make agent action serializable (#10797)

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

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

@@ -1,6 +1,5 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
 import json
-from dataclasses import dataclass
 from json import JSONDecodeError
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
@@ -21,6 +20,7 @@ from langchain.schema import (
     BasePromptTemplate,
     OutputParserException,
 )
+from langchain.schema.agent import AgentActionMessageLog
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import (
     AIMessage,
@@ -31,10 +31,8 @@ from langchain.schema.messages import (
 from langchain.tools import BaseTool
 from langchain.tools.convert_to_openai import format_tool_to_openai_function
 
-
-@dataclass
-class _FunctionsAgentAction(AgentAction):
-    message_log: List[BaseMessage]
+# For backwards compatibility
+_FunctionsAgentAction = AgentActionMessageLog
 
 
 def _convert_agent_action_to_messages(
@@ -51,7 +49,7 @@ def _convert_agent_action_to_messages(
         AIMessage that corresponds to the original tool invocation.
     """
     if isinstance(agent_action, _FunctionsAgentAction):
-        return agent_action.message_log + [
+        return list(agent_action.message_log) + [
             _create_function_message(agent_action, observation)
         ]
     else:

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

@@ -1,6 +1,5 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
 import json
-from dataclasses import dataclass
 from json import JSONDecodeError
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
@@ -21,6 +20,7 @@ from langchain.schema import (
     BasePromptTemplate,
     OutputParserException,
 )
+from langchain.schema.agent import AgentActionMessageLog
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import (
     AIMessage,
@@ -30,10 +30,8 @@ from langchain.schema.messages import (
 )
 from langchain.tools import BaseTool
 
-
-@dataclass
-class _FunctionsAgentAction(AgentAction):
-    message_log: List[BaseMessage]
+# For backwards compatibility
+_FunctionsAgentAction = AgentActionMessageLog
 
 
 def _convert_agent_action_to_messages(
@@ -50,7 +48,7 @@ def _convert_agent_action_to_messages(
         AIMessage that corresponds to the original tool invocation.
     """
     if isinstance(agent_action, _FunctionsAgentAction):
-        return agent_action.message_log + [
+        return list(agent_action.message_log) + [
             _create_function_message(agent_action, observation)
         ]
     else:

libs/langchain/langchain/schema/agent.py

@@ -1,11 +1,12 @@
 from __future__ import annotations
 
-from dataclasses import dataclass
-from typing import NamedTuple, Union
+from typing import Any, Sequence, Union
 
+from langchain.load.serializable import Serializable
+from langchain.schema.messages import BaseMessage
 
-@dataclass
-class AgentAction:
+
+class AgentAction(Serializable):
     """A full description of an action for an ActionAgent to execute."""
 
     tool: str
@@ -13,13 +14,57 @@ class AgentAction:
     tool_input: Union[str, dict]
     """The input to pass in to the Tool."""
     log: str
-    """Additional information to log about the action."""
+    """Additional information to log about the action.
+    This log can be used in a few ways. First, it can be used to audit
+    what exactly the LLM predicted to lead to this (tool, tool_input).
+    Second, it can be used in future iterations to show the LLMs prior
+    thoughts. This is useful when (tool, tool_input) does not contain
+    full information about the LLM prediction (for example, any `thought`
+    before the tool/tool_input)."""
+
+    def __init__(
+        self, tool: str, tool_input: Union[str, dict], log: str, **kwargs: Any
+    ):
+        super().__init__(tool=tool, tool_input=tool_input, log=log, **kwargs)
+
+    @property
+    def lc_serializable(self) -> bool:
+        """
+        Return whether or not the class is serializable.
+        """
+        return True
+
 
+class AgentActionMessageLog(AgentAction):
+    message_log: Sequence[BaseMessage]
+    """Similar to log, this can be used to pass along extra
+    information about what exact messages were predicted by the LLM
+    before parsing out the (tool, tool_input). 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
+    ChatModel (and therefore returns messages rather than a string)."""
 
-class AgentFinish(NamedTuple):
+
+class AgentFinish(Serializable):
     """The final return value of an ActionAgent."""
 
     return_values: dict
     """Dictionary of return values."""
     log: str
-    """Additional information to log about the return value"""
+    """Additional information to log about the return value.
+    This is used to pass along the full LLM prediction, not just the parsed out
+    return value. For example, if the full LLM prediction was
+    `Final Answer: 2` you may want to just return `2` as a return value, but pass
+    along the full string as a `log` (for debugging or observability purposes).
+    """
+
+    def __init__(self, return_values: dict, log: str, **kwargs: Any):
+        super().__init__(return_values=return_values, log=log, **kwargs)
+
+    @property
+    def lc_serializable(self) -> bool:
+        """
+        Return whether or not the class is serializable.
+        """
+        return True