Add `type` to Agent actions (#11682)

Add `type` to agent actions.

libs/langchain/langchain/schema/agent.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Sequence, Union
+from typing import Any, Literal, Sequence, Union
 
 from langchain.load.serializable import Serializable
 from langchain.schema.messages import BaseMessage
@@ -21,10 +21,12 @@ class AgentAction(Serializable):
     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)."""
+    type: Literal["AgentAction"] = "AgentAction"
 
     def __init__(
         self, tool: str, tool_input: Union[str, dict], log: str, **kwargs: Any
     ):
+        """Override init to support instantiation by position for backward compat."""
         super().__init__(tool=tool, tool_input=tool_input, log=log, **kwargs)
 
     @classmethod
@@ -42,6 +44,10 @@ class AgentActionMessageLog(AgentAction):
     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)."""
+    # Ignoring type because we're overriding the type from AgentAction.
+    # And this is the correct thing to do in this case.
+    # The type literal is used for serialization purposes.
+    type: Literal["AgentActionMessageLog"] = "AgentActionMessageLog"  # type: ignore
 
 
 class AgentFinish(Serializable):
@@ -56,8 +62,10 @@ class AgentFinish(Serializable):
     `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).
     """
+    type: Literal["AgentFinish"] = "AgentFinish"
 
     def __init__(self, return_values: dict, log: str, **kwargs: Any):
+        """Override init to support instantiation by position for backward compat."""
         super().__init__(return_values=return_values, log=log, **kwargs)
 
     @classmethod

libs/langchain/tests/unit_tests/test_schema.py

@@ -6,7 +6,8 @@ from typing import Union
 from langchain.prompts.base import StringPromptValue
 from langchain.prompts.chat import ChatPromptValueConcrete
 from langchain.pydantic_v1 import BaseModel
-from langchain.schema import Document
+from langchain.schema import AgentAction, AgentFinish, Document
+from langchain.schema.agent import AgentActionMessageLog
 from langchain.schema.messages import (
     AIMessage,
     AIMessageChunk,
@@ -104,6 +105,9 @@ def test_serialization_of_wellknown_objects() -> None:
             AIMessageChunk,
             StringPromptValue,
             ChatPromptValueConcrete,
+            AgentFinish,
+            AgentAction,
+            AgentActionMessageLog,
         ]
 
     lc_objects = [
@@ -132,6 +136,14 @@ def test_serialization_of_wellknown_objects() -> None:
         StringPromptValue(text="hello"),
         ChatPromptValueConcrete(messages=[HumanMessage(content="human")]),
         Document(page_content="hello"),
+        AgentFinish(return_values={}, log=""),
+        AgentAction(tool="tool", tool_input="input", log=""),
+        AgentActionMessageLog(
+            tool="tool",
+            tool_input="input",
+            log="",
+            message_log=[HumanMessage(content="human")],
+        ),
     ]
 
     for lc_object in lc_objects: