src/backend/base/langflow/components/langchain_utilities/xml.py

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)