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Merge branch 'bugfix/kickoff-for-each-usage-metrics' into feature/kickoff-consistent-output

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This commit is contained in:
Brandon Hancock
2024-07-03 11:36:06 -04:00
parent 55af7e0f15 bae9c70730
commit a3bdc09f2d
20 changed files with 485 additions and 722 deletions
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22
src/crewai/agent.py
View File

@@ -18,7 +18,20 @@ from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_F
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.training_handler import CrewTrainingHandler
agentops = None
try:
import agentops
from agentops import track_agent
except ImportError:
def track_agent():
def noop(f):
return f
return noop
@track_agent()
class Agent(BaseAgent):
"""Represents an agent in a system.
@@ -47,6 +60,8 @@ class Agent(BaseAgent):
default=None,
description="Maximum execution time for an agent to execute a task",
)
agent_ops_agent_name: str = None
agent_ops_agent_id: str = None
cache_handler: InstanceOf[CacheHandler] = Field(
default=None, description="An instance of the CacheHandler class."
)
@@ -82,6 +97,7 @@ class Agent(BaseAgent):
def __init__(__pydantic_self__, **data):
config = data.pop("config", {})
super().__init__(**config, **data)
__pydantic_self__.agent_ops_agent_name = __pydantic_self__.role
@model_validator(mode="after")
def set_agent_executor(self) -> "Agent":
@@ -99,6 +115,12 @@ class Agent(BaseAgent):
):
self.llm.callbacks.append(token_handler)
if agentops and not any(
isinstance(handler, agentops.LangchainCallbackHandler) for handler in self.llm.callbacks
):
agentops.stop_instrumenting()
self.llm.callbacks.append(agentops.LangchainCallbackHandler())
if not self.agent_executor:
if not self.cache_handler:
self.cache_handler = CacheHandler()

3
src/crewai/agents/agent_builder/base_agent.py
View File

@@ -15,8 +15,9 @@ from pydantic import (
)
from pydantic_core import PydanticCustomError
from crewai.agents import CacheHandler, ToolsHandler
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
from crewai.agents.cache.cache_handler import CacheHandler
from crewai.agents.tools_handler import ToolsHandler
from crewai.utilities import I18N, Logger, RPMController
T = TypeVar("T", bound="BaseAgent")

118
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -1,65 +1,109 @@
import time
from typing import TYPE_CHECKING, Optional
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
if TYPE_CHECKING:
from crewai.crew import Crew
from crewai.task import Task
from crewai.agents.agent_builder.base_agent import BaseAgent
class CrewAgentExecutorMixin:
crew: Optional["Crew"]
crew_agent: Optional["BaseAgent"]
task: Optional["Task"]
iterations: int
force_answer_max_iterations: int
have_forced_answer: bool
_i18n: I18N
def _should_force_answer(self) -> bool:
"""Determine if a forced answer is required based on iteration count."""
return (
self.iterations == self.force_answer_max_iterations
) and not self.have_forced_answer
def _create_short_term_memory(self, output) -> None:
"""Create and save a short-term memory item if conditions are met."""
if (
self.crew
and self.crew_agent
and self.task
and "Action: Delegate work to coworker" not in output.log
):
try:
memory = ShortTermMemoryItem(
data=output.log,
agent=self.crew_agent.role,
metadata={
"observation": self.task.description,
},
)
if (
hasattr(self.crew, "_short_term_memory")
and self.crew._short_term_memory
):
self.crew._short_term_memory.save(memory)
except Exception as e:
print(f"Failed to add to short term memory: {e}")
pass
def _create_long_term_memory(self, output) -> None:
"""Create and save long-term and entity memory items based on evaluation."""
if (
self.crew
and self.crew.memory
and "Action: Delegate work to coworker" not in output.log
and self.crew._long_term_memory
and self.crew._entity_memory
and self.task
and self.crew_agent
):
memory = ShortTermMemoryItem(
data=output.log,
agent=self.crew_agent.role,
metadata={
"observation": self.task.description,
},
)
self.crew._short_term_memory.save(memory)
try:
ltm_agent = TaskEvaluator(self.crew_agent)
evaluation = ltm_agent.evaluate(self.task, output.log)
def _create_long_term_memory(self, output) -> None:
if self.crew and self.crew.memory:
ltm_agent = TaskEvaluator(self.crew_agent)
evaluation = ltm_agent.evaluate(self.task, output.log)
if isinstance(evaluation, ConverterError):
return
if isinstance(evaluation, ConverterError):
return
long_term_memory = LongTermMemoryItem(
task=self.task.description,
agent=self.crew_agent.role,
quality=evaluation.quality,
datetime=str(time.time()),
expected_output=self.task.expected_output,
metadata={
"suggestions": evaluation.suggestions,
"quality": evaluation.quality,
},
)
self.crew._long_term_memory.save(long_term_memory)
for entity in evaluation.entities:
entity_memory = EntityMemoryItem(
name=entity.name,
type=entity.type,
description=entity.description,
relationships="\n".join([f"- {r}" for r in entity.relationships]),
long_term_memory = LongTermMemoryItem(
task=self.task.description,
agent=self.crew_agent.role,
quality=evaluation.quality,
datetime=str(time.time()),
expected_output=self.task.expected_output,
metadata={
"suggestions": evaluation.suggestions,
"quality": evaluation.quality,
},
)
self.crew._entity_memory.save(entity_memory)
self.crew._long_term_memory.save(long_term_memory)
for entity in evaluation.entities:
entity_memory = EntityMemoryItem(
name=entity.name,
type=entity.type,
description=entity.description,
relationships="\n".join(
[f"- {r}" for r in entity.relationships]
),
)
self.crew._entity_memory.save(entity_memory)
except AttributeError as e:
print(f"Missing attributes for long term memory: {e}")
pass
except Exception as e:
print(f"Failed to add to long term memory: {e}")
pass
def _ask_human_input(self, final_answer: dict) -> str:
"""Get human input."""
"""Prompt human input for final decision making."""
return input(
self._i18n.slice("getting_input").format(final_answer=final_answer)
)

16
src/crewai/agents/executor.py
View File

@@ -1,6 +1,14 @@
import threading
import time
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from typing import (
Any,
Dict,
Iterator,
List,
Optional,
Tuple,
Union,
)
from langchain.agents import AgentExecutor
from langchain.agents.agent import ExceptionTool
@@ -11,13 +19,15 @@ from langchain_core.exceptions import OutputParserException
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from pydantic import InstanceOf
from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.agent_builder.base_agent_executor_mixin import (
CrewAgentExecutorMixin,
)
from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.utilities import I18N
class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):

2
src/crewai/cli/templates/pyproject.toml
View File

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
[tool.poetry.dependencies]
python = ">=3.10,<=3.13"
crewai = { extras = ["tools"], version = "^0.35.4" }
crewai = { extras = ["tools"], version = "^0.35.8" }
[tool.poetry.scripts]
{{folder_name}} = "{{folder_name}}.main:run"

11
src/crewai/crew.py
View File

@@ -35,6 +35,11 @@ from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities.formatter import aggregate_raw_outputs_from_task_outputs
from crewai.utilities.training_handler import CrewTrainingHandler
try:
import agentops
except ImportError:
agentops = None
class Crew(BaseModel):
"""
@@ -599,6 +604,12 @@ class Crew(BaseModel):
def _finish_execution(self, final_string_output: str) -> None:
if self.max_rpm:
self._rpm_controller.stop_rpm_counter()
if agentops:
agentops.end_session(
end_state="Success",
end_state_reason="Finished Execution",
is_auto_end=True,
)
self._telemetry.end_crew(self, final_string_output)
def calculate_usage_metrics(self) -> Dict[str, int]:

33
src/crewai/tools/tool_usage.py
View File

@@ -11,6 +11,12 @@ from crewai.telemetry import Telemetry
from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
from crewai.utilities import I18N, Converter, ConverterError, Printer
agentops = None
try:
import agentops
except ImportError:
pass
OPENAI_BIGGER_MODELS = ["gpt-4"]
@@ -91,15 +97,16 @@ class ToolUsage:
self.task.increment_tools_errors()
self._printer.print(content=f"\n\n{error}\n", color="red")
return error
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}" # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
def _use(
self,
tool_string: str,
tool: BaseTool,
calling: Union[ToolCalling, InstructorToolCalling],
) -> str: # TODO: Fix this return type --> finecwg : I updated return type to str
if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
) -> str: # TODO: Fix this return type
tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None
if self._check_tool_repeated_usage(calling=calling): # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
try:
result = self._i18n.errors("task_repeated_usage").format(
tool_names=self.tools_names
@@ -110,13 +117,13 @@ class ToolUsage:
tool_name=tool.name,
attempts=self._run_attempts,
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
return result # type: ignore # Fix the reutrn type of this function
except Exception:
self.task.increment_tools_errors()
result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
result = None # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
if self.tools_handler.cache:
result = self.tools_handler.cache.read( # type: ignore # Incompatible types in assignment (expression has type "str | None", variable has type "str")
@@ -133,7 +140,7 @@ class ToolUsage:
if calling.arguments:
try:
acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
acceptable_args = tool.args_schema.schema()["properties"].keys() # type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "schema"
arguments = {
k: v
for k, v in calling.arguments.items()
@@ -145,7 +152,7 @@ class ToolUsage:
arguments = calling.arguments
result = tool._run(**arguments)
else:
arguments = calling.arguments.values() # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
arguments = calling.arguments.values() # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
result = tool._run(*arguments)
else:
result = tool._run()
@@ -164,6 +171,10 @@ class ToolUsage:
return error # type: ignore # No return value expected
self.task.increment_tools_errors()
if agentops:
agentops.record(
agentops.ErrorEvent(exception=e, trigger_event=tool_event)
)
return self.use(calling=calling, tool_string=tool_string) # type: ignore # No return value expected
if self.tools_handler:
@@ -184,18 +195,20 @@ class ToolUsage:
)
self._printer.print(content=f"\n\n{result}\n", color="purple")
if agentops:
agentops.record(tool_event)
self._telemetry.tool_usage(
llm=self.function_calling_llm,
tool_name=tool.name,
attempts=self._run_attempts,
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
)
result = self._format_result(result=result) # type: ignore # "_format_result" of "ToolUsage" does not return a value (it only ever returns None)
return result # type: ignore # No return value expected
def _format_result(self, result: Any) -> None:
self.task.used_tools += 1
if self._should_remember_format(): # type: ignore # "_should_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
result = self._remember_format(result=result) # type: ignore # "_remember_format" of "ToolUsage" does not return a value (it only ever returns None)
return result
def _should_remember_format(self) -> None:

12
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -5,6 +5,17 @@ from pydantic import BaseModel, Field
from crewai.utilities import Converter
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
agentops = None
try:
import agentops
from agentops import track_agent
except ImportError:
def track_agent(name):
def noop(f):
return f
return noop
class Entity(BaseModel):
@@ -38,6 +49,7 @@ class TrainingTaskEvaluation(BaseModel):
)
@track_agent(name="Task Evaluator")
class TaskEvaluator:
def __init__(self, original_agent):
self.llm = original_agent.llm

2
src/crewai/utilities/logger.py
View File

@@ -1,7 +1,7 @@
from datetime import datetime
from crewai.utilities.printer import Printer
from datetime import datetime
class Logger:
_printer = Printer()