XMLAgent#

class langchain.agents.xml.base.XMLAgent[source]#

Bases: BaseSingleActionAgent

Deprecated since version 0.1.0: Use create_xml_agent() instead. It will not be removed until langchain==1.0.

Agent that uses XML tags.

Parameters:

tools – list of tools the agent can choose from
llm_chain – The LLMChain to call to predict the next action

Examples

from langchain.agents import XMLAgent
from langchain

tools = ...
model =

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param llm_chain: LLMChain [Required]#: Chain to use to predict action.

param tools: list[BaseTool] [Required]#: List of tools this agent has access to.

classmethod from_llm_and_tools(

llm: BaseLanguageModel,

tools: Sequence[BaseTool],

callback_manager: BaseCallbackManager | None = None,

**kwargs: Any,

) → BaseSingleActionAgent#

Construct an agent from an LLM and tools.

Parameters:

llm (BaseLanguageModel) – Language model to use.
tools (Sequence[BaseTool]) – Tools to use.
callback_manager (BaseCallbackManager | None) – Callback manager to use.
kwargs (Any) – Additional arguments.

Returns:

Agent object.

Return type:

BaseSingleActionAgent

static get_default_output_parser() → XMLAgentOutputParser[source]#

Return type:: XMLAgentOutputParser

static get_default_prompt() → ChatPromptTemplate[source]#

Return type:: ChatPromptTemplate

async aplan(

intermediate_steps: list[tuple[AgentAction, str]],

callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,

**kwargs: Any,

) → AgentAction | AgentFinish[source]#

Async given input, decided what to do.

Parameters:

intermediate_steps (list[tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to run.
**kwargs (Any) – User inputs.

Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

get_allowed_tools() → list[str] | None#

Return type:: list[str] | None

plan(

intermediate_steps: list[tuple[AgentAction, str]],

callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,

**kwargs: Any,

) → AgentAction | AgentFinish[source]#

Given input, decided what to do.

Parameters:

intermediate_steps (list[tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to run.
**kwargs (Any) – User inputs.

Returns:

Action specifying what tool to use.

Return type:

AgentAction | AgentFinish

return_stopped_response(

early_stopping_method: str,

intermediate_steps: list[tuple[AgentAction, str]],

**kwargs: Any,

) → AgentFinish#

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

Parameters:

early_stopping_method (str) – Method to use for early stopping.
intermediate_steps (list[tuple[AgentAction, str]]) – Steps the LLM has taken to date, along with observations.
**kwargs (Any) – User inputs.

Returns:

Agent finish object.

Return type:

AgentFinish

Raises:

ValueError – If early_stopping_method is not supported.

save(file_path: Path | str) → None#

Save the agent.

Parameters:: file_path (Path | str) – Path to file to save the agent to.
Return type:: None

Example: .. code-block:: python

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

tool_run_logging_kwargs() → dict#

Return logging kwargs for tool run.

Return type:: dict

property return_values: list[str]#: Return values of the agent.