fails without AgentFinish.""" + executor = CrewAgentExecutorFlow(**mock_dependencies) + executor.state.current_answer = AgentAction( + thought="thinking", tool="test", tool_input="test", text="action text" + ) + + inputs = {"input": "test", "tool_names": "", "tools": ""} + + with pytest.raises(RuntimeError, match="without reaching a final answer"): + executor.invoke(inputs) + + @patch.object(CrewAgentExecutorFlow, "kickoff") + @patch.object(CrewAgentExecutorFlow, "_create_short_term_memory") + @patch.object(CrewAgentExecutorFlow, "_create_long_term_memory") + @patch.object(CrewAgentExecutorFlow, "_create_external_memory") + def test_invoke_with_system_prompt( + self, + mock_external_memory, + mock_long_term_memory, + mock_short_term_memory, + mock_kickoff, + mock_dependencies, + ): + """Test invoke with system prompt configuration.""" + mock_dependencies["prompt"] = { + "system": "System: {input}", + "user": "User: {input} {tool_names} {tools}", + } + executor = CrewAgentExecutorFlow(**mock_dependencies) + + def mock_kickoff_side_effect(): + executor.state.current_answer = AgentFinish( + thought="final thoughts", output="Done", text="complete" + ) + + mock_kickoff.side_effect = mock_kickoff_side_effect + + inputs = {"input": "test", "tool_names": "", "tools": ""} + result = executor.invoke(inputs) + mock_short_term_memory.assert_called_once() + mock_long_term_memory.assert_called_once() + mock_external_memory.assert_called_once() + mock_kickoff.assert_called_once() + + assert result == {"output": "Done"} + assert len(executor.state.messages) >= 2 diff --git a/uv.lock b/uv.lock index 42fb449de..448593726 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