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# Agent Toolkits

Agent toolkits.

pydantic model langchain.agents.agent_toolkits.JiraToolkit

  Jira Toolkit.

  field tools: List[langchain.tools.base.BaseTool] = []
  classmethod from_jira_api_wrapper(jira_api_wrapper: langchain.utilities.jira.JiraAPIWrapper) → langchain.agents.agent_toolkits.jira.toolkit.JiraToolkit
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.JsonToolkit

  Toolkit for interacting with a JSON spec.

  field spec: langchain.tools.json.tool.JsonSpec [Required]
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.NLAToolkit

  Natural Language API Toolkit Definition.

  field nla_tools: Sequence[langchain.agents.agent_toolkits.nla.tool.NLATool] [Required]

    List of API Endpoint Tools.

  classmethod from_llm_and_ai_plugin(llm: langchain.llms.base.BaseLLM, ai_plugin: langchain.tools.plugin.AIPlugin, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit

    Instantiate the toolkit from an OpenAPI Spec URL

  classmethod from_llm_and_ai_plugin_url(llm: langchain.llms.base.BaseLLM, ai_plugin_url: str, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit

    Instantiate the toolkit from an OpenAPI Spec URL

  classmethod from_llm_and_spec(llm: langchain.llms.base.BaseLLM, spec: langchain.tools.openapi.utils.openapi_utils.OpenAPISpec, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit

    Instantiate the toolkit by creating tools for each operation.

  classmethod from_llm_and_url(llm: langchain.llms.base.BaseLLM, open_api_url: str, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit

    Instantiate the toolkit from an OpenAPI Spec URL

  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools for all the API operations.

pydantic model langchain.agents.agent_toolkits.OpenAPIToolkit

  Toolkit for interacting with a OpenAPI api.

  field json_agent: langchain.agents.agent.AgentExecutor [Required]
  field requests_wrapper: langchain.requests.TextRequestsWrapper [Required]
  classmethod from_llm(llm: langchain.llms.base.BaseLLM, json_spec: langchain.tools.json.tool.JsonSpec, requests_wrapper: langchain.requests.TextRequestsWrapper, **kwargs: Any) → langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit

    Create json agent from llm, then initialize.

  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.PowerBIToolkit

  Toolkit for interacting with PowerBI dataset.

  field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None
  field examples: Optional[str] = None
  field llm: langchain.schema.BaseLanguageModel [Required]
  field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.SQLDatabaseToolkit

  Toolkit for interacting with SQL databases.

  field db: langchain.sql_database.SQLDatabase [Required]
  field llm: langchain.llms.base.BaseLLM [Required]
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

  property dialect: str

    Return string representation of dialect to use.

pydantic model langchain.agents.agent_toolkits.VectorStoreInfo

  Information about a vectorstore.

  field description: str [Required]
  field name: str [Required]
  field vectorstore: langchain.vectorstores.base.VectorStore [Required]

pydantic model langchain.agents.agent_toolkits.VectorStoreRouterToolkit

  Toolkit for routing between vectorstores.

  field llm: langchain.llms.base.BaseLLM [Optional]
  field vectorstores: List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo] [Required]
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.VectorStoreToolkit

  Toolkit for interacting with a vector store.

  field llm: langchain.llms.base.BaseLLM [Optional]
  field vectorstore_info: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo [Required]
  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

pydantic model langchain.agents.agent_toolkits.ZapierToolkit

  Zapier Toolkit.

  field tools: List[langchain.tools.base.BaseTool] = []
  classmethod from_zapier_nla_wrapper(zapier_nla_wrapper: langchain.utilities.zapier.ZapierNLAWrapper) → langchain.agents.agent_toolkits.zapier.toolkit.ZapierToolkit

    Create a toolkit from a ZapierNLAWrapper.

  get_tools() → List[langchain.tools.base.BaseTool]

    Get the tools in the toolkit.

langchain.agents.agent_toolkits.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.agent_toolkits.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.agent_toolkits.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.agent_toolkits.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.agent_toolkits.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.agent_toolkits.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.agent_toolkits.create_python_agent(llm: langchain.llms.base.BaseLLM, tool: langchain.tools.python.tool.PythonREPLTool, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, verbose: bool = False, prefix: str = 'You are an agent designed to write and execute python code to answer questions.\nYou have access to a python REPL, which you can use to execute python code.\nIf you get an error, debug your code and try again.\nOnly use the output of your code to answer the question. \nYou might know the answer without running any code, but you should still run the code to get the answer.\nIf it does not seem like you can write code to answer the question, just return "I don\'t know" as the answer.\n', **kwargs: Any) → langchain.agents.agent.AgentExecutor

  Construct a python agent from an LLM and tool.

langchain.agents.agent_toolkits.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.agent_toolkits.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.agent_toolkits.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.