style(core): lint (#34862)

it looks scary but i promise it is not

improving documentation consistency across core. primarily update
docstrings and comments for better formatting, readability, and
accuracy, as well as add minor clarifications and formatting
improvements to user-facing documentation.

libs/core/langchain_core/agents.py

@@ -1,9 +1,11 @@
 """Schema definitions for representing agent actions, observations, and return values.
 
 !!! warning
+
     The schema definitions are provided for backwards compatibility.
 
 !!! warning
+
     New agents should be built using the
     [`langchain` library](https://pypi.org/project/langchain/), which provides a
     simpler and more flexible way to define agents.
@@ -21,7 +23,7 @@ A basic agent works in the following manner:
     the next action.
 4. When the agent reaches a stopping condition, it returns a final return value.
 
-The schemas for the agents themselves are defined in langchain.agents.agent.
+The schemas for the agents themselves are defined in `langchain.agents.agent`.
 """
 
 from __future__ import annotations
@@ -47,9 +49,11 @@ class AgentAction(Serializable):
     """
 
     tool: str
-    """The name of the Tool to execute."""
+    """The name of the `Tool` to execute."""
+
     tool_input: str | dict
-    """The input to pass in to the Tool."""
+    """The input to pass in to the `Tool`."""
+
     log: str
     """Additional information to log about the action.
 
@@ -60,6 +64,7 @@ class AgentAction(Serializable):
     useful when `(tool, tool_input)` does not contain full information about the LLM
     prediction (for example, any `thought` before the tool/tool_input).
     """
+
     type: Literal["AgentAction"] = "AgentAction"
 
     # Override init to support instantiation by position for backward compat.
@@ -114,8 +119,8 @@ class AgentActionMessageLog(AgentAction):
     This is again useful if `(tool, tool_input)` cannot be used to fully recreate the
     LLM prediction, and you need that LLM prediction (for future agent iteration).
 
-    Compared to `log`, this is useful when the underlying LLM is a
-    chat model (and therefore returns messages rather than a string).
+    Compared to `log`, this is useful when the underlying LLM is a chat model (and
+    therefore returns messages rather than a string).
     """
     # Ignoring type because we're overriding the type from AgentAction.
     # And this is the correct thing to do in this case.
@@ -128,6 +133,7 @@ class AgentStep(Serializable):
 
     action: AgentAction
     """The `AgentAction` that was executed."""
+
     observation: Any
     """The result of the `AgentAction`."""
 
@@ -145,6 +151,7 @@ class AgentFinish(Serializable):
 
     return_values: dict
     """Dictionary of return values."""
+
     log: str
     """Additional information to log about the return value.
 
@@ -192,7 +199,7 @@ def _convert_agent_action_to_messages(
         agent_action: Agent action to convert.
 
     Returns:
-        AIMessage that corresponds to the original tool invocation.
+        `AIMessage` that corresponds to the original tool invocation.
     """
     if isinstance(agent_action, AgentActionMessageLog):
         return agent_action.message_log