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refactor: extract hitl to provider pattern

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* refactor: extract hitl to provider pattern

- add humaninputprovider protocol with setup_messages and handle_feedback
- move sync hitl logic from executor to synchuman inputprovider
- add _passthrough_exceptions extension point in agent/core.py
- create crewai.core.providers module for extensible components
- remove _ask_human_input from base_agent_executor_mixin
This commit is contained in:
Greyson LaLonde
2026-02-04 15:40:22 -05:00
committed by GitHub
parent 3cc33ef6ab
commit 6bfc98e960
10 changed files with 465 additions and 191 deletions
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6
lib/crewai/src/crewai/agent/core.py
View File

@@ -118,6 +118,8 @@ MCP_TOOL_EXECUTION_TIMEOUT: Final[int] = 30
MCP_DISCOVERY_TIMEOUT: Final[int] = 15
MCP_MAX_RETRIES: Final[int] = 3
_passthrough_exceptions: tuple[type[Exception], ...] = ()
# Simple in-memory cache for MCP tool schemas (duration: 5 minutes)
_mcp_schema_cache: dict[str, Any] = {}
_cache_ttl: Final[int] = 300 # 5 minutes
@@ -479,6 +481,8 @@ class Agent(BaseAgent):
),
)
raise e
if isinstance(e, _passthrough_exceptions):
raise
self._times_executed += 1
if self._times_executed > self.max_retry_limit:
crewai_event_bus.emit(
@@ -711,6 +715,8 @@ class Agent(BaseAgent):
),
)
raise e
if isinstance(e, _passthrough_exceptions):
raise
self._times_executed += 1
if self._times_executed > self.max_retry_limit:
crewai_event_bus.emit(

50
lib/crewai/src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -4,7 +4,6 @@ import time
from typing import TYPE_CHECKING
from crewai.agents.parser import AgentFinish
from crewai.events.event_listener import event_listener
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.utilities.converter import ConverterError
@@ -138,52 +137,3 @@ class CrewAgentExecutorMixin:
content="Long term memory is enabled, but entity memory is not enabled. Please configure entity memory or set memory=True to automatically enable it.",
color="bold_yellow",
)
def _ask_human_input(self, final_answer: str) -> str:
"""Prompt human input with mode-appropriate messaging.
Note: The final answer is already displayed via the AgentLogsExecutionEvent
panel, so we only show the feedback prompt here.
"""
from rich.panel import Panel
from rich.text import Text
formatter = event_listener.formatter
formatter.pause_live_updates()
try:
# Training mode prompt (single iteration)
if self.crew and getattr(self.crew, "_train", False):
prompt_text = (
"TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
"This will be used to train better versions of the agent.\n"
"Please provide detailed feedback about the result quality and reasoning process."
)
title = "🎓 Training Feedback Required"
# Regular human-in-the-loop prompt (multiple iterations)
else:
prompt_text = (
"Provide feedback on the Final Result above.\n\n"
"• If you are happy with the result, simply hit Enter without typing anything.\n"
"• Otherwise, provide specific improvement requests.\n"
"• You can provide multiple rounds of feedback until satisfied."
)
title = "💬 Human Feedback Required"
content = Text()
content.append(prompt_text, style="yellow")
prompt_panel = Panel(
content,
title=title,
border_style="yellow",
padding=(1, 2),
)
formatter.console.print(prompt_panel)
response = input()
if response.strip() != "":
formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
return response
finally:
formatter.resume_live_updates()

121
lib/crewai/src/crewai/agents/crew_agent_executor.py
View File

@@ -19,6 +19,7 @@ from crewai.agents.parser import (
AgentFinish,
OutputParserError,
)
from crewai.core.providers.human_input import ExecutorContext, get_provider
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.logging_events import (
AgentLogsExecutionEvent,
@@ -175,15 +176,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
"""
return self.llm.supports_stop_words() if self.llm else False
def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
"""Execute the agent with given inputs.
def _setup_messages(self, inputs: dict[str, Any]) -> None:
"""Set up messages for the agent execution.
Args:
inputs: Input dictionary containing prompt variables.
Returns:
Dictionary with agent output.
"""
provider = get_provider()
if provider.setup_messages(cast(ExecutorContext, cast(object, self))):
return
if "system" in self.prompt:
system_prompt = self._format_prompt(
cast(str, self.prompt.get("system", "")), inputs
@@ -197,6 +199,19 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
self.messages.append(format_message_for_llm(user_prompt))
provider.post_setup_messages(cast(ExecutorContext, cast(object, self)))
def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
"""Execute the agent with given inputs.
Args:
inputs: Input dictionary containing prompt variables.
Returns:
Dictionary with agent output.
"""
self._setup_messages(inputs)
self._inject_multimodal_files(inputs)
self._show_start_logs()
@@ -970,18 +985,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
Returns:
Dictionary with agent output.
"""
if "system" in self.prompt:
system_prompt = self._format_prompt(
cast(str, self.prompt.get("system", "")), inputs
)
user_prompt = self._format_prompt(
cast(str, self.prompt.get("user", "")), inputs
)
self.messages.append(format_message_for_llm(system_prompt, role="system"))
self.messages.append(format_message_for_llm(user_prompt))
else:
user_prompt = self._format_prompt(self.prompt.get("prompt", ""), inputs)
self.messages.append(format_message_for_llm(user_prompt))
self._setup_messages(inputs)
await self._ainject_multimodal_files(inputs)
@@ -1491,7 +1495,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
return prompt.replace("{tools}", inputs["tools"])
def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
"""Process human feedback.
"""Process human feedback via the configured provider.
Args:
formatted_answer: Initial agent result.
@@ -1499,17 +1503,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
Returns:
Final answer after feedback.
"""
output_str = (
formatted_answer.output
if isinstance(formatted_answer.output, str)
else formatted_answer.output.model_dump_json()
)
human_feedback = self._ask_human_input(output_str)
if self._is_training_mode():
return self._handle_training_feedback(formatted_answer, human_feedback)
return self._handle_regular_feedback(formatted_answer, human_feedback)
provider = get_provider()
return provider.handle_feedback(formatted_answer, self)
def _is_training_mode(self) -> bool:
"""Check if training mode is active.
@@ -1519,74 +1514,18 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
"""
return bool(self.crew and self.crew._train)
def _handle_training_feedback(
self, initial_answer: AgentFinish, feedback: str
) -> AgentFinish:
"""Process training feedback.
def _format_feedback_message(self, feedback: str) -> LLMMessage:
"""Format feedback as a message for the LLM.
Args:
initial_answer: Initial agent output.
feedback: Training feedback.
feedback: User feedback string.
Returns:
Improved answer.
Formatted message dict.
"""
self._handle_crew_training_output(initial_answer, feedback)
self.messages.append(
format_message_for_llm(
self._i18n.slice("feedback_instructions").format(feedback=feedback)
)
return format_message_for_llm(
self._i18n.slice("feedback_instructions").format(feedback=feedback)
)
improved_answer = self._invoke_loop()
self._handle_crew_training_output(improved_answer)
self.ask_for_human_input = False
return improved_answer
def _handle_regular_feedback(
self, current_answer: AgentFinish, initial_feedback: str
) -> AgentFinish:
"""Process regular feedback iteratively.
Args:
current_answer: Current agent output.
initial_feedback: Initial user feedback.
Returns:
Final answer after iterations.
"""
feedback = initial_feedback
answer = current_answer
while self.ask_for_human_input:
# If the user provides a blank response, assume they are happy with the result
if feedback.strip() == "":
self.ask_for_human_input = False
else:
answer = self._process_feedback_iteration(feedback)
output_str = (
answer.output
if isinstance(answer.output, str)
else answer.output.model_dump_json()
)
feedback = self._ask_human_input(output_str)
return answer
def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
"""Process single feedback iteration.
Args:
feedback: User feedback.
Returns:
Updated agent response.
"""
self.messages.append(
format_message_for_llm(
self._i18n.slice("feedback_instructions").format(feedback=feedback)
)
)
return self._invoke_loop()
@classmethod
def __get_pydantic_core_schema__(

1
lib/crewai/src/crewai/core/__init__.py Normal file
View File

@@ -0,0 +1 @@
"""Core crewAI components and interfaces."""

1
lib/crewai/src/crewai/core/providers/__init__.py Normal file
View File

@@ -0,0 +1 @@
"""Provider interfaces for extensible crewAI components."""

78
lib/crewai/src/crewai/core/providers/content_processor.py Normal file
View File

@@ -0,0 +1,78 @@
"""Content processor provider for extensible content processing."""
from contextvars import ContextVar
from typing import Any, Protocol, runtime_checkable
@runtime_checkable
class ContentProcessorProvider(Protocol):
"""Protocol for content processing during task execution."""
def process(self, content: str, context: dict[str, Any] | None = None) -> str:
"""Process content before use.
Args:
content: The content to process.
context: Optional context information.
Returns:
The processed content.
"""
...
class NoOpContentProcessor:
"""Default processor that returns content unchanged."""
def process(self, content: str, context: dict[str, Any] | None = None) -> str:
"""Return content unchanged.
Args:
content: The content to process.
context: Optional context information (unused).
Returns:
The original content unchanged.
"""
return content
_content_processor: ContextVar[ContentProcessorProvider | None] = ContextVar(
"_content_processor", default=None
)
_default_processor = NoOpContentProcessor()
def get_processor() -> ContentProcessorProvider:
"""Get the current content processor.
Returns:
The registered content processor or the default no-op processor.
"""
processor = _content_processor.get()
if processor is not None:
return processor
return _default_processor
def set_processor(processor: ContentProcessorProvider) -> None:
"""Set the content processor for the current context.
Args:
processor: The content processor to use.
"""
_content_processor.set(processor)
def process_content(content: str, context: dict[str, Any] | None = None) -> str:
"""Process content using the registered processor.
Args:
content: The content to process.
context: Optional context information.
Returns:
The processed content.
"""
return get_processor().process(content, context)

304
lib/crewai/src/crewai/core/providers/human_input.py Normal file
View File

@@ -0,0 +1,304 @@
"""Human input provider for HITL (Human-in-the-Loop) flows."""
from __future__ import annotations
from contextvars import ContextVar, Token
from typing import TYPE_CHECKING, Protocol, runtime_checkable
if TYPE_CHECKING:
from crewai.agent.core import Agent
from crewai.agents.parser import AgentFinish
from crewai.crew import Crew
from crewai.llms.base_llm import BaseLLM
from crewai.task import Task
from crewai.utilities.types import LLMMessage
class ExecutorContext(Protocol):
"""Context interface for human input providers to interact with executor."""
task: Task | None
crew: Crew | None
messages: list[LLMMessage]
ask_for_human_input: bool
llm: BaseLLM
agent: Agent
def _invoke_loop(self) -> AgentFinish:
"""Invoke the agent loop and return the result."""
...
def _is_training_mode(self) -> bool:
"""Check if training mode is active."""
...
def _handle_crew_training_output(
self,
result: AgentFinish,
human_feedback: str | None = None,
) -> None:
"""Handle training output."""
...
def _format_feedback_message(self, feedback: str) -> LLMMessage:
"""Format feedback as a message."""
...
@runtime_checkable
class HumanInputProvider(Protocol):
"""Protocol for human input handling.
Implementations handle the full feedback flow:
- Sync: prompt user, loop until satisfied
- Async: raise exception for external handling
"""
def setup_messages(self, context: ExecutorContext) -> bool:
"""Set up messages for execution.
Called before standard message setup. Allows providers to handle
conversation resumption or other custom message initialization.
Args:
context: Executor context with messages list to modify.
Returns:
True if messages were set up (skip standard setup),
False to use standard setup.
"""
...
def post_setup_messages(self, context: ExecutorContext) -> None:
"""Called after standard message setup.
Allows providers to modify messages after standard setup completes.
Only called when setup_messages returned False.
Args:
context: Executor context with messages list to modify.
"""
...
def handle_feedback(
self,
formatted_answer: AgentFinish,
context: ExecutorContext,
) -> AgentFinish:
"""Handle the full human feedback flow.
Args:
formatted_answer: The agent's current answer.
context: Executor context for callbacks.
Returns:
The final answer after feedback processing.
Raises:
Exception: Async implementations may raise to signal external handling.
"""
...
@staticmethod
def _get_output_string(answer: AgentFinish) -> str:
"""Extract output string from answer.
Args:
answer: The agent's finished answer.
Returns:
String representation of the output.
"""
if isinstance(answer.output, str):
return answer.output
return answer.output.model_dump_json()
class SyncHumanInputProvider(HumanInputProvider):
"""Default synchronous human input via terminal."""
def setup_messages(self, context: ExecutorContext) -> bool:
"""Use standard message setup.
Args:
context: Executor context (unused).
Returns:
False to use standard setup.
"""
return False
def post_setup_messages(self, context: ExecutorContext) -> None:
"""No-op for sync provider.
Args:
context: Executor context (unused).
"""
def handle_feedback(
self,
formatted_answer: AgentFinish,
context: ExecutorContext,
) -> AgentFinish:
"""Handle feedback synchronously with terminal prompts.
Args:
formatted_answer: The agent's current answer.
context: Executor context for callbacks.
Returns:
The final answer after feedback processing.
"""
feedback = self._prompt_input(context.crew)
if context._is_training_mode():
return self._handle_training_feedback(formatted_answer, feedback, context)
return self._handle_regular_feedback(formatted_answer, feedback, context)
@staticmethod
def _handle_training_feedback(
initial_answer: AgentFinish,
feedback: str,
context: ExecutorContext,
) -> AgentFinish:
"""Process training feedback (single iteration).
Args:
initial_answer: The agent's initial answer.
feedback: Human feedback string.
context: Executor context for callbacks.
Returns:
Improved answer after processing feedback.
"""
context._handle_crew_training_output(initial_answer, feedback)
context.messages.append(context._format_feedback_message(feedback))
improved_answer = context._invoke_loop()
context._handle_crew_training_output(improved_answer)
context.ask_for_human_input = False
return improved_answer
def _handle_regular_feedback(
self,
current_answer: AgentFinish,
initial_feedback: str,
context: ExecutorContext,
) -> AgentFinish:
"""Process regular feedback with iteration loop.
Args:
current_answer: The agent's current answer.
initial_feedback: Initial human feedback string.
context: Executor context for callbacks.
Returns:
Final answer after all feedback iterations.
"""
feedback = initial_feedback
answer = current_answer
while context.ask_for_human_input:
if feedback.strip() == "":
context.ask_for_human_input = False
else:
context.messages.append(context._format_feedback_message(feedback))
answer = context._invoke_loop()
feedback = self._prompt_input(context.crew)
return answer
@staticmethod
def _prompt_input(crew: Crew | None) -> str:
"""Show rich panel and prompt for input.
Args:
crew: The crew instance for context.
Returns:
User input string from terminal.
"""
from rich.panel import Panel
from rich.text import Text
from crewai.events.event_listener import event_listener
formatter = event_listener.formatter
formatter.pause_live_updates()
try:
if crew and getattr(crew, "_train", False):
prompt_text = (
"TRAINING MODE: Provide feedback to improve the agent's performance.\n\n"
"This will be used to train better versions of the agent.\n"
"Please provide detailed feedback about the result quality and reasoning process."
)
title = "🎓 Training Feedback Required"
else:
prompt_text = (
"Provide feedback on the Final Result above.\n\n"
"• If you are happy with the result, simply hit Enter without typing anything.\n"
"• Otherwise, provide specific improvement requests.\n"
"• You can provide multiple rounds of feedback until satisfied."
)
title = "💬 Human Feedback Required"
content = Text()
content.append(prompt_text, style="yellow")
prompt_panel = Panel(
content,
title=title,
border_style="yellow",
padding=(1, 2),
)
formatter.console.print(prompt_panel)
response = input()
if response.strip() != "":
formatter.console.print("\n[cyan]Processing your feedback...[/cyan]")
return response
finally:
formatter.resume_live_updates()
_provider: ContextVar[HumanInputProvider | None] = ContextVar(
"human_input_provider",
default=None,
)
def get_provider() -> HumanInputProvider:
"""Get the current human input provider.
Returns:
The current provider, or a new SyncHumanInputProvider if none set.
"""
provider = _provider.get()
if provider is None:
initialized_provider = SyncHumanInputProvider()
set_provider(initialized_provider)
return initialized_provider
return provider
def set_provider(provider: HumanInputProvider) -> Token[HumanInputProvider | None]:
"""Set the human input provider for the current context.
Args:
provider: The provider to use.
Returns:
Token that can be used to reset to previous value.
"""
return _provider.set(provider)
def reset_provider(token: Token[HumanInputProvider | None]) -> None:
"""Reset the provider to its previous value.
Args:
token: Token returned from set_provider.
"""
_provider.reset(token)

20
lib/crewai/src/crewai/task.py
View File

@@ -31,6 +31,7 @@ from pydantic_core import PydanticCustomError
from typing_extensions import Self
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.core.providers.content_processor import process_content
from crewai.events.event_bus import crewai_event_bus
from crewai.events.types.task_events import (
TaskCompletedEvent,
@@ -496,6 +497,7 @@ class Task(BaseModel):
tools: list[BaseTool] | None = None,
) -> TaskOutput:
"""Execute the task synchronously."""
self.start_time = datetime.datetime.now()
return self._execute_core(agent, context, tools)
@property
@@ -536,6 +538,7 @@ class Task(BaseModel):
) -> None:
"""Execute the task asynchronously with context handling."""
try:
self.start_time = datetime.datetime.now()
result = self._execute_core(agent, context, tools)
future.set_result(result)
except Exception as e:
@@ -548,6 +551,7 @@ class Task(BaseModel):
tools: list[BaseTool] | None = None,
) -> TaskOutput:
"""Execute the task asynchronously using native async/await."""
self.start_time = datetime.datetime.now()
return await self._aexecute_core(agent, context, tools)
async def _aexecute_core(
@@ -566,8 +570,6 @@ class Task(BaseModel):
f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
)
self.start_time = datetime.datetime.now()
self.prompt_context = context
tools = tools or self.tools or []
@@ -579,6 +581,8 @@ class Task(BaseModel):
tools=tools,
)
self._post_agent_execution(agent)
if not self._guardrails and not self._guardrail:
pydantic_output, json_output = self._export_output(result)
else:
@@ -661,8 +665,6 @@ class Task(BaseModel):
f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
)
self.start_time = datetime.datetime.now()
self.prompt_context = context
tools = tools or self.tools or []
@@ -674,6 +676,8 @@ class Task(BaseModel):
tools=tools,
)
self._post_agent_execution(agent)
if not self._guardrails and not self._guardrail:
pydantic_output, json_output = self._export_output(result)
else:
@@ -741,6 +745,9 @@ class Task(BaseModel):
finally:
clear_task_files(self.id)
def _post_agent_execution(self, agent: BaseAgent) -> None:
pass
def prompt(self) -> str:
"""Generates the task prompt with optional markdown formatting.
@@ -863,6 +870,11 @@ Follow these guidelines:
except ValueError as e:
raise ValueError(f"Error interpolating description: {e!s}") from e
self.description = process_content(self.description, {"task": self})
self._original_expected_output = process_content(
self._original_expected_output, {"task": self}
)
try:
self.expected_output = interpolate_only(
input_string=self._original_expected_output, inputs=inputs

18
lib/crewai/tests/agents/test_agent.py
View File

@@ -703,6 +703,8 @@ def test_agent_definition_based_on_dict():
# test for human input
@pytest.mark.vcr()
def test_agent_human_input():
from crewai.core.providers.human_input import SyncHumanInputProvider
# Agent configuration
config = {
"role": "test role",
@@ -720,7 +722,7 @@ def test_agent_human_input():
human_input=True,
)
# Side effect function for _ask_human_input to simulate multiple feedback iterations
# Side effect function for _prompt_input to simulate multiple feedback iterations
feedback_responses = iter(
[
"Don't say hi, say Hello instead!", # First feedback: instruct change
@@ -728,16 +730,16 @@ def test_agent_human_input():
]
)
def ask_human_input_side_effect(*args, **kwargs):
def prompt_input_side_effect(*args, **kwargs):
return next(feedback_responses)
# Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
# Patch both _prompt_input on provider and _invoke_loop to avoid real API/network calls.
with (
patch.object(
CrewAgentExecutor,
"_ask_human_input",
side_effect=ask_human_input_side_effect,
) as mock_human_input,
SyncHumanInputProvider,
"_prompt_input",
side_effect=prompt_input_side_effect,
) as mock_prompt_input,
patch.object(
CrewAgentExecutor,
"_invoke_loop",
@@ -749,7 +751,7 @@ def test_agent_human_input():
# Assertions to ensure the agent behaves correctly.
# It should have requested feedback twice.
assert mock_human_input.call_count == 2
assert mock_prompt_input.call_count == 2
# The final result should be processed to "Hello"
assert output.strip().lower() == "hello"

57
lib/crewai/tests/test_flow_human_input_integration.py
View File

@@ -2,6 +2,7 @@ from unittest.mock import MagicMock, patch
import pytest
from crewai.events.event_listener import event_listener
from crewai.core.providers.human_input import SyncHumanInputProvider
class TestFlowHumanInputIntegration:
@@ -24,14 +25,9 @@ class TestFlowHumanInputIntegration:
@patch("builtins.input", return_value="")
def test_human_input_pauses_flow_updates(self, mock_input):
"""Test that human input pauses Flow status updates."""
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
executor = CrewAgentExecutorMixin()
executor.crew = MagicMock()
executor.crew._train = False
executor._printer = MagicMock()
provider = SyncHumanInputProvider()
crew = MagicMock()
crew._train = False
formatter = event_listener.formatter
@@ -39,7 +35,7 @@ class TestFlowHumanInputIntegration:
patch.object(formatter, "pause_live_updates") as mock_pause,
patch.object(formatter, "resume_live_updates") as mock_resume,
):
result = executor._ask_human_input("Test result")
result = provider._prompt_input(crew)
mock_pause.assert_called_once()
mock_resume.assert_called_once()
@@ -49,14 +45,9 @@ class TestFlowHumanInputIntegration:
@patch("builtins.input", side_effect=["feedback", ""])
def test_multiple_human_input_rounds(self, mock_input):
"""Test multiple rounds of human input with Flow status management."""
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
executor = CrewAgentExecutorMixin()
executor.crew = MagicMock()
executor.crew._train = False
executor._printer = MagicMock()
provider = SyncHumanInputProvider()
crew = MagicMock()
crew._train = False
formatter = event_listener.formatter
@@ -75,10 +66,10 @@ class TestFlowHumanInputIntegration:
formatter, "resume_live_updates", side_effect=track_resume
),
):
result1 = executor._ask_human_input("Test result 1")
result1 = provider._prompt_input(crew)
assert result1 == "feedback"
result2 = executor._ask_human_input("Test result 2")
result2 = provider._prompt_input(crew)
assert result2 == ""
assert len(pause_calls) == 2
@@ -103,14 +94,9 @@ class TestFlowHumanInputIntegration:
def test_pause_resume_exception_handling(self):
"""Test that resume is called even if exception occurs during human input."""
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
executor = CrewAgentExecutorMixin()
executor.crew = MagicMock()
executor.crew._train = False
executor._printer = MagicMock()
provider = SyncHumanInputProvider()
crew = MagicMock()
crew._train = False
formatter = event_listener.formatter
@@ -122,21 +108,16 @@ class TestFlowHumanInputIntegration:
),
):
with pytest.raises(KeyboardInterrupt):
executor._ask_human_input("Test result")
provider._prompt_input(crew)
mock_pause.assert_called_once()
mock_resume.assert_called_once()
def test_training_mode_human_input(self):
"""Test human input in training mode."""
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
executor = CrewAgentExecutorMixin()
executor.crew = MagicMock()
executor.crew._train = True
executor._printer = MagicMock()
provider = SyncHumanInputProvider()
crew = MagicMock()
crew._train = True
formatter = event_listener.formatter
@@ -146,7 +127,7 @@ class TestFlowHumanInputIntegration:
patch.object(formatter.console, "print") as mock_console_print,
patch("builtins.input", return_value="training feedback"),
):
result = executor._ask_human_input("Test result")
result = provider._prompt_input(crew)
mock_pause.assert_called_once()
mock_resume.assert_called_once()
@@ -161,4 +142,4 @@ class TestFlowHumanInputIntegration:
for call in call_args
if call[0]
)
assert training_panel_found
assert training_panel_found