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# Agents

Interface for agents.

pydantic model langchain.agents.Agent

  Class responsible for calling the language model and deciding the action.

  This is driven by an LLMChain. The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work.

  field allowed_tools: Optional[List[str]] = None
  field llm_chain: langchain.chains.llm.LLMChain [Required]
  field output_parser: langchain.agents.agent.AgentOutputParser [Required]
  async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  abstract classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool]) → langchain.prompts.base.BasePromptTemplate

    Create a prompt for this class.

  classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, **kwargs: Any) → langchain.agents.agent.Agent

    Construct an agent from an LLM and tools.

  get_allowed_tools() → Optional[List[str]]
  get_full_inputs(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Dict[str, Any]

    Create the full inputs for the LLMChain from intermediate steps.

  plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish

    Return response when agent has been stopped due to max iterations.

  tool_run_logging_kwargs() → Dict
  abstract property llm_prefix: str

    Prefix to append the LLM call with.

  abstract property observation_prefix: str

    Prefix to append the observation with.

  property return_values: List[str]

    Return values of the agent.

pydantic model langchain.agents.AgentExecutor

  Consists of an agent using tools.

  Validators

    - `set_callback_manager` » `callback_manager`

    - `set_verbose` » `verbose`

    - `validate_return_direct_tool` » `all fields`

    - `validate_tools` » `all fields`

  field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]
  field early_stopping_method: str = 'force'
  field max_execution_time: Optional[float] = None
  field max_iterations: Optional[int] = 15
  field return_intermediate_steps: bool = False
  field tools: Sequence[BaseTool] [Required]
  classmethod from_agent_and_tools(agent: Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiActionAgent], tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor

    Create from agent and tools.

  lookup_tool(name: str) → langchain.tools.base.BaseTool

    Lookup tool by name.

  save(file_path: Union[pathlib.Path, str]) → None

    Raise error - saving not supported for Agent Executors.

  save_agent(file_path: Union[pathlib.Path, str]) → None

    Save the underlying agent.

pydantic model langchain.agents.AgentOutputParser
  abstract parse(text: str) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Parse text into agent action/finish.

class langchain.agents.AgentType(value)

  An enumeration.

  CHAT_CONVERSATIONAL_REACT_DESCRIPTION = 'chat-conversational-react-description'
  CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'chat-zero-shot-react-description'
  CONVERSATIONAL_REACT_DESCRIPTION = 'conversational-react-description'
  REACT_DOCSTORE = 'react-docstore'
  SELF_ASK_WITH_SEARCH = 'self-ask-with-search'
  ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'

pydantic model langchain.agents.BaseMultiActionAgent

  Base Agent class.

  abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[List[langchain.schema.AgentAction], langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Actions specifying what tool to use.

  dict(**kwargs: Any) → Dict

    Return dictionary representation of agent.

  get_allowed_tools() → Optional[List[str]]
  abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[List[langchain.schema.AgentAction], langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Actions specifying what tool to use.

  return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish

    Return response when agent has been stopped due to max iterations.

  save(file_path: Union[pathlib.Path, str]) → None

    Save the agent.

    Parameters

      file_path – Path to file to save the agent to.

    Example: .. code-block:: python

    > # If working with agent executor agent.agent.save(file_path=”path/agent.yaml”)

  tool_run_logging_kwargs() → Dict
  property return_values: List[str]

    Return values of the agent.

pydantic model langchain.agents.BaseSingleActionAgent

  Base Agent class.

  abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  dict(**kwargs: Any) → Dict

    Return dictionary representation of agent.

  classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent
  get_allowed_tools() → Optional[List[str]]
  abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish

    Return response when agent has been stopped due to max iterations.

  save(file_path: Union[pathlib.Path, str]) → None

    Save the agent.

    Parameters

      file_path – Path to file to save the agent to.

    Example: .. code-block:: python

    > # If working with agent executor agent.agent.save(file_path=”path/agent.yaml”)

  tool_run_logging_kwargs() → Dict
  property return_values: List[str]

    Return values of the agent.

pydantic model langchain.agents.ConversationalAgent

  An agent designed to hold a conversation in addition to using tools.

  field ai_prefix: str = 'AI'
  field output_parser: langchain.agents.agent.AgentOutputParser [Optional]
  classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response to say to the Human, 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\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate

    Create prompt in the style of the zero shot agent.

    Parameters

      - tools – List of tools the agent will have access to, used to format the prompt.

      - prefix – String to put before the list of tools.

      - suffix – String to put after the list of tools.

      - ai_prefix – String to use before AI output.

      - human_prefix – String to use before human output.

      - input_variables – List of input variables the final prompt will expect.

    Returns

      A PromptTemplate with the template assembled from the pieces here.

  classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response to say to the Human, 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\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent

    Construct an agent from an LLM and tools.

  property llm_prefix: str

    Prefix to append the llm call with.

  property observation_prefix: str

    Prefix to append the observation with.

pydantic model langchain.agents.ConversationalChatAgent

  An agent designed to hold a conversation in addition to using tools.

  field output_parser: langchain.agents.agent.AgentOutputParser [Optional]
  classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.', human_message: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, output_parser: Optional[langchain.schema.BaseOutputParser] = None) → langchain.prompts.base.BasePromptTemplate

    Create a prompt for this class.

  classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.', human_message: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent

    Construct an agent from an LLM and tools.

  property llm_prefix: str

    Prefix to append the llm call with.

  property observation_prefix: str

    Prefix to append the observation with.

pydantic model langchain.agents.LLMSingleActionAgent
  field llm_chain: langchain.chains.llm.LLMChain [Required]
  field output_parser: langchain.agents.agent.AgentOutputParser [Required]
  field stop: List[str] [Required]
  async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish]

    Given input, decided what to do.

    Parameters

      - intermediate_steps – Steps the LLM has taken to date, along with observations

      - **kwargs – User inputs.

    Returns

      Action specifying what tool to use.

  tool_run_logging_kwargs() → Dict

pydantic model langchain.agents.MRKLChain

  Chain that implements the MRKL system.

  Example

      from langchain import OpenAI, MRKLChain
      from langchain.chains.mrkl.base import ChainConfig
      llm = OpenAI(temperature=0)
      prompt = PromptTemplate(...)
      chains = [...]
      mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)

  Validators

    - `set_callback_manager` » `callback_manager`

    - `set_verbose` » `verbose`

    - `validate_return_direct_tool` » `all fields`

    - `validate_tools` » `all fields`

  field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]
  field callback_manager: BaseCallbackManager [Optional]
  field early_stopping_method: str = 'force'
  field max_execution_time: Optional[float] = None
  field max_iterations: Optional[int] = 15
  field memory: Optional[BaseMemory] = None
  field return_intermediate_steps: bool = False
  field tools: Sequence[BaseTool] [Required]
  field verbose: bool [Optional]
  classmethod from_chains(llm: langchain.schema.BaseLanguageModel, chains: List[langchain.agents.mrkl.base.ChainConfig], **kwargs: Any) → langchain.agents.agent.AgentExecutor

    User friendly way to initialize the MRKL chain.

    This is intended to be an easy way to get up and running with the MRKL chain.

    Parameters

      - llm – The LLM to use as the agent LLM.

      - chains – The chains the MRKL system has access to.

      - **kwargs – parameters to be passed to initialization.

    Returns

      An initialized MRKL chain.

    Example

        from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
        from langchain.chains.mrkl.base import ChainConfig
        llm = OpenAI(temperature=0)
        search = SerpAPIWrapper()
        llm_math_chain = LLMMathChain(llm=llm)
        chains = [
            ChainConfig(
                action_name = "Search",
                action=search.search,
                action_description="useful for searching"
            ),
            ChainConfig(
                action_name="Calculator",
                action=llm_math_chain.run,
                action_description="useful for doing math"
            )
        ]
        mrkl = MRKLChain.from_chains(llm, chains)

pydantic model langchain.agents.ReActChain

  Chain that implements the ReAct paper.

  Example

      from langchain import ReActChain, OpenAI
      react = ReAct(llm=OpenAI())

  Validators

    - `set_callback_manager` » `callback_manager`

    - `set_verbose` » `verbose`

    - `validate_return_direct_tool` » `all fields`

    - `validate_tools` » `all fields`

  field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]
  field callback_manager: BaseCallbackManager [Optional]
  field early_stopping_method: str = 'force'
  field max_execution_time: Optional[float] = None
  field max_iterations: Optional[int] = 15
  field memory: Optional[BaseMemory] = None
  field return_intermediate_steps: bool = False
  field tools: Sequence[BaseTool] [Required]
  field verbose: bool [Optional]

pydantic model langchain.agents.ReActTextWorldAgent

  Agent for the ReAct TextWorld chain.

  field output_parser: langchain.agents.agent.AgentOutputParser [Optional]
  classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool]) → langchain.prompts.base.BasePromptTemplate

    Return default prompt.

pydantic model langchain.agents.SelfAskWithSearchChain

  Chain that does self ask with search.

  Example

      from langchain import SelfAskWithSearchChain, OpenAI, GoogleSerperAPIWrapper
      search_chain = GoogleSerperAPIWrapper()
      self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)

  Validators

    - `set_callback_manager` » `callback_manager`

    - `set_verbose` » `verbose`

    - `validate_return_direct_tool` » `all fields`

    - `validate_tools` » `all fields`

  field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]
  field callback_manager: BaseCallbackManager [Optional]
  field early_stopping_method: str = 'force'
  field max_execution_time: Optional[float] = None
  field max_iterations: Optional[int] = 15
  field memory: Optional[BaseMemory] = None
  field return_intermediate_steps: bool = False
  field tools: Sequence[BaseTool] [Required]
  field verbose: bool [Optional]

pydantic model langchain.agents.Tool

  Tool that takes in function or coroutine directly.

  Validators

    - `set_callback_manager` » `callback_manager`

    - `validate_func_not_partial` » `func`

  field coroutine: Optional[Callable[[...], Awaitable[str]]] = None

    The asynchronous version of the function.

  field description: str = ''
  field func: Callable[[...], str] [Required]

    The function to run when the tool is called.

  property args: dict

pydantic model langchain.agents.ZeroShotAgent

  Agent for the MRKL chain.

  field output_parser: langchain.agents.agent.AgentOutputParser [Optional]
  classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate

    Create prompt in the style of the zero shot agent.

    Parameters

      - tools – List of tools the agent will have access to, used to format the prompt.

      - prefix – String to put before the list of tools.

      - suffix – String to put after the list of tools.

      - input_variables – List of input variables the final prompt will expect.

    Returns

      A PromptTemplate with the template assembled from the pieces here.

  classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent

    Construct an agent from an LLM and tools.

  property llm_prefix: str

    Prefix to append the llm call with.

  property observation_prefix: str

    Prefix to append the observation with.

langchain.agents.create_csv_agent(llm: langchain.llms.base.BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Create csv agent by loading to a dataframe and using pandas agent.

langchain.agents.create_json_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return a final answer by interacting with the JSON.\nYou have access to the following tools which help you learn more about the JSON you are interacting with.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nDo not make up any information that is not contained in the JSON.\nYour input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. \nYou should only use keys that you know for a fact exist. You must validate that a key exists by seeing it previously when calling `json_spec_list_keys`. \nIf you have not seen a key in one of those responses, you cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlways begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.\n\nNote that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".\nIn this case, you should ALWAYS follow up by using the `json_spec_list_keys` tool to see what keys exist at that path.\nDo not simply refer the user to the JSON or a section of the JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.\n', suffix: str = 'Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a json agent from an LLM and tools.

langchain.agents.create_openapi_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by the tools to construct your response.\n\nFirst, find the base URL needed to make the request.\n\nSecond, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.\n\nThird, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.\n\nFourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that actually exists in the spec.\n", suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base url for the API.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a json agent from an LLM and tools.

langchain.agents.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix: str = '\nThis is the result of `print(df.head())`:\n{df}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}', input_variables: Optional[List[str]] = None, verbose: bool = False, return_intermediate_steps: bool = False, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a pandas agent from an LLM and dataframe.

langchain.agents.create_pbi_agent(llm: langchain.llms.base.BaseLLM, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a Power BI Dataset.\nGiven an input question, create a syntactically correct DAX query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\n\nYou have access to tools for interacting with the Power BI Dataset. Only use the below tools. Only use the information returned by the below tools to construct your final answer. Usually I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question. If you receive an error back that mentions that the query was wrong try to phrase the question differently and get a new query from the question to query tool.\n\nIf the question does not seem related to the dataset, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', examples: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor

  Construct a pbi agent from an LLM and tools.

langchain.agents.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'Assistant is a large language model trained by OpenAI built to help users interact with a PowerBI Dataset.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics. \n\nGiven an input question, create a syntactically correct DAX query to run, then look at the results of the query and return the answer. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nUsually I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a complete sentence that answers the question. If you receive an error back that mentions that the query was wrong try to phrase the question differently and get a new query from the question to query tool.\n', suffix: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}\n", examples: Optional[str] = None, input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor

  Construct a pbi agent from an Chat LLM and tools.

  If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.

langchain.agents.create_sql_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a sql agent from an LLM and tools.

langchain.agents.create_vectorstore_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a vectorstore agent from an LLM and tools.

langchain.agents.create_vectorstore_router_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is to decide which of the tools is relevant for answering question at hand.\nFor complex questions, you can break the question down into sub questions and use tools to answers the sub questions.\n', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a vectorstore router agent from an LLM and tools.

langchain.agents.get_all_tool_names() → List[str]

  Get a list of all possible tool names.

langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.schema.BaseLanguageModel, agent: Optional[langchain.agents.agent_types.AgentType] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Load an agent executor given tools and LLM.

  Parameters

    - tools – List of tools this agent has access to.

    - llm – Language model to use as the agent.

    - agent – Agent type to use. If None and agent_path is also None, will default to AgentType.ZERO_SHOT_REACT_DESCRIPTION.

    - callback_manager – CallbackManager to use. Global callback manager is used if not provided. Defaults to None.

    - agent_path – Path to serialized agent to use.

    - agent_kwargs – Additional key word arguments to pass to the underlying agent

    - **kwargs – Additional key word arguments passed to the agent executor

  Returns

    An agent executor

langchain.agents.load_agent(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent

  Unified method for loading a agent from LangChainHub or local fs.

langchain.agents.load_tools(tool_names: List[str], llm: Optional[langchain.llms.base.BaseLLM] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → List[langchain.tools.base.BaseTool]

  Load tools based on their name.

  Parameters

    - tool_names – name of tools to load.

    - llm – Optional language model, may be needed to initialize certain tools.

    - callback_manager – Optional callback manager. If not provided, default global callback manager will be used.

  Returns

    List of tools.

langchain.agents.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True) → Callable

  Make tools out of functions, can be used with or without arguments.

  Parameters

    - *args – The arguments to the tool.

    - return_direct – Whether to return directly from the tool rather than continuing the agent loop.

    - args_schema – optional argument schema for user to specify

    - infer_schema – Whether to infer the schema of the arguments from the function’s signature. This also makes the resultant tool accept a dictionary input to its run() function.

  Requires:

    - Function must be of type (str) -> str

    - Function must have a docstring

  Examples

      @tool
      def search_api(query: str) -> str:
          # Searches the API for the query.
          return

      @tool("search", return_direct=True)
      def search_api(query: str) -> str:
          # Searches the API for the query.
          return