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Code Issues Actions 5 Packages Projects Releases Wiki Activity

Lorenzejay/byoa (#776)

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* better spacing

* works with llama index

* works on langchain custom just need delegation to work

* cleanup for custom_agent class

* works with different argument expectations for agent_executor

* cleanup for hierarchial process, better agent_executor args handler and added to the crew agent doc page

* removed code examples for langchain + llama index, added to docs instead

* added key output if return is not a str for and added some tests

* added hinting for CustomAgent class

* removed pass as it was not needed

* closer just need to figuire ou agentTools

* running agents - llamaindex and langchain with base agent

* some cleanup on baseAgent

* minimum for agent to run for base class and ensure it works with hierarchical process

* cleanup for original agent to take on BaseAgent class

* Agent takes on langchainagent and cleanup across

* token handling working for usage_metrics to continue working

* installed llama-index, updated docs and added better name

* fixed some type errors

* base agent holds token_process

* heirarchail process uses proper tools and no longer relies on hasattr for token_processes

* removal of test_custom_agent_executions

* this fixes copying agents

* leveraging an executor class for trigger llamaindex agent

* llama index now has ask_human

* executor mixins added

* added output converter base class

* type listed

* cleanup for output conversions and tokenprocess eliminated redundancy

* properly handling tokens

* simplified token calc handling

* original agent with base agent builder structure setup

* better docs

* no more llama-index dep

* cleaner docs

* test fixes

* poetry reverts and better docs

* base_agent_tools set for third party agents

* updated task and test fix
This commit is contained in:
Lorenze Jay
2024-06-27 10:56:08 -07:00
committed by GitHub
parent da9cc5f097
commit 10997dd175
22 changed files with 637 additions and 407 deletions
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57
src/crewai/crew.py
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@@ -18,6 +18,7 @@ from pydantic import (
from pydantic_core import PydanticCustomError
from crewai.agent import Agent
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.agents.cache import CacheHandler
from crewai.memory.entity.entity_memory import EntityMemory
from crewai.memory.long_term.long_term_memory import LongTermMemory
@@ -71,7 +72,7 @@ class Crew(BaseModel):
cache: bool = Field(default=True)
model_config = ConfigDict(arbitrary_types_allowed=True)
tasks: List[Task] = Field(default_factory=list)
agents: List[Agent] = Field(default_factory=list)
agents: List[BaseAgent] = Field(default_factory=list)
process: Process = Field(default=Process.sequential)
verbose: Union[int, bool] = Field(default=0)
memory: bool = Field(
@@ -93,7 +94,7 @@ class Crew(BaseModel):
manager_llm: Optional[Any] = Field(
description="Language model that will run the agent.", default=None
)
manager_agent: Optional[Any] = Field(
manager_agent: Optional[BaseAgent] = Field(
description="Custom agent that will be used as manager.", default=None
)
manager_callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
@@ -288,12 +289,17 @@ class Crew(BaseModel):
i18n = I18N(prompt_file=self.prompt_file)
for agent in self.agents:
# type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
agent.i18n = i18n
agent.crew = self
if not agent.function_calling_llm:
# type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
agent.crew = self # type: ignore[attr-defined]
# TODO: Create an AgentFunctionCalling protocol for future refactoring
if (
hasattr(agent, "function_calling_llm")
and not agent.function_calling_llm
):
agent.function_calling_llm = self.function_calling_llm
if not agent.step_callback:
if hasattr(agent, "step_callback") and not agent.step_callback:
agent.step_callback = self.step_callback
agent.create_agent_executor()
@@ -311,10 +317,10 @@ class Crew(BaseModel):
raise NotImplementedError(
f"The process '{self.process}' is not implemented yet."
)
metrics = metrics + [
agent._token_process.get_summary() for agent in self.agents
]
self.usage_metrics = {
key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
}
@@ -364,13 +370,14 @@ class Crew(BaseModel):
def _run_sequential_process(self) -> str:
"""Executes tasks sequentially and returns the final output."""
task_output = ""
token_usage = []
for task in self.tasks:
if task.agent.allow_delegation: # type: ignore # Item "None" of "Agent | None" has no attribute "allow_delegation"
agents_for_delegation = [
agent for agent in self.agents if agent != task.agent
]
if len(self.agents) > 1 and len(agents_for_delegation) > 0:
task.tools += AgentTools(agents=agents_for_delegation).tools()
task.tools += task.agent.get_delegation_tools(agents_for_delegation)
role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== Working Agent: {role}", color="bold_purple")
@@ -382,7 +389,6 @@ class Crew(BaseModel):
self._file_handler.log(
agent=role, task=task.description, status="started"
)
output = task.execute(context=task_output)
if not task.async_execution:
@@ -390,15 +396,18 @@ class Crew(BaseModel):
role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== [{role}] Task output: {task_output}\n\n")
token_summ = task.agent._token_process.get_summary()
token_usage.append(token_summ)
if self.output_log_file:
self._file_handler.log(agent=role, task=task_output, status="completed")
token_usage_formatted = self.aggregate_token_usage(token_usage)
self._finish_execution(task_output)
# type: ignore # Item "None" of "Agent | None" has no attribute "_token_process"
token_usage = task.agent._token_process.get_summary()
# type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
return self._format_output(task_output, token_usage)
return self._format_output(task_output, token_usage_formatted)
def _run_hierarchical_process(self) -> Union[str, Dict[str, Any]]:
"""Creates and assigns a manager agent to make sure the crew completes the tasks."""
@@ -409,7 +418,7 @@ class Crew(BaseModel):
manager = self.manager_agent
if len(manager.tools) > 0:
raise Exception("Manager agent should not have tools")
manager.tools = AgentTools(agents=self.agents).tools()
manager.tools = self.manager_agent.get_delegation_tools(self.agents)
else:
manager = Agent(
role=i18n.retrieve("hierarchical_manager_agent", "role"),
@@ -421,6 +430,7 @@ class Crew(BaseModel):
)
task_output = ""
token_usage = []
for task in self.tasks:
self._logger.log("debug", f"Working Agent: {manager.role}")
self._logger.log("info", f"Starting Task: {task.description}")
@@ -435,17 +445,23 @@ class Crew(BaseModel):
)
self._logger.log("debug", f"[{manager.role}] Task output: {task_output}")
if hasattr(task, "agent._token_process"):
token_summ = task.agent._token_process.get_summary()
token_usage.append(token_summ)
if self.output_log_file:
self._file_handler.log(
agent=manager.role, task=task_output, status="completed"
)
self._finish_execution(task_output)
# type: ignore # Incompatible return value type (got "tuple[str, Any]", expected "str")
manager_token_usage = manager._token_process.get_summary()
token_usage.append(manager_token_usage)
token_usage_formatted = self.aggregate_token_usage(token_usage)
return self._format_output(
task_output, manager_token_usage
task_output, token_usage_formatted
), manager_token_usage
def copy(self):
@@ -494,10 +510,11 @@ class Crew(BaseModel):
for task in self.tasks
]
# type: ignore # "interpolate_inputs" of "Agent" does not return a value (it only ever returns None)
[agent.interpolate_inputs(inputs) for agent in self.agents]
for agent in self.agents:
agent.interpolate_inputs(inputs)
def _format_output(
self, output: str, token_usage: Optional[Dict[str, Any]]
self, output: str, token_usage: Optional[Dict[str, Any]] = None
) -> Union[str, Dict[str, Any]]:
"""
Formats the output of the crew execution.
@@ -519,3 +536,9 @@ class Crew(BaseModel):
def __repr__(self):
return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
def aggregate_token_usage(self, token_usage_list: List[Dict[str, Any]]):
return {
key: sum([m[key] for m in token_usage_list if m is not None])
for key in token_usage_list[0]
}