langchain: docstrings agents nested (#23598)

Added missed docstrings. Formatted docstrings to the consistent form.

---------

Co-authored-by: ccurme <chester.curme@gmail.com>

libs/langchain/langchain/agents/agent_toolkits/__init__.py

@@ -13,6 +13,7 @@ whether permissions of the given toolkit are appropriate for the application.
 
 See [Security](https://python.langchain.com/docs/security) for more information.
 """
+
 from pathlib import Path
 from typing import TYPE_CHECKING, Any
 

libs/langchain/langchain/agents/chat/base.py

@@ -80,6 +80,23 @@ class ChatAgent(Agent):
         format_instructions: str = FORMAT_INSTRUCTIONS,
         input_variables: Optional[List[str]] = None,
     ) -> BasePromptTemplate:
+        """Create a prompt from a list of tools.
+
+        Args:
+            tools: A list of tools.
+            system_message_prefix: The system message prefix.
+                Default is SYSTEM_MESSAGE_PREFIX.
+            system_message_suffix: The system message suffix.
+                Default is SYSTEM_MESSAGE_SUFFIX.
+            human_message: The human message. Default is HUMAN_MESSAGE.
+            format_instructions: The format instructions.
+                Default is FORMAT_INSTRUCTIONS.
+            input_variables: The input variables. Default is None.
+
+        Returns:
+            A prompt template.
+        """
+
         tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
         tool_names = ", ".join([tool.name for tool in tools])
         format_instructions = format_instructions.format(tool_names=tool_names)
@@ -113,7 +130,26 @@ class ChatAgent(Agent):
         input_variables: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Agent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The language model.
+            tools: A list of tools.
+            callback_manager: The callback manager. Default is None.
+            output_parser: The output parser. Default is None.
+            system_message_prefix: The system message prefix.
+                Default is SYSTEM_MESSAGE_PREFIX.
+            system_message_suffix: The system message suffix.
+                Default is SYSTEM_MESSAGE_SUFFIX.
+            human_message: The human message. Default is HUMAN_MESSAGE.
+            format_instructions: The format instructions.
+                Default is FORMAT_INSTRUCTIONS.
+            input_variables: The input variables. Default is None.
+            kwargs: Additional keyword arguments.
+
+        Returns:
+            An agent.
+        """
         cls._validate_tools(tools)
         prompt = cls.create_prompt(
             tools,

libs/langchain/langchain/agents/chat/output_parser.py

@@ -25,6 +25,20 @@ class ChatOutputParser(AgentOutputParser):
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        """Parse the output from the agent into
+        an AgentAction or AgentFinish object.
+
+        Args:
+            text: The text to parse.
+
+        Returns:
+            An AgentAction or AgentFinish object.
+
+        Raises:
+            OutputParserException: If the output could not be parsed.
+            ValueError: If the action could not be found.
+        """
+
         includes_answer = FINAL_ANSWER_ACTION in text
         try:
             found = self.pattern.search(text)

libs/langchain/langchain/agents/conversational/base.py

@@ -1,4 +1,5 @@
 """An agent designed to hold a conversation in addition to using tools."""
+
 from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence
@@ -40,12 +41,20 @@ class ConversationalAgent(Agent):
 
     @property
     def observation_prefix(self) -> str:
-        """Prefix to append the observation with."""
+        """Prefix to append the observation with.
+
+        Returns:
+            "Observation: "
+        """
         return "Observation: "
 
     @property
     def llm_prefix(self) -> str:
-        """Prefix to append the llm call with."""
+        """Prefix to append the llm call with.
+
+        Returns:
+            "Thought: "
+        """
         return "Thought:"
 
     @classmethod
@@ -64,11 +73,15 @@ class ConversationalAgent(Agent):
         Args:
             tools: List of tools the agent will have access to, used to format the
                 prompt.
-            prefix: String to put before the list of tools.
+            prefix: String to put before the list of tools. Defaults to PREFIX.
-            suffix: String to put after the list of tools.
+            suffix: String to put after the list of tools. Defaults to SUFFIX.
-            ai_prefix: String to use before AI output.
+            format_instructions: Instructions on how to use the tools. Defaults to
+                FORMAT_INSTRUCTIONS
+            ai_prefix: String to use before AI output. Defaults to "AI".
             human_prefix: String to use before human output.
+                Defaults to "Human".
             input_variables: List of input variables the final prompt will expect.
+                Defaults to ["input", "chat_history", "agent_scratchpad"].
 
         Returns:
             A PromptTemplate with the template assembled from the pieces here.
@@ -105,7 +118,26 @@ class ConversationalAgent(Agent):
         input_variables: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Agent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The language model to use.
+            tools: A list of tools to use.
+            callback_manager: The callback manager to use. Default is None.
+            output_parser: The output parser to use. Default is None.
+            prefix: The prefix to use in the prompt. Default is PREFIX.
+            suffix: The suffix to use in the prompt. Default is SUFFIX.
+            format_instructions: The format instructions to use.
+                Default is FORMAT_INSTRUCTIONS.
+            ai_prefix: The prefix to use before AI output. Default is "AI".
+            human_prefix: The prefix to use before human output.
+                Default is "Human".
+            input_variables: The input variables to use. Default is None.
+            **kwargs: Any additional keyword arguments to pass to the agent.
+
+        Returns:
+            An agent.
+        """
         cls._validate_tools(tools)
         prompt = cls.create_prompt(
             tools,

libs/langchain/langchain/agents/conversational/output_parser.py

@@ -22,6 +22,16 @@ class ConvoOutputParser(AgentOutputParser):
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        """Parse the output from the agent into
+        an AgentAction or AgentFinish object.
+
+        Args:
+            text: The text to parse.
+
+        Returns:
+            An AgentAction or AgentFinish object.
+        """
+
         if f"{self.ai_prefix}:" in text:
             return AgentFinish(
                 {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text

libs/langchain/langchain/agents/conversational_chat/base.py

@@ -1,4 +1,5 @@
 """An agent designed to hold a conversation in addition to using tools."""
+
 from __future__ import annotations
 
 from typing import Any, List, Optional, Sequence, Tuple
@@ -35,7 +36,9 @@ class ConversationalChatAgent(Agent):
     """An agent designed to hold a conversation in addition to using tools."""
 
     output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
+    """Output parser for the agent."""
     template_tool_response: str = TEMPLATE_TOOL_RESPONSE
+    """Template for the tool response."""
 
     @classmethod
     def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
@@ -47,12 +50,20 @@ class ConversationalChatAgent(Agent):
 
     @property
     def observation_prefix(self) -> str:
-        """Prefix to append the observation with."""
+        """Prefix to append the observation with.
+
+        Returns:
+            "Observation: "
+        """
         return "Observation: "
 
     @property
     def llm_prefix(self) -> str:
-        """Prefix to append the llm call with."""
+        """Prefix to append the llm call with.
+
+        Returns:
+            "Thought: "
+        """
         return "Thought:"
 
     @classmethod
@@ -69,6 +80,20 @@ class ConversationalChatAgent(Agent):
         input_variables: Optional[List[str]] = None,
         output_parser: Optional[BaseOutputParser] = None,
     ) -> BasePromptTemplate:
+        """Create a prompt for the agent.
+
+        Args:
+            tools: The tools to use.
+            system_message: The system message to use.
+                Defaults to the PREFIX.
+            human_message: The human message to use.
+                Defaults to the SUFFIX.
+            input_variables: The input variables to use. Defaults to None.
+            output_parser: The output parser to use. Defaults to None.
+
+        Returns:
+            A PromptTemplate.
+        """
         tool_strings = "\n".join(
             [f"> {tool.name}: {tool.description}" for tool in tools]
         )
@@ -115,7 +140,21 @@ class ConversationalChatAgent(Agent):
         input_variables: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Agent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The language model to use.
+            tools: A list of tools to use.
+            callback_manager: The callback manager to use. Default is None.
+            output_parser: The output parser to use. Default is None.
+            system_message: The system message to use. Default is PREFIX.
+            human_message: The human message to use. Default is SUFFIX.
+            input_variables: The input variables to use. Default is None.
+            **kwargs: Any additional arguments.
+
+        Returns:
+            An agent.
+        """
         cls._validate_tools(tools)
         _output_parser = output_parser or cls._get_default_output_parser()
         prompt = cls.create_prompt(

libs/langchain/langchain/agents/format_scratchpad/__init__.py

@@ -5,6 +5,7 @@ that result from previous iterations of the agent.
 Depending on the prompting strategy you are using, you may want to format these
 differently before passing them into the LLM.
 """
+
 from langchain.agents.format_scratchpad.log import format_log_to_str
 from langchain.agents.format_scratchpad.log_to_messages import format_log_to_messages
 from langchain.agents.format_scratchpad.openai_functions import (

libs/langchain/langchain/agents/format_scratchpad/log.py

@@ -8,7 +8,18 @@ def format_log_to_str(
     observation_prefix: str = "Observation: ",
     llm_prefix: str = "Thought: ",
 ) -> str:
-    """Construct the scratchpad that lets the agent continue its thought process."""
+    """Construct the scratchpad that lets the agent continue its thought process.
+
+    Args:
+        intermediate_steps: List of tuples of AgentAction and observation strings.
+        observation_prefix: Prefix to append the observation with.
+             Defaults to "Observation: ".
+        llm_prefix: Prefix to append the llm call with.
+                Defaults to "Thought: ".
+
+    Returns:
+        str: The scratchpad.
+    """
     thoughts = ""
     for action, observation in intermediate_steps:
         thoughts += action.log

libs/langchain/langchain/agents/format_scratchpad/log_to_messages.py

@@ -8,7 +8,16 @@ def format_log_to_messages(
     intermediate_steps: List[Tuple[AgentAction, str]],
     template_tool_response: str = "{observation}",
 ) -> List[BaseMessage]:
-    """Construct the scratchpad that lets the agent continue its thought process."""
+    """Construct the scratchpad that lets the agent continue its thought process.
+
+    Args:
+        intermediate_steps: List of tuples of AgentAction and observation strings.
+        template_tool_response: Template to format the observation with.
+             Defaults to "{observation}".
+
+    Returns:
+        List[BaseMessage]: The scratchpad.
+    """
     thoughts: List[BaseMessage] = []
     for action, observation in intermediate_steps:
         thoughts.append(AIMessage(content=action.log))

libs/langchain/langchain/agents/format_scratchpad/openai_functions.py

@@ -16,7 +16,8 @@ def _convert_agent_action_to_messages(
         agent_action: Agent action to convert.
 
     Returns:
-        AIMessage that corresponds to the original tool invocation.
+        AIMessage or the previous messages plus a FunctionMessage that corresponds to
+            the original tool invocation
     """
     if isinstance(agent_action, AgentActionMessageLog):
         return list(agent_action.message_log) + [
@@ -31,10 +32,13 @@ def _create_function_message(
 ) -> FunctionMessage:
     """Convert agent action and observation into a function message.
     Args:
-        agent_action: the tool invocation request from the agent
+        agent_action: the tool invocation request from the agent.
-        observation: the result of the tool invocation
+        observation: the result of the tool invocation.
     Returns:
-        FunctionMessage that corresponds to the original tool invocation
+        FunctionMessage that corresponds to the original tool invocation.
+
+    Raises:
+        ValueError: if the observation cannot be converted to a string.
     """
     if not isinstance(observation, str):
         try:
@@ -59,7 +63,8 @@ def format_to_openai_function_messages(
 
     Returns:
         list of messages to send to the LLM for the next prediction
-
+    Raises:
+        ValueError: if the observation cannot be converted to a string.
     """
     messages = []
 

libs/langchain/langchain/agents/format_scratchpad/tools.py

@@ -15,11 +15,15 @@ def _create_tool_message(
     agent_action: ToolAgentAction, observation: str
 ) -> ToolMessage:
     """Convert agent action and observation into a tool message.
+
     Args:
-        agent_action: the tool invocation request from the agent
+        agent_action: the tool invocation request from the agent.
-        observation: the result of the tool invocation
+        observation: the result of the tool invocation.
     Returns:
-        ToolMessage that corresponds to the original tool invocation
+        ToolMessage that corresponds to the original tool invocation.
+
+    Raises:
+        ValueError: if the observation cannot be converted to a string.
     """
     if not isinstance(observation, str):
         try:
@@ -41,10 +45,10 @@ def format_to_tool_messages(
     """Convert (AgentAction, tool output) tuples into ToolMessages.
 
     Args:
-        intermediate_steps: Steps the LLM has taken to date, along with observations
+        intermediate_steps: Steps the LLM has taken to date, along with observations.
 
     Returns:
-        list of messages to send to the LLM for the next prediction
+        list of messages to send to the LLM for the next prediction.
 
     """
     messages = []

libs/langchain/langchain/agents/json_chat/base.py

@@ -36,12 +36,18 @@ def create_json_chat_agent(
             then passed into the LLM. Default is `render_text_description`.
         template_tool_response: Template prompt that uses the tool response (observation)
             to make the LLM generate the next action to take.
+            Default is TEMPLATE_TOOL_RESPONSE.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
         variables as the prompt passed in does. It returns as output either an
         AgentAction or AgentFinish.
         
+    Raises:
+        ValueError: If the prompt is missing required variables.
+        ValueError: If the template_tool_response is missing
+            the required variable 'observation'.
+
     Example:
 
         .. code-block:: python

libs/langchain/langchain/agents/mrkl/base.py

@@ -1,4 +1,5 @@
 """Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
+
 from __future__ import annotations
 
 from typing import Any, Callable, List, NamedTuple, Optional, Sequence
@@ -20,9 +21,9 @@ from langchain.tools.render import render_text_description
 
 
 class ChainConfig(NamedTuple):
-    """Configuration for chain to use in MRKL system.
+    """Configuration for a chain to use in MRKL system.
 
-    Args:
+    Parameters:
         action_name: Name of the action.
         action: Action function to call.
         action_description: Description of the action.
@@ -35,7 +36,11 @@ class ChainConfig(NamedTuple):
 
 @deprecated("0.1.0", alternative="create_react_agent", removal="0.3.0")
 class ZeroShotAgent(Agent):
-    """Agent for the MRKL chain."""
+    """Agent for the MRKL chain.
+
+    Parameters:
+        output_parser: Output parser for the agent.
+    """
 
     output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser)
 
@@ -50,12 +55,20 @@ class ZeroShotAgent(Agent):
 
     @property
     def observation_prefix(self) -> str:
-        """Prefix to append the observation with."""
+        """Prefix to append the observation with.
+
+        Returns:
+            "Observation: "
+        """
         return "Observation: "
 
     @property
     def llm_prefix(self) -> str:
-        """Prefix to append the llm call with."""
+        """Prefix to append the llm call with.
+
+        Returns:
+            "Thought: "
+        """
         return "Thought:"
 
     @classmethod
@@ -72,9 +85,12 @@ class ZeroShotAgent(Agent):
         Args:
             tools: List of tools the agent will have access to, used to format the
                 prompt.
-            prefix: String to put before the list of tools.
+            prefix: String to put before the list of tools. Defaults to PREFIX.
-            suffix: String to put after the list of tools.
+            suffix: String to put after the list of tools. Defaults to SUFFIX.
+            format_instructions: Instructions on how to use the tools.
+                Defaults to FORMAT_INSTRUCTIONS
             input_variables: List of input variables the final prompt will expect.
+                Defaults to None.
 
         Returns:
             A PromptTemplate with the template assembled from the pieces here.
@@ -100,7 +116,20 @@ class ZeroShotAgent(Agent):
         input_variables: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Agent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The LLM to use as the agent LLM.
+            tools: The tools to use.
+            callback_manager: The callback manager to use. Defaults to None.
+            output_parser: The output parser to use. Defaults to None.
+            prefix: The prefix to use. Defaults to PREFIX.
+            suffix: The suffix to use. Defaults to SUFFIX.
+            format_instructions: The format instructions to use.
+                Defaults to FORMAT_INSTRUCTIONS.
+            input_variables: The input variables to use. Defaults to None.
+            **kwargs: Additional parameters to pass to the agent.
+        """
         cls._validate_tools(tools)
         prompt = cls.create_prompt(
             tools,
@@ -141,13 +170,13 @@ class ZeroShotAgent(Agent):
 
 @deprecated("0.1.0", removal="0.3.0")
 class MRKLChain(AgentExecutor):
-    """[Deprecated] Chain that implements the MRKL system."""
+    """Chain that implements the MRKL system."""
 
     @classmethod
     def from_chains(
         cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any
     ) -> AgentExecutor:
-        """User friendly way to initialize the MRKL chain.
+        """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.

libs/langchain/langchain/agents/mrkl/output_parser.py

@@ -30,6 +30,18 @@ class MRKLOutputParser(AgentOutputParser):
         return self.format_instructions
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        """Parse the output from the agent into
+        an AgentAction or AgentFinish object.
+
+        Args:
+            text: The text to parse.
+
+        Returns:
+            An AgentAction or AgentFinish object.
+
+        Raises:
+            OutputParserException: If the output could not be parsed.
+        """
         includes_answer = FINAL_ANSWER_ACTION in text
         regex = (
             r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -33,18 +33,34 @@ if TYPE_CHECKING:
 
 
 class OpenAIAssistantFinish(AgentFinish):
-    """AgentFinish with run and thread metadata."""
+    """AgentFinish with run and thread metadata.
+
+    Parameters:
+        run_id: Run id.
+        thread_id: Thread id.
+    """
 
     run_id: str
     thread_id: str
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
+        """Check if the class is serializable by LangChain.
+
+        Returns:
+            False
+        """
         return False
 
 
 class OpenAIAssistantAction(AgentAction):
-    """AgentAction with info needed to submit custom tool output to existing run."""
+    """AgentAction with info needed to submit custom tool output to existing run.
+
+    Parameters:
+        tool_call_id: Tool call id.
+        run_id: Run id.
+        thread_id: Thread id
+    """
 
     tool_call_id: str
     run_id: str
@@ -52,6 +68,11 @@ class OpenAIAssistantAction(AgentAction):
 
     @classmethod
     def is_lc_serializable(cls) -> bool:
+        """Check if the class is serializable by LangChain.
+
+        Returns:
+            False
+        """
         return False
 
 
@@ -238,7 +259,8 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
             tools: Assistant tools. Can be passed in OpenAI format or as BaseTools.
             model: Assistant model to use.
             client: OpenAI or AzureOpenAI client.
-                Will create default OpenAI client if not specified.
+                Will create a default OpenAI client if not specified.
+            **kwargs: Additional arguments.
 
         Returns:
             OpenAIAssistantRunnable configured to run using the created assistant.
@@ -272,12 +294,12 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
                 model: Override Assistant model for this run.
                 tools: Override Assistant tools for this run.
                 run_metadata: Metadata to associate with new run.
-            config: Runnable config:
+            config: Runnable config. Defaults to None.
 
         Return:
             If self.as_agent, will return
-                Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise,
+                Union[List[OpenAIAssistantAction], OpenAIAssistantFinish].
-                will return OpenAI types
+                Otherwise, will return OpenAI types
                 Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
         """
 
@@ -351,7 +373,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
         ] = None,
         **kwargs: Any,
     ) -> OpenAIAssistantRunnable:
-        """Create an AsyncOpenAI Assistant and instantiate the Runnable.
+        """Async create an AsyncOpenAI Assistant and instantiate the Runnable.
 
         Args:
             name: Assistant name.
@@ -387,19 +409,20 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
                 run_id: Existing run to use. Should only be supplied when providing
                     the tool output for a required action after an initial invocation.
                 file_ids: File ids to include in new run. Used for retrieval.
-                message_metadata: Metadata to associate with new message.
+                message_metadata: Metadata to associate with a new message.
                 thread_metadata: Metadata to associate with new thread. Only relevant
-                    when new thread being created.
+                    when a new thread is created.
                 instructions: Additional run instructions.
                 model: Override Assistant model for this run.
                 tools: Override Assistant tools for this run.
                 run_metadata: Metadata to associate with new run.
-            config: Runnable config:
+            config: Runnable config. Defaults to None.
+            **kwargs: Additional arguments.
 
         Return:
             If self.as_agent, will return
-                Union[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise,
+                Union[List[OpenAIAssistantAction], OpenAIAssistantFinish].
-                will return OpenAI types
+                Otherwise, will return OpenAI types
                 Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
         """
 

libs/langchain/langchain/agents/openai_functions_agent/agent_token_buffer_memory.py

@@ -1,4 +1,5 @@
 """Memory used to save agent output AND intermediate steps."""
+
 from typing import Any, Dict, List
 
 from langchain_core.language_models import BaseLanguageModel
@@ -12,7 +13,22 @@ from langchain.memory.chat_memory import BaseChatMemory
 
 
 class AgentTokenBufferMemory(BaseChatMemory):
-    """Memory used to save agent output AND intermediate steps."""
+    """Memory used to save agent output AND intermediate steps.
+
+    Parameters:
+        human_prefix: Prefix for human messages. Default is "Human".
+        ai_prefix: Prefix for AI messages. Default is "AI".
+        llm: Language model.
+        memory_key: Key to save memory under. Default is "history".
+        max_token_limit: Maximum number of tokens to keep in the buffer.
+            Once the buffer exceeds this many tokens, the oldest
+            messages will be pruned. Default is 12000.
+        return_messages: Whether to return messages. Default is True.
+        output_key: Key to save output under. Default is "output".
+        intermediate_steps_key: Key to save intermediate steps under.
+            Default is "intermediate_steps".
+        format_as_tools: Whether to format as tools. Default is False.
+    """
 
     human_prefix: str = "Human"
     ai_prefix: str = "AI"
@@ -33,14 +49,21 @@ class AgentTokenBufferMemory(BaseChatMemory):
 
     @property
     def memory_variables(self) -> List[str]:
-        """Will always return list of memory variables.
+        """Always return list of memory variables.
 
         :meta private:
         """
         return [self.memory_key]
 
     def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-        """Return history buffer."""
+        """Return history buffer.
+
+        Args:
+            inputs: Inputs to the agent.
+
+        Returns:
+            A dictionary with the history buffer.
+        """
         if self.return_messages:
             final_buffer: Any = self.buffer
         else:
@@ -52,7 +75,12 @@ class AgentTokenBufferMemory(BaseChatMemory):
         return {self.memory_key: final_buffer}
 
     def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
-        """Save context from this conversation to buffer. Pruned."""
+        """Save context from this conversation to buffer. Pruned.
+
+        Args:
+            inputs: Inputs to the agent.
+            outputs: Outputs from the agent.
+        """
         input_str, output_str = self._get_input_output(inputs, outputs)
         self.chat_memory.add_user_message(input_str)
         format_to_messages = (

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -1,4 +1,5 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
+
 from typing import Any, List, Optional, Sequence, Tuple, Type, Union
 
 from langchain_core._api import deprecated
@@ -41,6 +42,9 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         prompt: The prompt for this agent, should support agent_scratchpad as one
             of the variables. For an easy way to construct this prompt, use
             `OpenAIFunctionsAgent.create_prompt(...)`
+        output_parser: The output parser for this agent. Should be an instance of
+            OpenAIFunctionsAgentOutputParser.
+            Defaults to OpenAIFunctionsAgentOutputParser.
     """
 
     llm: BaseLanguageModel
@@ -56,6 +60,17 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
 
     @root_validator(pre=False, skip_on_failure=True)
     def validate_prompt(cls, values: dict) -> dict:
+        """Validate prompt.
+
+        Args:
+            values: Values to validate.
+
+        Returns:
+            Validated values.
+
+        Raises:
+            ValueError: If `agent_scratchpad` is not in the prompt.
+        """
         prompt: BasePromptTemplate = values["prompt"]
         if "agent_scratchpad" not in prompt.input_variables:
             raise ValueError(
@@ -71,6 +86,8 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
 
     @property
     def functions(self) -> List[dict]:
+        """Get functions."""
+
         return [dict(convert_to_openai_function(t)) for t in self.tools]
 
     def plan(
@@ -83,11 +100,16 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         """Given input, decided what to do.
 
         Args:
-            intermediate_steps: Steps the LLM has taken to date, along with observations
+            intermediate_steps: Steps the LLM has taken to date,
+                along with observations.
+            callbacks: Callbacks to use. Defaults to None.
+            with_functions: Whether to use functions. Defaults to True.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
+            If the agent is finished, returns an AgentFinish.
+            If the agent is not finished, returns an AgentAction.
         """
         agent_scratchpad = format_to_openai_function_messages(intermediate_steps)
         selected_inputs = {
@@ -116,15 +138,18 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
-        """Given input, decided what to do.
+        """Async given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
-                along with observations
+                along with observations.
+            callbacks: Callbacks to use. Defaults to None.
             **kwargs: User inputs.
 
         Returns:
             Action specifying what tool to use.
+            If the agent is finished, returns an AgentFinish.
+            If the agent is not finished, returns an AgentAction.
         """
         agent_scratchpad = format_to_openai_function_messages(intermediate_steps)
         selected_inputs = {
@@ -145,7 +170,20 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         intermediate_steps: List[Tuple[AgentAction, str]],
         **kwargs: Any,
     ) -> AgentFinish:
-        """Return response when agent has been stopped due to max iterations."""
+        """Return response when agent has been stopped due to max iterations.
+
+        Args:
+            early_stopping_method: The early stopping method to use.
+            intermediate_steps: Intermediate steps.
+            **kwargs: User inputs.
+
+        Returns:
+            AgentFinish.
+
+        Raises:
+            ValueError: If `early_stopping_method` is not `force` or `generate`.
+            ValueError: If `agent_decision` is not an AgentAction.
+        """
         if early_stopping_method == "force":
             # `force` just returns a constant string
             return AgentFinish(
@@ -215,7 +253,17 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         ),
         **kwargs: Any,
     ) -> BaseSingleActionAgent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The LLM to use as the agent.
+            tools: The tools to use.
+            callback_manager: The callback manager to use. Defaults to None.
+            extra_prompt_messages: Extra prompt messages to use. Defaults to None.
+            system_message: The system message to use.
+                Defaults to a default system message.
+            **kwargs: Additional parameters to pass to the agent.
+        """
         prompt = cls.create_prompt(
             extra_prompt_messages=extra_prompt_messages,
             system_message=system_message,
@@ -246,6 +294,9 @@ def create_openai_functions_agent(
             variables as the prompt passed in does. It returns as output either an
             AgentAction or AgentFinish.
 
+    Raises:
+        ValueError: If `agent_scratchpad` is not in the prompt.
+
     Example:
 
         Creating an agent with no memory

libs/langchain/langchain/agents/openai_functions_multi_agent/base.py

@@ -1,4 +1,5 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
+
 import json
 from json import JSONDecodeError
 from typing import Any, List, Optional, Sequence, Tuple, Union
@@ -95,7 +96,7 @@ def _parse_ai_message(message: BaseMessage) -> Union[List[AgentAction], AgentFin
 
 @deprecated("0.1.0", alternative="create_openai_tools_agent", removal="0.3.0")
 class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
-    """An Agent driven by OpenAIs function powered API.
+    """Agent driven by OpenAIs function powered API.
 
     Args:
         llm: This should be an instance of ChatOpenAI, specifically a model
@@ -131,6 +132,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
 
     @property
     def functions(self) -> List[dict]:
+        """Get the functions for the agent."""
         enum_vals = [t.name for t in self.tools]
         tool_selection = {
             # OpenAI functions returns a single tool invocation
@@ -198,7 +200,9 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         """Given input, decided what to do.
 
         Args:
-            intermediate_steps: Steps the LLM has taken to date, along with observations
+            intermediate_steps: Steps the LLM has taken to date,
+                along with observations.
+            callbacks: Callbacks to use. Default is None.
             **kwargs: User inputs.
 
         Returns:
@@ -223,11 +227,12 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> Union[List[AgentAction], AgentFinish]:
-        """Given input, decided what to do.
+        """Async given input, decided what to do.
 
         Args:
             intermediate_steps: Steps the LLM has taken to date,
-                along with observations
+                along with observations.
+            callbacks: Callbacks to use. Default is None.
             **kwargs: User inputs.
 
         Returns:
@@ -260,7 +265,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
             system_message: Message to use as the system message that will be the
                 first in the prompt.
             extra_prompt_messages: Prompt messages that will be placed between the
-                system message and the new human input.
+                system message and the new human input. Default is None.
 
         Returns:
             A prompt template to pass into this agent.
@@ -293,7 +298,17 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         ),
         **kwargs: Any,
     ) -> BaseMultiActionAgent:
-        """Construct an agent from an LLM and tools."""
+        """Construct an agent from an LLM and tools.
+
+        Args:
+            llm: The language model to use.
+            tools: A list of tools to use.
+            callback_manager: The callback manager to use. Default is None.
+            extra_prompt_messages: Extra prompt messages to use. Default is None.
+            system_message: The system message to use.
+                Default is a default system message.
+            **kwargs: Additional arguments.
+        """
         prompt = cls.create_prompt(
             extra_prompt_messages=extra_prompt_messages,
             system_message=system_message,

libs/langchain/langchain/agents/openai_tools/base.py

@@ -28,6 +28,9 @@ def create_openai_tools_agent(
         variables as the prompt passed in does. It returns as output either an
         AgentAction or AgentFinish.
 
+    Raises:
+        ValueError: If the prompt is missing required variables.
+
     Example:
 
         .. code-block:: python

libs/langchain/langchain/agents/output_parsers/__init__.py

@@ -9,6 +9,7 @@ This contains a `return_values` dictionary. This usually contains a
 single `output` key, but can be extended to contain more.
 This also contains a `log` variable (which contains a log of the agent's thinking).
 """
+
 from langchain.agents.output_parsers.json import JSONAgentOutputParser
 from langchain.agents.output_parsers.openai_functions import (
     OpenAIFunctionsAgentOutputParser,

libs/langchain/langchain/agents/react/base.py

@@ -1,4 +1,5 @@
 """Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, List, Optional, Sequence

libs/langchain/langchain/agents/self_ask_with_search/base.py

@@ -1,4 +1,5 @@
 """Chain that does self-ask with search."""
+
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Sequence, Union