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crewai/__init__.py

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+from crewai.agent import Agent
+from crewai.crew import Crew
+from crewai.process import Process
+from crewai.task import Task

crewai/agent.py

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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])
+
+'''

crewai/agents/__init__.py

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+from .cache.cache_handler import CacheHandler
+from .executor import CrewAgentExecutor
+from .output_parser import CrewAgentOutputParser
+from .tools_handler import ToolsHandler

crewai/agents/cache/__init__.py

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+from .cache_handler import CacheHandler
+from .cache_hit import CacheHit

crewai/agents/cache/cache_handler.py

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+from typing import Optional
+
+from pydantic import PrivateAttr
+
+
+class CacheHandler:
+    """Callback handler for tool usage."""
+
+    _cache: PrivateAttr = {}
+
+    def __init__(self):
+        self._cache = {}
+
+    def add(self, tool, input, output):
+        input = input.strip()
+        self._cache[f"{tool}-{input}"] = output
+
+    def read(self, tool, input) -> Optional[str]:
+        input = input.strip()
+        return self._cache.get(f"{tool}-{input}")

crewai/agents/cache/cache_hit.py

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+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from .cache_handler import CacheHandler
+
+
+class CacheHit(BaseModel):
+    """Cache Hit Object."""
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    # Making it Any instead of AgentAction to avoind
+    # pydantic v1 vs v2 incompatibility, langchain should
+    # soon be updated to pydantic v2
+    action: Any = Field(description="Action taken")
+    cache: CacheHandler = Field(description="Cache Handler for the tool")

crewai/agents/exceptions.py

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+from langchain_core.exceptions import OutputParserException
+
+
+class TaskRepeatedUsageException(OutputParserException):
+    """Exception raised when a task is used twice in a roll."""
+
+    error: str = "TaskRepeatedUsageException"
+    message: str = "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
+
+    def __init__(self, tool: str, tool_input: str, text: str):
+        self.text = text
+        self.tool = tool
+        self.tool_input = tool_input
+        self.message = self.message.format(tool=tool, tool_input=tool_input)
+
+        super().__init__(
+            error=self.error,
+            observation=self.message,
+            send_to_llm=True,
+            llm_output=self.text,
+        )
+
+    def __str__(self):
+        return self.message

crewai/agents/executor.py

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+import time
+from textwrap import dedent
+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 ..tools.cache_tools import CacheTools
+from .cache.cache_hit import CacheHit
+
+
+class CrewAgentExecutor(AgentExecutor):
+    iterations: int = 0
+    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=dedent(
+                """\
+            I've used too many tools for this task.
+            I'm going to give you my absolute BEST Final answer now and
+            not use any more tools."""
+            ),
+        )
+
+    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):
+            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):
+                    output = output
+                else:
+                    output = output.action
+                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)

crewai/agents/output_parser.py

@@ -0,0 +1,75 @@
+import re
+from typing import Union
+
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain_core.agents import AgentAction, AgentFinish
+
+from .cache import CacheHandler, CacheHit
+from .exceptions import TaskRepeatedUsageException
+from .tools_handler import ToolsHandler
+
+FINAL_ANSWER_ACTION = "Final Answer:"
+FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
+    "Parsing LLM output produced both a final answer and a parse-able action:"
+)
+
+
+class CrewAgentOutputParser(ReActSingleInputOutputParser):
+    """Parses ReAct-style LLM calls that have a single tool input.
+
+    Expects output to be in one of two formats.
+
+    If the output signals that an action should be taken,
+    should be in the below format. This will result in an AgentAction
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Action: search
+    Action Input: what is the temperature in SF?
+    ```
+
+    If the output signals that a final answer should be given,
+    should be in the below format. This will result in an AgentFinish
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Final Answer: The temperature is 100 degrees
+    ```
+
+    It also prevents tools from being reused in a roll.
+    """
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    tools_handler: ToolsHandler
+    cache: CacheHandler
+
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
+        FINAL_ANSWER_ACTION in text
+        regex = (
+            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
+        )
+        if action_match := re.search(regex, text, re.DOTALL):
+            action = action_match.group(1).strip()
+            action_input = action_match.group(2)
+            tool_input = action_input.strip(" ")
+            tool_input = tool_input.strip('"')
+
+            if last_tool_usage := self.tools_handler.last_used_tool:
+                usage = {
+                    "tool": action,
+                    "input": tool_input,
+                }
+                if usage == last_tool_usage:
+                    raise TaskRepeatedUsageException(
+                        tool=action, tool_input=tool_input, text=text
+                    )
+
+            if result := self.cache.read(action, tool_input):
+                action = AgentAction(action, tool_input, text)
+                return CacheHit(action=action, cache=self.cache)
+
+        return super().parse(text)

crewai/agents/tools_handler.py

@@ -0,0 +1,44 @@
+from typing import Any, Dict
+
+from langchain.callbacks.base import BaseCallbackHandler
+
+from ..tools.cache_tools import CacheTools
+from .cache.cache_handler import CacheHandler
+
+
+class ToolsHandler(BaseCallbackHandler):
+    """Callback handler for tool usage."""
+
+    last_used_tool: Dict[str, Any] = {}
+    cache: CacheHandler = None
+
+    def __init__(self, cache: CacheHandler = None, **kwargs: Any):
+        """Initialize the callback handler."""
+        self.cache = cache
+        super().__init__(**kwargs)
+
+    def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> Any:
+        """Run when tool starts running."""
+        name = serialized.get("name")
+        if name not in ["invalid_tool", "_Exception"]:
+            tools_usage = {
+                "tool": name,
+                "input": input_str,
+            }
+            self.last_used_tool = tools_usage
+
+    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
+        """Run when tool ends running."""
+        if (
+            "is not a valid tool" not in output
+            and "Invalid or incomplete response" not in output
+            and "Invalid Format" not in output
+        ):
+            if self.last_used_tool["tool"] != CacheTools().name:
+                self.cache.add(
+                    tool=self.last_used_tool["tool"],
+                    input=self.last_used_tool["input"],
+                    output=output,
+                )

crewai/crew.py

@@ -0,0 +1,132 @@
+import json
+import uuid
+from typing import Any, Dict, List, Optional, Union
+
+from pydantic import (
+    UUID4,
+    BaseModel,
+    ConfigDict,
+    Field,
+    InstanceOf,
+    Json,
+    field_validator,
+    model_validator,
+)
+from pydantic_core import PydanticCustomError
+
+from crewai.agent import Agent
+from crewai.agents.cache import CacheHandler
+from crewai.process import Process
+from crewai.task import Task
+from crewai.tools.agent_tools import AgentTools
+
+
+class Crew(BaseModel):
+    """
+    Represents a group of agents, defining how they should collaborate and the tasks they should perform.
+
+    Attributes:
+        tasks: List of tasks assigned to the crew.
+        agents: List of agents part of this crew.
+        process: The process flow that the crew will follow (e.g., sequential).
+        verbose: Indicates the verbosity level for logging during execution.
+        config: Configuration settings for the crew.
+        cache_handler: Handles caching for the crew's operations.
+        id: A unique identifier for the crew instance.
+    """
+
+    __hash__ = object.__hash__
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    tasks: List[Task] = Field(default_factory=list)
+    agents: List[Agent] = Field(default_factory=list)
+    process: Process = Field(default=Process.sequential)
+    verbose: Union[int, bool] = Field(default=0)
+    config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
+    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(default=CacheHandler())
+    id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
+
+    @field_validator("id", mode="before")
+    @classmethod
+    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
+        """Prevent manual setting of the 'id' field by users."""
+        if v:
+            raise PydanticCustomError(
+                "may_not_set_field", "The 'id' field cannot be set by the user.", {}
+            )
+
+    @classmethod
+    @field_validator("config", mode="before")
+    def check_config_type(cls, v: Union[Json, Dict[str, Any]]):
+        return json.loads(v) if isinstance(v, Json) else v
+
+    @model_validator(mode="after")
+    def check_config(self):
+        """Validates that the crew is properly configured with agents and tasks."""
+        if not self.config and not self.tasks and not self.agents:
+            raise PydanticCustomError(
+                "missing_keys",
+                "Either 'agents' and 'tasks' need to be set or 'config'.",
+                {},
+            )
+
+        if self.config:
+            self._setup_from_config()
+
+        if self.agents:
+            for agent in self.agents:
+                agent.set_cache_handler(self.cache_handler)
+        return self
+
+    def _setup_from_config(self):
+        """Initializes agents and tasks from the provided config."""
+        if not self.config.get("agents") or not self.config.get("tasks"):
+            raise PydanticCustomError(
+                "missing_keys_in_config", "Config should have 'agents' and 'tasks'.", {}
+            )
+
+        self.agents = [Agent(**agent) for agent in self.config["agents"]]
+        self.tasks = [self._create_task(task) for task in self.config["tasks"]]
+
+    def _create_task(self, task_config):
+        """Creates a task instance from its configuration."""
+        task_agent = next(
+            agt for agt in self.agents if agt.role == task_config["agent"]
+        )
+        del task_config["agent"]
+        return Task(**task_config, agent=task_agent)
+
+    def kickoff(self) -> str:
+        """Starts the crew to work on its assigned tasks."""
+        for agent in self.agents:
+            agent.cache_handler = self.cache_handler
+
+        if self.process == Process.sequential:
+            return self._sequential_loop()
+
+    def _sequential_loop(self) -> str:
+        """Executes tasks sequentially and returns the final output."""
+        task_output = None
+        for task in self.tasks:
+            self._prepare_and_execute_task(task)
+            task_output = task.execute(task_output)
+            self._log(
+                "debug", f"\n\n[{task.agent.role}] Task output: {task_output}\n\n"
+            )
+        return task_output
+
+    def _prepare_and_execute_task(self, task):
+        """Prepares and logs information about the task being executed."""
+        if task.agent.allow_delegation:
+            task.tools += AgentTools(agents=self.agents).tools()
+
+        self._log("debug", f"Working Agent: {task.agent.role}")
+        self._log("info", f"Starting Task: {task.description}")
+
+    def _log(self, level, message):
+        """Logs a message at the specified verbosity level."""
+        level_map = {"debug": 1, "info": 2}
+        verbose_level = (
+            2 if isinstance(self.verbose, bool) and self.verbose else self.verbose
+        )
+        if verbose_level and level_map[level] <= verbose_level:
+            print(message)

crewai/process.py

@@ -0,0 +1,11 @@
+from enum import Enum
+
+
+class Process(str, Enum):
+    """
+    Class representing the different processes that can be used to tackle tasks
+    """
+
+    sequential = "sequential"
+    # TODO: consensual = 'consensual'
+    # TODO: hierarchical = 'hierarchical'

crewai/prompts.py

@@ -0,0 +1,57 @@
+import json
+import os
+from typing import ClassVar, Dict, Optional
+
+from langchain.prompts import PromptTemplate
+from pydantic import BaseModel, Field, PrivateAttr, ValidationError, model_validator
+
+
+class Prompts(BaseModel):
+    """Manages and generates prompts for a generic agent with support for different languages."""
+
+    _prompts: Optional[Dict[str, str]] = PrivateAttr()
+    language: Optional[str] = Field(
+        default="en",
+        description="Language of the prompts.",
+    )
+
+    @model_validator(mode="after")
+    def load_prompts(self) -> "Prompts":
+        """Load prompts from a JSON file based on the specified language."""
+        try:
+            dir_path = os.path.dirname(os.path.realpath(__file__))
+            prompts_path = os.path.join(dir_path, f"prompts/{self.language}.json")
+
+            with open(prompts_path, "r") as f:
+                self._prompts = json.load(f)["slices"]
+        except FileNotFoundError:
+            raise ValidationError(
+                f"Prompt file for language '{self.language}' not found."
+            )
+        except json.JSONDecodeError:
+            raise ValidationError(f"Error decoding JSON from the prompts file.")
+        return self
+
+    SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
+
+    def task_execution_with_memory(self) -> str:
+        """Generate a prompt for task execution with memory components."""
+        return self._build_prompt(["role_playing", "tools", "memory", "task"])
+
+    def task_execution_without_tools(self) -> str:
+        """Generate a prompt for task execution without tools components."""
+        return self._build_prompt(["role_playing", "task"])
+
+    def task_execution(self) -> str:
+        """Generate a standard prompt for task execution."""
+        return self._build_prompt(["role_playing", "tools", "task"])
+
+    def _build_prompt(self, components: [str]) -> str:
+        """Constructs a prompt string from specified components."""
+        prompt_parts = [
+            self._prompts[component]
+            for component in components
+            if component in self._prompts
+        ]
+        prompt_parts.append(self.SCRATCHPAD_SLICE)
+        return PromptTemplate.from_template("".join(prompt_parts))

crewai/prompts/en.json

@@ -0,0 +1,8 @@
+{
+  "slices": {
+    "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
+    "memory": "This is the summary of your work so far:\n{chat_history}",
+    "role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
+    "tools": "TOOLS:\n------\nYou have access to the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]"
+  }
+}

crewai/task.py

@@ -0,0 +1,61 @@
+import uuid
+from typing import Any, List, Optional
+
+from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
+from pydantic_core import PydanticCustomError
+
+from crewai.agent import Agent
+from crewai.tasks.task_output import TaskOutput
+
+
+class Task(BaseModel):
+    """Class that represent a task to be executed."""
+
+    __hash__ = object.__hash__
+    description: str = Field(description="Description of the actual task.")
+    agent: Optional[Agent] = Field(
+        description="Agent responsible for the task.", default=None
+    )
+    tools: List[Any] = Field(
+        default_factory=list,
+        description="Tools the agent are limited to use for this task.",
+    )
+    output: Optional[TaskOutput] = Field(
+        description="Task output, it's final result.", default=None
+    )
+    id: UUID4 = Field(
+        default_factory=uuid.uuid4,
+        frozen=True,
+        description="Unique identifier for the object, not set by user.",
+    )
+
+    @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_tools(self):
+        if not self.tools and (self.agent and self.agent.tools):
+            self.tools.extend(self.agent.tools)
+        return self
+
+    def execute(self, context: str = None) -> str:
+        """Execute the task.
+
+        Returns:
+            Output of the task.
+        """
+        if not self.agent:
+            raise Exception(
+                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, either consensual or hierarchical."
+            )
+        result = self.agent.execute_task(
+            task=self.description, context=context, tools=self.tools
+        )
+
+        self.output = TaskOutput(description=self.description, result=result)
+        return result

crewai/tasks/task_output.py

@@ -0,0 +1,17 @@
+from typing import Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class TaskOutput(BaseModel):
+    """Class that represents the result of a task."""
+
+    description: str = Field(description="Description of the task")
+    summary: Optional[str] = Field(description="Summary of the task", default=None)
+    result: str = Field(description="Result of the task")
+
+    @model_validator(mode="after")
+    def set_summary(self):
+        excerpt = " ".join(self.description.split(" ")[:10])
+        self.summary = f"{excerpt}..."
+        return self

crewai/tools/agent_tools.py

@@ -0,0 +1,72 @@
+from textwrap import dedent
+from typing import List
+
+from langchain.tools import Tool
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent
+
+
+class AgentTools(BaseModel):
+    """Tools for generic agent."""
+
+    agents: List[Agent] = Field(description="List of agents in this crew.")
+
+    def tools(self):
+        return [
+            Tool.from_function(
+                func=self.delegate_work,
+                name="Delegate work to co-worker",
+                description=dedent(
+                    f"""Useful to delegate a specific task to one of the
+				following co-workers: [{', '.join([agent.role for agent in self.agents])}].
+				The input to this tool should be a pipe (|) separated text of length
+				three, representing the role you want to delegate it to, the task and
+				information necessary. For example, `coworker|task|information`.
+				"""
+                ),
+            ),
+            Tool.from_function(
+                func=self.ask_question,
+                name="Ask question to co-worker",
+                description=dedent(
+                    f"""Useful to ask a question, opinion or take from on
+				of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
+				The input to this tool should be a pipe (|) separated text of length
+				three, representing the role you want to ask it to, the question and
+				information necessary. For example, `coworker|question|information`.
+				"""
+                ),
+            ),
+        ]
+
+    def delegate_work(self, command):
+        """Useful to delegate a specific task to a coworker."""
+        return self.__execute(command)
+
+    def ask_question(self, command):
+        """Useful to ask a question, opinion or take from a coworker."""
+        return self.__execute(command)
+
+    def __execute(self, command):
+        """Execute the command."""
+        try:
+            agent, task, information = command.split("|")
+        except ValueError:
+            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|information`.\n"
+
+        if not agent or not task or not information:
+            return "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|question|information`.\n"
+
+        agent = [
+            available_agent
+            for available_agent in self.agents
+            if available_agent.role == agent
+        ]
+
+        if len(agent) == 0:
+            return f"\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {', '.join([agent.role for agent in self.agents])}.\n"
+
+        agent = agent[0]
+        result = agent.execute_task(task, information)
+        return result

crewai/tools/browser_tools.py

@@ -0,0 +1,38 @@
+import json
+import os
+
+import requests
+from crewai import Agent, Task
+from langchain.tools import tool
+from unstructured.partition.html import partition_html
+
+
+class BrowserTools():
+
+  @tool("Scrape website content")
+  def scrape_and_summarize_website(website):
+    """Useful to scrape and summarize a website content"""
+    url = f"https://chrome.browserless.io/content?token={os.environ['BROWSERLESS_API_KEY']}"
+    payload = json.dumps({"url": website})
+    headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
+    response = requests.request("POST", url, headers=headers, data=payload)
+    elements = partition_html(text=response.text)
+    content = "\n\n".join([str(el) for el in elements])
+    content = [content[i:i + 8000] for i in range(0, len(content), 8000)]
+    summaries = []
+    for chunk in content:
+      agent = Agent(
+          role='Principal Researcher',
+          goal=
+          'Do amazing research and summaries based on the content you are working with',
+          backstory=
+          "You're a Principal Researcher at a big company and you need to do research about a given topic.",
+          allow_delegation=False)
+      task = Task(
+          agent=agent,
+          description=
+          f'Analyze and summarize the content below, make sure to include the most relevant information in the summary, return only the summary nothing else.\n\nCONTENT\n----------\n{chunk}'
+      )
+      summary = task.execute()
+      summaries.append(summary)
+    return "\n\n".join(summaries)

crewai/tools/cache_tools.py

@@ -0,0 +1,28 @@
+from langchain.tools import Tool
+from pydantic import BaseModel, ConfigDict, Field
+
+from crewai.agents.cache import CacheHandler
+
+
+class CacheTools(BaseModel):
+    """Default tools to hit the cache."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    name: str = "Hit Cache"
+    cache_handler: CacheHandler = Field(
+        description="Cache Handler for the crew",
+        default=CacheHandler(),
+    )
+
+    def tool(self):
+        return Tool.from_function(
+            func=self.hit_cache,
+            name=self.name,
+            description="Reads directly from the cache",
+        )
+
+    def hit_cache(self, key):
+        split = key.split("tool:")
+        tool = split[1].split("|input:")[0].strip()
+        tool_input = split[1].split("|input:")[1].strip()
+        return self.cache_handler.read(tool, tool_input)

crewai/tools/calculator_tools.py

@@ -0,0 +1,13 @@
+from langchain.tools import tool
+
+
+class CalculatorTools():
+
+  @tool("Make a calcualtion")
+  def calculate(operation):
+    """Useful to perform any mathematica calculations, 
+    like sum, minus, mutiplcation, division, etc.
+    The input to this tool should be a mathematical 
+    expression, a couple examples are `200*7` or `5000/2*10`
+    """
+    return eval(operation)

crewai/tools/clarifai_tools.py

@@ -0,0 +1 @@
+# clarify tools

crewai/tools/gemini_tools.py

@@ -0,0 +1,66 @@
+# tools created using gemini
+
+import json
+import os
+
+import google.generativeai as genai
+from google.api_core import exceptions
+
+# Retrieve API Key from Environment Variable
+GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_API_KEY')
+
+# Ensure the API key is available
+if not GOOGLE_AI_STUDIO:
+    raise ValueError("API key not found. Please set the GOOGLE_AI_STUDIO2 environment variable.")
+
+import requests
+from langchain.tools import tool
+
+# Rest of your code remains the same
+genai.configure(api_key=GOOGLE_AI_STUDIO)
+model = genai.GenerativeModel('gemini-pro')
+
+class GeminiSearchTools():
+  @tool("Gemini search the internet")
+  def gemini_search(query):
+    """
+    Searches for content based on the provided query using the Gemini model.
+    Handles DeadlineExceeded exceptions from the Google API.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: The response text from the Gemini model or an error message.
+    """
+    try:
+        response = model.generate_content(query)
+        return response.text
+    except exceptions.DeadlineExceeded as e:
+        # Handle the DeadlineExceeded exception here
+        print("Error: Deadline Exceeded -", str(e))
+        # You can return a custom message or take other appropriate actions
+        return "Error: The request timed out. Please try again later."
+
+        
+
+  @tool("Gemini search news on the internet")
+  def gemini_search_news(query):
+    """
+    Searches for content based on the provided query using the Gemini model.
+    Handles DeadlineExceeded exceptions from the Google API.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        str: The response text from the Gemini model or an error message.
+    """
+    try:
+        response = model.generate_content(query)
+        return response.text
+    except exceptions.DeadlineExceeded as e:
+        # Handle the DeadlineExceeded exception here
+        print("Error: Deadline Exceeded -", str(e))
+        # You can return a custom message or take other appropriate actions
+        return "Error: The request timed out. Please try again later."

crewai/tools/mixtral_tools.py

@@ -0,0 +1,82 @@
+# tools created using Mixtral
+
+import json
+import os
+
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+client = InferenceClient(
+    "mistralai/Mixtral-8x7B-Instruct-v0.1"
+)
+
+# Helper Method
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+
+import requests
+from langchain.tools import tool
+
+history = ""
+
+class MixtralSearchTools():
+  @tool("Mixtral Normal")
+  def mixtral_normal(prompt, histroy="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Mixtral model.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Mixtral model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+  @tool("Mixtral Crazy")
+  def mixtral_crazy(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Mixtral model but has the gaurd rails removed, 
+    and responses are crazy and off the wall and sometimes scary.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Mixtral model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output

crewai/tools/search_tools.py

@@ -0,0 +1,57 @@
+import json
+import os
+
+import requests
+from langchain.tools import tool
+
+
+class SearchTools():
+  @tool("Search the internet")
+  def search_internet(query):
+    """Useful to search the internet 
+    about a a given topic and return relevant results"""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/search"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': os.environ['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    results = response.json()['organic']
+    string = []
+    for result in results[:top_result_to_return]:
+      try:
+        string.append('\n'.join([
+            f"Title: {result['title']}", f"Link: {result['link']}",
+            f"Snippet: {result['snippet']}", "\n-----------------"
+        ]))
+      except KeyError:
+        next
+
+    return '\n'.join(string)
+
+  @tool("Search news on the internet")
+  def search_news(query):
+    """Useful to search news about a company, stock or any other
+    topic and return relevant results"""""
+    top_result_to_return = 4
+    url = "https://google.serper.dev/news"
+    payload = json.dumps({"q": query})
+    headers = {
+        'X-API-KEY': os.environ['SERPER_API_KEY'],
+        'content-type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload)
+    results = response.json()['news']
+    string = []
+    for result in results[:top_result_to_return]:
+      try:
+        string.append('\n'.join([
+            f"Title: {result['title']}", f"Link: {result['link']}",
+            f"Snippet: {result['snippet']}", "\n-----------------"
+        ]))
+      except KeyError:
+        next
+
+    return '\n'.join(string)

crewai/tools/sec_tools.py

@@ -0,0 +1,108 @@
+import os
+
+import requests
+
+from langchain.tools import tool
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+
+from sec_api import QueryApi
+from unstructured.partition.html import partition_html
+
+class SECTools():
+  @tool("Search 10-Q form")
+  def search_10q(data):
+    """
+    Useful to search information from the latest 10-Q form for a
+    given stock.
+    The input to this tool should be a pipe (|) separated text of 
+    length two, representing the stock ticker you are interested, what
+    question you have from it.
+		For example, `AAPL|what was last quarter's revenue`.
+    """
+    stock, ask = data.split("|")
+    queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
+    query = {
+      "query": { 
+        "query_string": {
+          "query": f"ticker:{stock} AND formType:\"10-Q\""
+        } 
+      },
+      "from": "0",
+      "size": "1",
+      "sort": [{ "filedAt": { "order": "desc" }}]
+    }
+
+    filings = queryApi.get_filings(query)['filings']
+    link = filings[0]['linkToFilingDetails']    
+    answer = SECTools.__embedding_search(link, ask)
+    return answer
+
+  @tool("Search 10-K form")
+  def search_10k(data):
+    """
+    Useful to search information from the latest 10-K form for a
+    given stock.
+    The input to this tool should be a pipe (|) separated text of 
+    length two, representing the stock ticker you are interested, what
+    question you have from it.
+    For example, `AAPL|what was last year's revenue`.
+    """
+    stock, ask = data.split("|")
+    queryApi = QueryApi(api_key=os.environ['SEC_API_API_KEY'])
+    query = {
+      "query": { 
+        "query_string": {
+          "query": f"ticker:{stock} AND formType:\"10-K\""
+        } 
+      },
+      "from": "0",
+      "size": "1",
+      "sort": [{ "filedAt": { "order": "desc" }}]
+    }
+
+    filings = queryApi.get_filings(query)['filings']
+    link = filings[0]['linkToFilingDetails']
+    answer = SECTools.__embedding_search(link, ask)
+    return answer
+  
+  def __embedding_search(url, ask):
+    text = SECTools.__download_form_html(url)
+    elements = partition_html(text=text)
+    content = "\n".join([str(el) for el in elements])
+    text_splitter = CharacterTextSplitter(
+        separator = "\n",
+        chunk_size = 1000,
+        chunk_overlap  = 150,
+        length_function = len,
+        is_separator_regex = False,
+    )
+    docs = text_splitter.create_documents([content])
+    retriever = FAISS.from_documents(
+      docs, OpenAIEmbeddings()
+    ).as_retriever()
+    answers = retriever.get_relevant_documents(ask, top_k=4)
+    answers = "\n\n".join([a.page_content for a in answers])
+    return answers
+
+  def __download_form_html(url):    
+    headers = {
+      'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
+      'Accept-Encoding': 'gzip, deflate, br',
+      'Accept-Language': 'en-US,en;q=0.9,pt-BR;q=0.8,pt;q=0.7',
+      'Cache-Control': 'max-age=0',
+      'Dnt': '1',
+      'Sec-Ch-Ua': '"Not_A Brand";v="8", "Chromium";v="120"',
+      'Sec-Ch-Ua-Mobile': '?0',
+      'Sec-Ch-Ua-Platform': '"macOS"',
+      'Sec-Fetch-Dest': 'document',
+      'Sec-Fetch-Mode': 'navigate',
+      'Sec-Fetch-Site': 'none',
+      'Sec-Fetch-User': '?1',
+      'Upgrade-Insecure-Requests': '1',
+      'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
+    }
+
+    response = requests.get(url, headers=headers)
+    return response.text

crewai/tools/zephyr_tools.py

@@ -0,0 +1,86 @@
+# tools created using Zephyr
+
+import json
+import os
+
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+client = InferenceClient(
+    "HuggingFaceH4/zephyr-7b-beta"
+)
+
+# Helper Method
+
+def format_prompt(message, history):
+  prompt = "<s>"
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+
+import requests
+from langchain.tools import tool
+
+history = ""
+
+class ZephyrSearchTools():
+  @tool("Zephyr Normal")
+  def zephyr_normal(prompt, histroy="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Zephyr model.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Zephyr model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+  @tool("Zephyrl Crazy")
+  def zephyr_crazy(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
+    """
+    Searches for content based on the provided query using the Zephyr model but has the gaurd rails removed, 
+    and responses are crazy and off the wall and sometimes scary.
+    Args:
+        query (str): The search query.
+    Returns:
+        str: The response text from the Zephyr model or an error message.
+    """
+    generate_kwargs = {
+        "temperature": temperature,
+        "max_new_tokens": max_new_tokens,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "do_sample": True,
+        "seed": 42,
+    }
+
+    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+
+
+