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import time
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from langchain.agents import AgentExecutor
from langchain.agents.agent import ExceptionTool
from langchain.agents.tools import InvalidTool
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain_core.agents import AgentAction, AgentFinish, AgentStep
from langchain_core.exceptions import OutputParserException
from langchain_core.pydantic_v1 import root_validator
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from crewai.agents.cache.cache_hit import CacheHit
from crewai.tools.cache_tools import CacheTools
from crewai.utilities import I18N
class CrewAgentExecutor(AgentExecutor):
i18n: I18N = I18N()
iterations: int = 0
request_within_rpm_limit: Any = None
max_iterations: Optional[int] = 15
force_answer_max_iterations: Optional[int] = None
@root_validator()
def set_force_answer_max_iterations(cls, values: Dict) -> Dict:
values["force_answer_max_iterations"] = values["max_iterations"] - 2
return values
def _should_force_answer(self) -> bool:
return True if self.iterations == self.force_answer_max_iterations else False
def _force_answer(self, output: AgentAction):
return AgentStep(
action=output, observation=self.i18n.errors("force_final_answer")
)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green", "red"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
self.iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(self.iterations, time_elapsed):
if not self.request_within_rpm_limit or self.request_within_rpm_limit():
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_manager
)
self.iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
def _iter_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
# Call the LLM to see what to do.
output = self.agent.plan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
if self._should_force_answer():
if isinstance(output, AgentAction) or isinstance(output, AgentFinish):
output = output
elif isinstance(output, CacheHit):
output = output.action
else:
raise ValueError(
f"Unexpected output type from agent: {type(output)}"
)
yield self._force_answer(output)
return
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise ValueError(
"An output parsing error occurred. "
"In order to pass this error back to the agent and have it try "
"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
f"This is the error: {str(e)}"
)
text = str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
if self._should_force_answer():
yield self._force_answer(output)
return
yield AgentStep(action=output, observation=observation)
return
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
yield output
return
# Override tool usage to use CacheTools
if isinstance(output, CacheHit):
cache = output.cache
action = output.action
tool = CacheTools(cache_handler=cache).tool()
output = action.copy()
output.tool_input = f"tool:{action.tool}|input:{action.tool_input}"
output.tool = tool.name
name_to_tool_map[tool.name] = tool
color_mapping[tool.name] = color_mapping[action.tool]
actions: List[AgentAction]
actions = [output] if isinstance(output, AgentAction) else output
yield from actions
for agent_action in actions:
if run_manager:
run_manager.on_agent_action(agent_action, color="green")
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = name_to_tool_map[agent_action.tool]
return_direct = tool.return_direct
color = color_mapping[agent_action.tool]
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = tool.run(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = InvalidTool().run(
{
"requested_tool_name": agent_action.tool,
"available_tool_names": list(name_to_tool_map.keys()),
},
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
yield AgentStep(action=agent_action, observation=observation)
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