Agents

from langchain_core.tools import StructuredTool

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.base.agents.events import ExceptionWithMessageError
from langflow.base.models.model_input_constants import (
    ALL_PROVIDER_FIELDS,
    MODEL_DYNAMIC_UPDATE_FIELDS,
    MODEL_PROVIDERS_DICT,
)
from langflow.base.models.model_utils import get_model_name
from langflow.components.helpers import CurrentDateComponent
from langflow.components.helpers.memory import MemoryComponent
from langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent
from langflow.custom.custom_component.component import _get_component_toolkit
from langflow.custom.utils import update_component_build_config
from langflow.field_typing import Tool
from langflow.io import BoolInput, DropdownInput, MultilineInput, Output
from langflow.logging import logger
from langflow.schema.dotdict import dotdict
from langflow.schema.message import Message


def set_advanced_true(component_input):
    component_input.advanced = True
    return component_input


class AgentComponent(ToolCallingAgentComponent):
    display_name: str = "Agent"
    description: str = "Define the agent's instructions, then enter a task to complete using tools."
    icon = "bot"
    beta = False
    name = "Agent"

    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]

    inputs = [
        DropdownInput(
            name="agent_llm",
            display_name="Model Provider",
            info="The provider of the language model that the agent will use to generate responses.",
            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), "Custom"],
            value="OpenAI",
            real_time_refresh=True,
            input_types=[],
        ),
        *MODEL_PROVIDERS_DICT["OpenAI"]["inputs"],
        MultilineInput(
            name="system_prompt",
            display_name="Agent Instructions",
            info="System Prompt: Initial instructions and context provided to guide the agent's behavior.",
            value="You are a helpful assistant that can use tools to answer questions and perform tasks.",
            advanced=False,
        ),
        *LCToolsAgentComponent._base_inputs,
        *memory_inputs,
        BoolInput(
            name="add_current_date_tool",
            display_name="Current Date",
            advanced=True,
            info="If true, will add a tool to the agent that returns the current date.",
            value=True,
        ),
    ]
    outputs = [Output(name="response", display_name="Response", method="message_response")]

    async def message_response(self) -> Message:
        try:
            # Get LLM model and validate
            llm_model, display_name = self.get_llm()
            if llm_model is None:
                msg = "No language model selected. Please choose a model to proceed."
                raise ValueError(msg)
            self.model_name = get_model_name(llm_model, display_name=display_name)

            # Get memory data
            self.chat_history = await self.get_memory_data()

            # Add current date tool if enabled
            if self.add_current_date_tool:
                if not isinstance(self.tools, list):  # type: ignore[has-type]
                    self.tools = []
                current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)
                if not isinstance(current_date_tool, StructuredTool):
                    msg = "CurrentDateComponent must be converted to a StructuredTool"
                    raise TypeError(msg)
                self.tools.append(current_date_tool)

            # Validate tools
            if not self.tools:
                msg = "Tools are required to run the agent. Please add at least one tool."
                raise ValueError(msg)

            # Set up and run agent
            self.set(
                llm=llm_model,
                tools=self.tools,
                chat_history=self.chat_history,
                input_value=self.input_value,
                system_prompt=self.system_prompt,
            )
            agent = self.create_agent_runnable()
            return await self.run_agent(agent)

        except (ValueError, TypeError, KeyError) as e:
            logger.error(f"{type(e).__name__}: {e!s}")
            raise
        except ExceptionWithMessageError as e:
            logger.error(f"ExceptionWithMessageError occurred: {e}")
            raise
        except Exception as e:
            logger.error(f"Unexpected error: {e!s}")
            raise

    async def get_memory_data(self):
        memory_kwargs = {
            component_input.name: getattr(self, f"{component_input.name}") for component_input in self.memory_inputs
        }
        # filter out empty values
        memory_kwargs = {k: v for k, v in memory_kwargs.items() if v}

        return await MemoryComponent(**self.get_base_args()).set(**memory_kwargs).retrieve_messages()

    def get_llm(self):
        if not isinstance(self.agent_llm, str):
            return self.agent_llm, None

        try:
            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
            if not provider_info:
                msg = f"Invalid model provider: {self.agent_llm}"
                raise ValueError(msg)

            component_class = provider_info.get("component_class")
            display_name = component_class.display_name
            inputs = provider_info.get("inputs")
            prefix = provider_info.get("prefix", "")

            return self._build_llm_model(component_class, inputs, prefix), display_name

        except Exception as e:
            logger.error(f"Error building {self.agent_llm} language model: {e!s}")
            msg = f"Failed to initialize language model: {e!s}"
            raise ValueError(msg) from e

    def _build_llm_model(self, component, inputs, prefix=""):
        model_kwargs = {input_.name: getattr(self, f"{prefix}{input_.name}") for input_ in inputs}
        return component.set(**model_kwargs).build_model()

    def set_component_params(self, component):
        provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
        if provider_info:
            inputs = provider_info.get("inputs")
            prefix = provider_info.get("prefix")
            model_kwargs = {input_.name: getattr(self, f"{prefix}{input_.name}") for input_ in inputs}

            return component.set(**model_kwargs)
        return component

    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:
        """Delete specified fields from build_config."""
        for field in fields:
            build_config.pop(field, None)

    def update_input_types(self, build_config: dotdict) -> dotdict:
        """Update input types for all fields in build_config."""
        for key, value in build_config.items():
            if isinstance(value, dict):
                if value.get("input_types") is None:
                    build_config[key]["input_types"] = []
            elif hasattr(value, "input_types") and value.input_types is None:
                value.input_types = []
        return build_config

    async def update_build_config(
        self, build_config: dotdict, field_value: str, field_name: str | None = None
    ) -> dotdict:
        # Iterate over all providers in the MODEL_PROVIDERS_DICT
        # Existing logic for updating build_config
        if field_name in ("agent_llm",):
            build_config["agent_llm"]["value"] = field_value
            provider_info = MODEL_PROVIDERS_DICT.get(field_value)
            if provider_info:
                component_class = provider_info.get("component_class")
                if component_class and hasattr(component_class, "update_build_config"):
                    # Call the component class's update_build_config method
                    build_config = await update_component_build_config(
                        component_class, build_config, field_value, "model_name"
                    )

            provider_configs: dict[str, tuple[dict, list[dict]]] = {
                provider: (
                    MODEL_PROVIDERS_DICT[provider]["fields"],
                    [
                        MODEL_PROVIDERS_DICT[other_provider]["fields"]
                        for other_provider in MODEL_PROVIDERS_DICT
                        if other_provider != provider
                    ],
                )
                for provider in MODEL_PROVIDERS_DICT
            }
            if field_value in provider_configs:
                fields_to_add, fields_to_delete = provider_configs[field_value]

                # Delete fields from other providers
                for fields in fields_to_delete:
                    self.delete_fields(build_config, fields)

                # Add provider-specific fields
                if field_value == "OpenAI" and not any(field in build_config for field in fields_to_add):
                    build_config.update(fields_to_add)
                else:
                    build_config.update(fields_to_add)
                # Reset input types for agent_llm
                build_config["agent_llm"]["input_types"] = []
            elif field_value == "Custom":
                # Delete all provider fields
                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)
                # Update with custom component
                custom_component = DropdownInput(
                    name="agent_llm",
                    display_name="Language Model",
                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), "Custom"],
                    value="Custom",
                    real_time_refresh=True,
                    input_types=["LanguageModel"],
                )
                build_config.update({"agent_llm": custom_component.to_dict()})
            # Update input types for all fields
            build_config = self.update_input_types(build_config)

            # Validate required keys
            default_keys = [
                "code",
                "_type",
                "agent_llm",
                "tools",
                "input_value",
                "add_current_date_tool",
                "system_prompt",
                "agent_description",
                "max_iterations",
                "handle_parsing_errors",
                "verbose",
            ]
            missing_keys = [key for key in default_keys if key not in build_config]
            if missing_keys:
                msg = f"Missing required keys in build_config: {missing_keys}"
                raise ValueError(msg)
        if (
            isinstance(self.agent_llm, str)
            and self.agent_llm in MODEL_PROVIDERS_DICT
            and field_name in MODEL_DYNAMIC_UPDATE_FIELDS
        ):
            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)
            if provider_info:
                component_class = provider_info.get("component_class")
                component_class = self.set_component_params(component_class)
                prefix = provider_info.get("prefix")
                if component_class and hasattr(component_class, "update_build_config"):
                    # Call each component class's update_build_config method
                    # remove the prefix from the field_name
                    if isinstance(field_name, str) and isinstance(prefix, str):
                        field_name = field_name.replace(prefix, "")
                    build_config = await update_component_build_config(
                        component_class, build_config, field_value, "model_name"
                    )
        return dotdict({k: v.to_dict() if hasattr(v, "to_dict") else v for k, v in build_config.items()})

    async def to_toolkit(self) -> list[Tool]:
        component_toolkit = _get_component_toolkit()
        tools_names = self._build_tools_names()
        agent_description = self.get_tool_description()
        # TODO: Agent Description Depreciated Feature to be removed
        description = f"{agent_description}{tools_names}"
        tools = component_toolkit(component=self).get_tools(
            tool_name=self.get_tool_name(), tool_description=description, callbacks=self.get_langchain_callbacks()
        )
        if hasattr(self, "tools_metadata"):
            tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)
        return tools

from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent

from langflow.base.agents.agent import LCAgentComponent
from langflow.field_typing import AgentExecutor
from langflow.inputs import DropdownInput, FileInput, HandleInput
from langflow.inputs.inputs import DictInput, MessageTextInput
from langflow.schema.message import Message
from langflow.template.field.base import Output


class CSVAgentComponent(LCAgentComponent):
    display_name = "CSVAgent"
    description = "Construct a CSV agent from a CSV and tools."
    documentation = "https://python.langchain.com/docs/modules/agents/toolkits/csv"
    name = "CSVAgent"
    icon = "LangChain"

    inputs = [
        *LCAgentComponent._base_inputs,
        HandleInput(
            name="llm",
            display_name="Language Model",
            input_types=["LanguageModel"],
            required=True,
            info="An LLM Model Object (It can be found in any LLM Component).",
        ),
        FileInput(
            name="path",
            display_name="File Path",
            file_types=["csv"],
            input_types=["str", "Message"],
            required=True,
            info="A CSV File or File Path.",
        ),
        DropdownInput(
            name="agent_type",
            display_name="Agent Type",
            advanced=True,
            options=["zero-shot-react-description", "openai-functions", "openai-tools"],
            value="openai-tools",
        ),
        MessageTextInput(
            name="input_value",
            display_name="Text",
            info="Text to be passed as input and extract info from the CSV File.",
            required=True,
        ),
        DictInput(
            name="pandas_kwargs",
            display_name="Pandas Kwargs",
            info="Pandas Kwargs to be passed to the agent.",
            advanced=True,
            is_list=True,
        ),
    ]

    outputs = [
        Output(display_name="Response", name="response", method="build_agent_response"),
        Output(display_name="Agent", name="agent", method="build_agent", hidden=True, tool_mode=False),
    ]

    def _path(self) -> str:
        if isinstance(self.path, Message) and isinstance(self.path.text, str):
            return self.path.text
        return self.path

    def build_agent_response(self) -> Message:
        agent_kwargs = {
            "verbose": self.verbose,
            "allow_dangerous_code": True,
        }

        agent_csv = create_csv_agent(
            llm=self.llm,
            path=self._path(),
            agent_type=self.agent_type,
            handle_parsing_errors=self.handle_parsing_errors,
            pandas_kwargs=self.pandas_kwargs,
            **agent_kwargs,
        )

        result = agent_csv.invoke({"input": self.input_value})
        return Message(text=str(result["output"]))

    def build_agent(self) -> AgentExecutor:
        agent_kwargs = {
            "verbose": self.verbose,
            "allow_dangerous_code": True,
        }

        agent_csv = create_csv_agent(
            llm=self.llm,
            path=self._path(),
            agent_type=self.agent_type,
            handle_parsing_errors=self.handle_parsing_errors,
            pandas_kwargs=self.pandas_kwargs,
            **agent_kwargs,
        )

        self.status = Message(text=str(agent_csv))

        return agent_csv

from crewai import Agent

from langflow.base.agents.crewai.crew import convert_llm, convert_tools
from langflow.custom import Component
from langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output


class CrewAIAgentComponent(Component):
    """Component for creating a CrewAI agent.

    This component allows you to create a CrewAI agent with the specified role, goal, backstory, tools,
    and language model.

    Args:
        Component (Component): Base class for all components.

    Returns:
        Agent: CrewAI agent.
    """

    display_name = "CrewAI Agent"
    description = "Represents an agent of CrewAI."
    documentation: str = "https://docs.crewai.com/how-to/LLM-Connections/"
    icon = "CrewAI"

    inputs = [
        MultilineInput(name="role", display_name="Role", info="The role of the agent."),
        MultilineInput(name="goal", display_name="Goal", info="The objective of the agent."),
        MultilineInput(name="backstory", display_name="Backstory", info="The backstory of the agent."),
        HandleInput(
            name="tools",
            display_name="Tools",
            input_types=["Tool"],
            is_list=True,
            info="Tools at agents disposal",
            value=[],
        ),
        HandleInput(
            name="llm",
            display_name="Language Model",
            info="Language model that will run the agent.",
            input_types=["LanguageModel"],
        ),
        BoolInput(
            name="memory",
            display_name="Memory",
            info="Whether the agent should have memory or not",
            advanced=True,
            value=True,
        ),
        BoolInput(
            name="verbose",
            display_name="Verbose",
            advanced=True,
            value=False,
        ),
        BoolInput(
            name="allow_delegation",
            display_name="Allow Delegation",
            info="Whether the agent is allowed to delegate tasks to other agents.",
            value=True,
        ),
        BoolInput(
            name="allow_code_execution",
            display_name="Allow Code Execution",
            info="Whether the agent is allowed to execute code.",
            value=False,
            advanced=True,
        ),
        DictInput(
            name="kwargs",
            display_name="kwargs",
            info="kwargs of agent.",
            is_list=True,
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Agent", name="output", method="build_output"),
    ]

    def build_output(self) -> Agent:
        kwargs = self.kwargs or {}

        # Define the Agent
        agent = Agent(
            role=self.role,
            goal=self.goal,
            backstory=self.backstory,
            llm=convert_llm(self.llm),
            verbose=self.verbose,
            memory=self.memory,
            tools=convert_tools(self.tools),
            allow_delegation=self.allow_delegation,
            allow_code_execution=self.allow_code_execution,
            **kwargs,
        )

        self.status = repr(agent)

        return agent

from crewai import Crew, Process

from langflow.base.agents.crewai.crew import BaseCrewComponent
from langflow.io import HandleInput


class HierarchicalCrewComponent(BaseCrewComponent):
    display_name: str = "Hierarchical Crew"
    description: str = (
        "Represents a group of agents, defining how they should collaborate and the tasks they should perform."
    )
    documentation: str = "https://docs.crewai.com/how-to/Hierarchical/"
    icon = "CrewAI"

    inputs = [
        *BaseCrewComponent._base_inputs,
        HandleInput(name="agents", display_name="Agents", input_types=["Agent"], is_list=True),
        HandleInput(name="tasks", display_name="Tasks", input_types=["HierarchicalTask"], is_list=True),
        HandleInput(name="manager_llm", display_name="Manager LLM", input_types=["LanguageModel"], required=False),
        HandleInput(name="manager_agent", display_name="Manager Agent", input_types=["Agent"], required=False),
    ]

    def build_crew(self) -> Crew:
        tasks, agents = self.get_tasks_and_agents()
        manager_llm = self.get_manager_llm()

        return Crew(
            agents=agents,
            tasks=tasks,
            process=Process.hierarchical,
            verbose=self.verbose,
            memory=self.memory,
            cache=self.use_cache,
            max_rpm=self.max_rpm,
            share_crew=self.share_crew,
            function_calling_llm=self.function_calling_llm,
            manager_agent=self.manager_agent,
            manager_llm=manager_llm,
            step_callback=self.get_step_callback(),
            task_callback=self.get_task_callback(),
        )

from pathlib import Path

import yaml
from langchain.agents import AgentExecutor
from langchain_community.agent_toolkits import create_json_agent
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
from langchain_community.tools.json.tool import JsonSpec

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import FileInput, HandleInput


class JsonAgentComponent(LCAgentComponent):
    display_name = "JsonAgent"
    description = "Construct a json agent from an LLM and tools."
    name = "JsonAgent"
    legacy: bool = True

    inputs = [
        *LCAgentComponent._base_inputs,
        HandleInput(
            name="llm",
            display_name="Language Model",
            input_types=["LanguageModel"],
            required=True,
        ),
        FileInput(
            name="path",
            display_name="File Path",
            file_types=["json", "yaml", "yml"],
            required=True,
        ),
    ]

    def build_agent(self) -> AgentExecutor:
        path = Path(self.path)
        if path.suffix in {"yaml", "yml"}:
            with path.open(encoding="utf-8") as file:
                yaml_dict = yaml.safe_load(file)
            spec = JsonSpec(dict_=yaml_dict)
        else:
            spec = JsonSpec.from_file(path)
        toolkit = JsonToolkit(spec=spec)

        return create_json_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

from langchain.agents import create_openai_tools_agent
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.inputs import MultilineInput
from langflow.inputs.inputs import DataInput, HandleInput
from langflow.schema import Data


class OpenAIToolsAgentComponent(LCToolsAgentComponent):
    display_name: str = "OpenAI Tools Agent"
    description: str = "Agent that uses tools via openai-tools."
    icon = "LangChain"
    name = "OpenAIToolsAgent"

    inputs = [
        *LCToolsAgentComponent._base_inputs,
        HandleInput(
            name="llm",
            display_name="Language Model",
            input_types=["LanguageModel", "ToolEnabledLanguageModel"],
            required=True,
        ),
        MultilineInput(
            name="system_prompt",
            display_name="System Prompt",
            info="System prompt for the agent.",
            value="You are a helpful assistant",
        ),
        MultilineInput(
            name="user_prompt", display_name="Prompt", info="This prompt must contain 'input' key.", value="{input}"
        ),
        DataInput(name="chat_history", display_name="Chat History", is_list=True, advanced=True),
    ]

    def get_chat_history_data(self) -> list[Data] | None:
        return self.chat_history

    def create_agent_runnable(self):
        if "input" not in self.user_prompt:
            msg = "Prompt must contain 'input' key."
            raise ValueError(msg)
        messages = [
            ("system", self.system_prompt),
            ("placeholder", "{chat_history}"),
            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt)),
            ("placeholder", "{agent_scratchpad}"),
        ]
        prompt = ChatPromptTemplate.from_messages(messages)
        return create_openai_tools_agent(self.llm, self.tools, prompt)

from pathlib import Path

import yaml
from langchain.agents import AgentExecutor
from langchain_community.agent_toolkits import create_openapi_agent
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
from langchain_community.tools.json.tool import JsonSpec
from langchain_community.utilities.requests import TextRequestsWrapper

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import BoolInput, FileInput, HandleInput


class OpenAPIAgentComponent(LCAgentComponent):
    display_name = "OpenAPI Agent"
    description = "Agent to interact with OpenAPI API."
    name = "OpenAPIAgent"
    icon = "LangChain"
    inputs = [
        *LCAgentComponent._base_inputs,
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        FileInput(name="path", display_name="File Path", file_types=["json", "yaml", "yml"], required=True),
        BoolInput(name="allow_dangerous_requests", display_name="Allow Dangerous Requests", value=False, required=True),
    ]

    def build_agent(self) -> AgentExecutor:
        path = Path(self.path)
        if path.suffix in {"yaml", "yml"}:
            with path.open(encoding="utf-8") as file:
                yaml_dict = yaml.safe_load(file)
            spec = JsonSpec(dict_=yaml_dict)
        else:
            spec = JsonSpec.from_file(path)
        requests_wrapper = TextRequestsWrapper()
        toolkit = OpenAPIToolkit.from_llm(
            llm=self.llm,
            json_spec=spec,
            requests_wrapper=requests_wrapper,
            allow_dangerous_requests=self.allow_dangerous_requests,
        )

        agent_args = self.get_agent_kwargs()

        # This is bit weird - generally other create_*_agent functions have max_iterations in the
        # `agent_executor_kwargs`, but openai has this parameter passed directly.
        agent_args["max_iterations"] = agent_args["agent_executor_kwargs"]["max_iterations"]
        del agent_args["agent_executor_kwargs"]["max_iterations"]
        return create_openapi_agent(llm=self.llm, toolkit=toolkit, **agent_args)

from langchain.agents import AgentExecutor
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.utilities import SQLDatabase

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import HandleInput, MessageTextInput


class SQLAgentComponent(LCAgentComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."
    name = "SQLAgent"
    icon = "LangChain"
    inputs = [
        *LCAgentComponent._base_inputs,
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        MessageTextInput(name="database_uri", display_name="Database URI", required=True),
        HandleInput(
            name="extra_tools",
            display_name="Extra Tools",
            input_types=["Tool"],
            is_list=True,
            advanced=True,
        ),
    ]

    def build_agent(self) -> AgentExecutor:
        db = SQLDatabase.from_uri(self.database_uri)
        toolkit = SQLDatabaseToolkit(db=db, llm=self.llm)
        agent_args = self.get_agent_kwargs()
        agent_args["max_iterations"] = agent_args["agent_executor_kwargs"]["max_iterations"]
        del agent_args["agent_executor_kwargs"]["max_iterations"]
        return create_sql_agent(llm=self.llm, toolkit=toolkit, extra_tools=self.extra_tools or [], **agent_args)

from crewai import Agent, Crew, Process, Task

from langflow.base.agents.crewai.crew import BaseCrewComponent
from langflow.io import HandleInput
from langflow.schema.message import Message


class SequentialCrewComponent(BaseCrewComponent):
    display_name: str = "Sequential Crew"
    description: str = "Represents a group of agents with tasks that are executed sequentially."
    documentation: str = "https://docs.crewai.com/how-to/Sequential/"
    icon = "CrewAI"

    inputs = [
        *BaseCrewComponent._base_inputs,
        HandleInput(name="tasks", display_name="Tasks", input_types=["SequentialTask"], is_list=True),
    ]

    @property
    def agents(self: "SequentialCrewComponent") -> list[Agent]:
        # Derive agents directly from linked tasks
        return [task.agent for task in self.tasks if hasattr(task, "agent")]

    def get_tasks_and_agents(self, agents_list=None) -> tuple[list[Task], list[Agent]]:
        # Use the agents property to derive agents
        if not agents_list:
            existing_agents = self.agents
            agents_list = existing_agents + (agents_list or [])

        return super().get_tasks_and_agents(agents_list=agents_list)

    def build_crew(self) -> Message:
        tasks, agents = self.get_tasks_and_agents()

        return Crew(
            agents=agents,
            tasks=tasks,
            process=Process.sequential,
            verbose=self.verbose,
            memory=self.memory,
            cache=self.use_cache,
            max_rpm=self.max_rpm,
            share_crew=self.share_crew,
            function_calling_llm=self.function_calling_llm,
            step_callback=self.get_step_callback(),
            task_callback=self.get_task_callback(),
        )

from langflow.base.agents.crewai.tasks import SequentialTask
from langflow.custom import Component
from langflow.io import BoolInput, HandleInput, MultilineInput, Output


class SequentialTaskComponent(Component):
    display_name: str = "Sequential Task"
    description: str = "Each task must have a description, an expected output and an agent responsible for execution."
    icon = "CrewAI"
    inputs = [
        MultilineInput(
            name="task_description",
            display_name="Description",
            info="Descriptive text detailing task's purpose and execution.",
        ),
        MultilineInput(
            name="expected_output",
            display_name="Expected Output",
            info="Clear definition of expected task outcome.",
        ),
        HandleInput(
            name="tools",
            display_name="Tools",
            input_types=["Tool"],
            is_list=True,
            info="List of tools/resources limited for task execution. Uses the Agent tools by default.",
            required=False,
            advanced=True,
        ),
        HandleInput(
            name="agent",
            display_name="Agent",
            input_types=["Agent"],
            info="CrewAI Agent that will perform the task",
            required=True,
        ),
        HandleInput(
            name="task",
            display_name="Task",
            input_types=["SequentialTask"],
            info="CrewAI Task that will perform the task",
        ),
        BoolInput(
            name="async_execution",
            display_name="Async Execution",
            value=True,
            advanced=True,
            info="Boolean flag indicating asynchronous task execution.",
        ),
    ]

    outputs = [
        Output(display_name="Task", name="task_output", method="build_task"),
    ]

    def build_task(self) -> list[SequentialTask]:
        tasks: list[SequentialTask] = []
        task = SequentialTask(
            description=self.task_description,
            expected_output=self.expected_output,
            tools=self.agent.tools,
            async_execution=False,
            agent=self.agent,
        )
        tasks.append(task)
        self.status = task
        if self.task:
            if isinstance(self.task, list) and all(isinstance(task, SequentialTask) for task in self.task):
                tasks = self.task + tasks
            elif isinstance(self.task, SequentialTask):
                tasks = [self.task, *tasks]
        return tasks

from langchain.agents import create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.custom.custom_component.component import _get_component_toolkit
from langflow.field_typing import Tool
from langflow.inputs import MessageTextInput
from langflow.inputs.inputs import DataInput, HandleInput
from langflow.schema import Data


class ToolCallingAgentComponent(LCToolsAgentComponent):
    display_name: str = "Tool Calling Agent"
    description: str = "An agent designed to utilize various tools seamlessly within workflows."
    icon = "LangChain"
    name = "ToolCallingAgent"

    inputs = [
        *LCToolsAgentComponent._base_inputs,
        HandleInput(
            name="llm",
            display_name="Language Model",
            input_types=["LanguageModel"],
            required=True,
            info="Language model that the agent utilizes to perform tasks effectively.",
        ),
        MessageTextInput(
            name="system_prompt",
            display_name="System Prompt",
            info="System prompt to guide the agent's behavior.",
            value="You are a helpful assistant that can use tools to answer questions and perform tasks.",
        ),
        DataInput(
            name="chat_history",
            display_name="Chat Memory",
            is_list=True,
            advanced=True,
            info="This input stores the chat history, allowing the agent to remember previous conversations.",
        ),
    ]

    def get_chat_history_data(self) -> list[Data] | None:
        return self.chat_history

    def create_agent_runnable(self):
        messages = [
            ("system", "{system_prompt}"),
            ("placeholder", "{chat_history}"),
            ("human", "{input}"),
            ("placeholder", "{agent_scratchpad}"),
        ]
        prompt = ChatPromptTemplate.from_messages(messages)
        self.validate_tool_names()
        try:
            return create_tool_calling_agent(self.llm, self.tools or [], prompt)
        except NotImplementedError as e:
            message = f"{self.display_name} does not support tool calling. Please try using a compatible model."
            raise NotImplementedError(message) from e

    async def to_toolkit(self) -> list[Tool]:
        component_toolkit = _get_component_toolkit()
        toolkit = component_toolkit(component=self)
        tools = toolkit.get_tools(callbacks=self.get_langchain_callbacks())
        if hasattr(self, "tools_metadata"):
            tools = toolkit.update_tools_metadata(tools=tools)
        return tools

from langchain_core.vectorstores import VectorStoreRetriever

from langflow.custom import CustomComponent
from langflow.field_typing import VectorStore


class VectoStoreRetrieverComponent(CustomComponent):
    display_name = "VectorStore Retriever"
    description = "A vector store retriever"
    name = "VectorStoreRetriever"
    legacy: bool = True
    icon = "LangChain"

    def build_config(self):
        return {
            "vectorstore": {"display_name": "Vector Store", "type": VectorStore},
        }

    def build(self, vectorstore: VectorStore) -> VectorStoreRetriever:
        return vectorstore.as_retriever()

from langchain.agents import AgentExecutor, create_vectorstore_router_agent
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import HandleInput


class VectorStoreRouterAgentComponent(LCAgentComponent):
    display_name = "VectorStoreRouterAgent"
    description = "Construct an agent from a Vector Store Router."
    name = "VectorStoreRouterAgent"
    legacy: bool = True

    inputs = [
        *LCAgentComponent._base_inputs,
        HandleInput(
            name="llm",
            display_name="Language Model",
            input_types=["LanguageModel"],
            required=True,
        ),
        HandleInput(
            name="vectorstores",
            display_name="Vector Stores",
            input_types=["VectorStoreInfo"],
            is_list=True,
            required=True,
        ),
    ]

    def build_agent(self) -> AgentExecutor:
        toolkit = VectorStoreRouterToolkit(vectorstores=self.vectorstores, llm=self.llm)
        return create_vectorstore_router_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

from langchain.agents import create_xml_agent
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, PromptTemplate

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.inputs import MultilineInput
from langflow.inputs.inputs import DataInput, HandleInput
from langflow.schema import Data


class XMLAgentComponent(LCToolsAgentComponent):
    display_name: str = "XML Agent"
    description: str = "Agent that uses tools formatting instructions as xml to the Language Model."
    icon = "LangChain"
    beta = True
    name = "XMLAgent"
    inputs = [
        *LCToolsAgentComponent._base_inputs,
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        DataInput(name="chat_history", display_name="Chat History", is_list=True, advanced=True),
        MultilineInput(
            name="system_prompt",
            display_name="System Prompt",
            info="System prompt for the agent.",
            value="""You are a helpful assistant. Help the user answer any questions.

You have access to the following tools:

{tools}

In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>

For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:

<tool>search</tool><tool_input>weather in SF</tool_input>

<observation>64 degrees</observation>

When you are done, respond with a final answer between <final_answer></final_answer>. For example:

<final_answer>The weather in SF is 64 degrees</final_answer>

Begin!

Question: {input}

{agent_scratchpad}
            """,  # noqa: E501
        ),
        MultilineInput(
            name="user_prompt", display_name="Prompt", info="This prompt must contain 'input' key.", value="{input}"
        ),
    ]

    def get_chat_history_data(self) -> list[Data] | None:
        return self.chat_history

    def create_agent_runnable(self):
        if "input" not in self.user_prompt:
            msg = "Prompt must contain 'input' key."
            raise ValueError(msg)
        messages = [
            ("system", self.system_prompt),
            ("placeholder", "{chat_history}"),
            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt)),
            ("ai", "{agent_scratchpad}"),
        ]
        prompt = ChatPromptTemplate.from_messages(messages)
        return create_xml_agent(self.llm, self.tools, prompt)