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 HandleInput, FileInput, DropdownInput


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"

    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=["csv"], required=True),
        DropdownInput(
            name="agent_type",
            display_name="Agent Type",
            advanced=True,
            options=["zero-shot-react-description", "openai-functions", "openai-tools"],
            value="openai-tools",
        ),
    ]

    def build_agent(self) -> AgentExecutor:
        return create_csv_agent(llm=self.llm, path=self.path, agent_type=self.agent_type, **self.get_agent_kwargs())

from crewai import Agent  # type: ignore

from langflow.custom import Component
from langflow.io import BoolInput, DictInput, HandleInput, MultilineInput, Output


class CrewAIAgentComponent(Component):
    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 if self.kwargs else {}
        agent = Agent(
            role=self.role,
            goal=self.goal,
            backstory=self.backstory,
            llm=self.llm,
            verbose=self.verbose,
            memory=self.memory,
            tools=self.tools if self.tools else [],
            allow_delegation=self.allow_delegation,
            allow_code_execution=self.allow_code_execution,
            **kwargs,
        )
        self.status = repr(agent)
        return agent

from crewai import Crew, Process  # type: ignore

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()
        crew = 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=self.manager_llm,
            step_callback=self.get_step_callback(),
            task_callback=self.get_task_callback(),
        )
        return crew

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 HandleInput, FileInput


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

    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:
        if self.path.endswith("yaml") or self.path.endswith("yml"):
            with open(self.path, "r") as file:
                yaml_dict = yaml.load(file, Loader=yaml.FullLoader)
            spec = JsonSpec(dict_=yaml_dict)
        else:
            spec = JsonSpec.from_file(Path(self.path))
        toolkit = JsonToolkit(spec=spec)

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

from typing import Optional, List

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

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.inputs import MultilineInput
from langflow.inputs.inputs import HandleInput, DataInput
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"
    beta = True
    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) -> Optional[List[Data]]:
        return self.chat_history

    def create_agent_runnable(self):
        if "input" not in self.user_prompt:
            raise ValueError("Prompt must contain 'input' key.")
        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.tools.json.tool import JsonSpec
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit

from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import BoolInput, HandleInput, FileInput
from langchain_community.utilities.requests import TextRequestsWrapper


class OpenAPIAgentComponent(LCAgentComponent):
    display_name = "OpenAPI Agent"
    description = "Agent to interact with OpenAPI API."
    name = "OpenAPIAgent"

    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:
        if self.path.endswith("yaml") or self.path.endswith("yml"):
            with open(self.path, "r") as file:
                yaml_dict = yaml.load(file, Loader=yaml.FullLoader)
            spec = JsonSpec(dict_=yaml_dict)
        else:
            spec = JsonSpec.from_file(Path(self.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 MessageTextInput, HandleInput


class SQLAgentComponent(LCAgentComponent):
    display_name = "SQLAgent"
    description = "Construct an SQL agent from an LLM and tools."
    name = "SQLAgent"

    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", "BaseTool"],
            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  # type: ignore

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),
    ]

    def get_tasks_and_agents(self) -> tuple[list[Task], list[Agent]]:
        return self.tasks, [task.agent for task in self.tasks]

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

from crewai import Agent, Task

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


class SequentialTaskAgentComponent(Component):
    display_name = "Sequential Task Agent"
    description = "Creates a CrewAI Task and its associated Agent."
    documentation = "https://docs.crewai.com/how-to/LLM-Connections/"
    icon = "CrewAI"

    inputs = [
        # Agent 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 agent's 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=True,
        ),
        BoolInput(
            name="allow_delegation",
            display_name="Allow Delegation",
            info="Whether the agent is allowed to delegate tasks to other agents.",
            value=False,
            advanced=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="agent_kwargs",
            display_name="Agent kwargs",
            info="Additional kwargs for the agent.",
            is_list=True,
            advanced=True,
        ),
        # Task inputs
        MultilineInput(
            name="task_description",
            display_name="Task Description",
            info="Descriptive text detailing task's purpose and execution.",
        ),
        MultilineInput(
            name="expected_output",
            display_name="Expected Task Output",
            info="Clear definition of expected task outcome.",
        ),
        BoolInput(
            name="async_execution",
            display_name="Async Execution",
            value=False,
            advanced=True,
            info="Boolean flag indicating asynchronous task execution.",
        ),
        # Chaining input
        HandleInput(
            name="previous_task",
            display_name="Previous Task",
            input_types=["SequentialTask"],
            info="The previous task in the sequence (for chaining).",
            required=False,
        ),
    ]

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

    def build_agent_and_task(self) -> list[SequentialTask]:
        # Build the agent
        agent_kwargs = self.agent_kwargs or {}
        agent = Agent(
            role=self.role,
            goal=self.goal,
            backstory=self.backstory,
            llm=self.llm,
            verbose=self.verbose,
            memory=self.memory,
            tools=self.tools if self.tools else [],
            allow_delegation=self.allow_delegation,
            allow_code_execution=self.allow_code_execution,
            **agent_kwargs,
        )

        # Build the task
        task = Task(
            description=self.task_description,
            expected_output=self.expected_output,
            agent=agent,
            async_execution=self.async_execution,
        )

        # If there's a previous task, create a list of tasks
        if self.previous_task:
            if isinstance(self.previous_task, list):
                tasks = self.previous_task + [task]
            else:
                tasks = [self.previous_task, task]
        else:
            tasks = [task]

        self.status = f"Agent: {repr(agent)}\nTask: {repr(task)}"
        return tasks

from typing import Optional, List

from langchain.agents import create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate
from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.inputs import MultilineInput
from langflow.inputs.inputs import HandleInput, DataInput
from langflow.schema import Data


class ToolCallingAgentComponent(LCToolsAgentComponent):
    display_name: str = "Tool Calling Agent"
    description: str = "Agent that uses tools"
    icon = "LangChain"
    beta = True
    name = "ToolCallingAgent"

    inputs = LCToolsAgentComponent._base_inputs + [
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], 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) -> Optional[List[Data]]:
        return self.chat_history

    def create_agent_runnable(self):
        if "input" not in self.user_prompt:
            raise ValueError("Prompt must contain 'input' key.")
        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_tool_calling_agent(self.llm, self.tools, prompt)

from langchain.agents import AgentExecutor, create_vectorstore_agent
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
from langflow.base.agents.agent import LCAgentComponent
from langflow.inputs import HandleInput


class VectorStoreAgentComponent(LCAgentComponent):
    display_name = "VectorStoreAgent"
    description = "Construct an agent from a Vector Store."
    name = "VectorStoreAgent"

    inputs = LCAgentComponent._base_inputs + [
        HandleInput(name="llm", display_name="Language Model", input_types=["LanguageModel"], required=True),
        HandleInput(name="vectorstore", display_name="Vector Store", input_types=["VectorStoreInfo"], required=True),
    ]

    def build_agent(self) -> AgentExecutor:
        toolkit = VectorStoreToolkit(vectorstore_info=self.vectorstore, llm=self.llm)
        return create_vectorstore_agent(llm=self.llm, toolkit=toolkit, **self.get_agent_kwargs())

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

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


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

    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, PromptTemplate, HumanMessagePromptTemplate

from langflow.base.agents.agent import LCToolsAgentComponent
from langflow.inputs import MultilineInput
from langflow.inputs.inputs import HandleInput


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),
        MultilineInput(
            name="user_prompt",
            display_name="Prompt",
            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}
            """,
        ),
    ]

    def create_agent_runnable(self):
        messages = [
            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=["input"], template=self.user_prompt))
        ]
        prompt = ChatPromptTemplate.from_messages(messages)
        return create_xml_agent(self.llm, self.tools, prompt)