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import uuid
from typing import Any, List, Optional
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.memory import ConversationSummaryMemory
from langchain.tools.render import render_text_description
from langchain_core.runnables.config import RunnableConfig
from langchain_openai import ChatOpenAI
from pydantic import (
UUID4,
BaseModel,
ConfigDict,
Field,
InstanceOf,
field_validator,
model_validator,
)
from pydantic_core import PydanticCustomError
from crewai.agents import (
CacheHandler,
CrewAgentExecutor,
CrewAgentOutputParser,
ToolsHandler,
)
from crewai.prompts import Prompts
class Agent(BaseModel):
"""Represents an agent in a system.
Each agent has a role, a goal, a backstory, and an optional language model (llm).
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.
role: The role of the agent.
goal: The objective of the agent.
backstory: The backstory of the agent.
llm: The language model that will run the agent.
max_iter: Maximum number of iterations for an agent to execute a task.
memory: Whether the agent should have memory or not.
verbose: Whether the agent execution should be in verbose mode.
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
"""
__hash__ = object.__hash__
model_config = ConfigDict(arbitrary_types_allowed=True)
id: UUID4 = Field(
default_factory=uuid.uuid4,
frozen=True,
description="Unique identifier for the object, not set by user.",
)
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
backstory: str = Field(description="Backstory of the agent")
llm: Optional[Any] = Field(
default_factory=lambda: ChatOpenAI(
temperature=0.7,
model_name="gpt-4",
),
description="Language model that will run the agent.",
)
memory: bool = Field(
default=True, description="Whether the agent should have memory or not"
)
verbose: bool = Field(
default=False, description="Verbose mode for the Agent Execution"
)
allow_delegation: bool = Field(
default=True, description="Allow delegation of tasks to agents"
)
tools: List[Any] = Field(
default_factory=list, description="Tools at agents disposal"
)
max_iter: Optional[int] = Field(
default=15, description="Maximum iterations for an agent to execute a task"
)
agent_executor: Optional[InstanceOf[CrewAgentExecutor]] = Field(
default=None, description="An instance of the CrewAgentExecutor class."
)
tools_handler: Optional[InstanceOf[ToolsHandler]] = Field(
default=None, description="An instance of the ToolsHandler class."
)
cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
default=CacheHandler(), description="An instance of the CacheHandler class."
)
@field_validator("id", mode="before")
@classmethod
def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
if v:
raise PydanticCustomError(
"may_not_set_field", "This field is not to be set by the user.", {}
)
@model_validator(mode="after")
def check_agent_executor(self) -> "Agent":
if not self.agent_executor:
self.set_cache_handler(self.cache_handler)
return self
def execute_task(
self, task: str, context: str = None, tools: List[Any] = None
) -> str:
"""Execute a task with the agent.
Args:
task: Task to execute.
context: Context to execute the task in.
tools: Tools to use for the task.
Returns:
Output of the agent
"""
if context:
task = "\n".join(
[task, "\nThis is the context you are working with:", context]
)
tools = tools or self.tools
self.agent_executor.tools = tools
return self.agent_executor.invoke(
{
"input": task,
"tool_names": self.__tools_names(tools),
"tools": render_text_description(tools),
},
RunnableConfig(callbacks=[self.tools_handler]),
)["output"]
def set_cache_handler(self, cache_handler) -> None:
self.cache_handler = cache_handler
self.tools_handler = ToolsHandler(cache=self.cache_handler)
self.__create_agent_executor()
def __create_agent_executor(self) -> CrewAgentExecutor:
"""Create an agent executor for the agent.
Returns:
An instance of the CrewAgentExecutor class.
"""
agent_args = {
"input": lambda x: x["input"],
"tools": lambda x: x["tools"],
"tool_names": lambda x: x["tool_names"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
executor_args = {
"tools": self.tools,
"verbose": self.verbose,
"handle_parsing_errors": True,
"max_iterations": self.max_iter,
}
if self.memory:
summary_memory = ConversationSummaryMemory(
llm=self.llm, memory_key="chat_history", input_key="input"
)
executor_args["memory"] = summary_memory
agent_args["chat_history"] = lambda x: x["chat_history"]
prompt = Prompts().task_execution_with_memory()
else:
prompt = Prompts().task_execution()
execution_prompt = prompt.partial(
goal=self.goal,
role=self.role,
backstory=self.backstory,
)
bind = self.llm.bind(stop=["\nObservation"])
inner_agent = (
agent_args
| execution_prompt
| bind
| CrewAgentOutputParser(
tools_handler=self.tools_handler, cache=self.cache_handler
)
)
self.agent_executor = CrewAgentExecutor(agent=inner_agent, **executor_args)
@staticmethod
def __tools_names(tools) -> str:
return ", ".join([t.name for t in tools])
'''
import uuid
from typing import Any, List, Optional
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
# from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.memory import ConversationSummaryMemory
from langchain.tools.render import render_text_description
from langchain_core.runnables.config import RunnableConfig
from pydantic import (
UUID4,
BaseModel,
ConfigDict,
Field,
InstanceOf,
field_validator,
model_validator,
)
from pydantic_core import PydanticCustomError
from crewai.agents import (
CacheHandler,
CrewAgentExecutor,
CrewAgentOutputParser,
ToolsHandler,
)
from crewai.prompts import Prompts
class Agent(BaseModel):
"""Represents an agent in a system.
Each agent has a role, a goal, a backstory, and an optional language model (llm).
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.
role: The role of the agent.
goal: The objective of the agent.
backstory: The backstory of the agent.
llm: The language model that will run the agent.
memory: Whether the agent should have memory or not.
verbose: Whether the agent execution should be in verbose mode.
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
"""
__hash__ = object.__hash__
model_config = ConfigDict(arbitrary_types_allowed=True)
id: UUID4 = Field(
default_factory=uuid.uuid4,
frozen=True,
description="Unique identifier for the object, not set by user.",
)
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
backstory: str = Field(description="Backstory of the agent")
llm: Optional[Any] = Field(
default_factory=lambda: ChatOpenAI(
temperature=0.7,
model_name="gpt-4",
),
description="Language model that will run the agent.",
)
memory: bool = Field(
default=True, description="Whether the agent should have memory or not"
)
verbose: bool = Field(
default=False, description="Verbose mode for the Agent Execution"
)
allow_delegation: bool = Field(
default=True, description="Allow delegation of tasks to agents"
)
tools: List[Any] = Field(
default_factory=list, description="Tools at agents disposal"
)
agent_executor: Optional[InstanceOf[CrewAgentExecutor]] = Field(
default=None, description="An instance of the CrewAgentExecutor class."
)
tools_handler: Optional[InstanceOf[ToolsHandler]] = Field(
default=None, description="An instance of the ToolsHandler class."
)
cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
default=CacheHandler(), description="An instance of the CacheHandler class."
)
@field_validator("id", mode="before")
@classmethod
def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
if v:
raise PydanticCustomError(
"may_not_set_field", "This field is not to be set by the user.", {}
)
@model_validator(mode="after")
def check_agent_executor(self) -> "Agent":
if not self.agent_executor:
self.set_cache_handler(self.cache_handler)
return self
def execute_task(
self, task: str, context: str = None, tools: List[Any] = None
) -> str:
"""Execute a task with the agent.
Args:
task: Task to execute.
context: Context to execute the task in.
tools: Tools to use for the task.
Returns:
Output of the agent
"""
if context:
task = "\n".join(
[task, "\nThis is the context you are working with:", context]
)
tools = tools or self.tools
self.agent_executor.tools = tools
return self.agent_executor.invoke(
{
"input": task,
"tool_names": self.__tools_names(tools),
"tools": render_text_description(tools),
},
RunnableConfig(callbacks=[self.tools_handler]),
)["output"]
def set_cache_handler(self, cache_handler) -> None:
self.cache_handler = cache_handler
self.tools_handler = ToolsHandler(cache=self.cache_handler)
self.__create_agent_executor()
def __create_agent_executor(self) -> CrewAgentExecutor:
"""Create an agent executor for the agent.
Returns:
An instance of the CrewAgentExecutor class.
"""
agent_args = {
"input": lambda x: x["input"],
"tools": lambda x: x["tools"],
"tool_names": lambda x: x["tool_names"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
executor_args = {
"tools": self.tools,
"verbose": self.verbose,
"handle_parsing_errors": True,
}
if self.memory:
summary_memory = ConversationSummaryMemory(
llm=self.llm, memory_key="chat_history", input_key="input"
)
executor_args["memory"] = summary_memory
agent_args["chat_history"] = lambda x: x["chat_history"]
prompt = Prompts.TASK_EXECUTION_WITH_MEMORY_PROMPT
else:
prompt = Prompts.TASK_EXECUTION_PROMPT
execution_prompt = prompt.partial(
goal=self.goal,
role=self.role,
backstory=self.backstory,
)
bind = self.llm.bind(stop=["\nObservation"])
inner_agent = (
agent_args
| execution_prompt
| bind
| CrewAgentOutputParser(
tools_handler=self.tools_handler, cache=self.cache_handler
)
)
self.agent_executor = CrewAgentExecutor(agent=inner_agent, **executor_args)
@staticmethod
def __tools_names(tools) -> str:
return ", ".join([t.name for t in tools])
'''
|