langchain[patch]: add tool messages formatter for tool calling agent (#22849)

- **Description:** add tool_messages_formatter for tool calling agent,
make tool messages can be formatted in different ways for your LLM.
  - **Issue:** N/A
  - **Dependencies:** N/A

libs/langchain/langchain/agents/tool_calling_agent/base.py

@@ -1,6 +1,8 @@
-from typing import Sequence
+from typing import Callable, List, Sequence, Tuple
 
+from langchain_core.agents import AgentAction
 from langchain_core.language_models import BaseLanguageModel
+from langchain_core.messages import BaseMessage
 from langchain_core.prompts.chat import ChatPromptTemplate
 from langchain_core.runnables import Runnable, RunnablePassthrough
 from langchain_core.tools import BaseTool
@@ -10,9 +12,15 @@ from langchain.agents.format_scratchpad.tools import (
 )
 from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
 
+MessageFormatter = Callable[[Sequence[Tuple[AgentAction, str]]], List[BaseMessage]]
+
 
 def create_tool_calling_agent(
-    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: ChatPromptTemplate,
+    *,
+    message_formatter: MessageFormatter = format_to_tool_messages,
 ) -> Runnable:
     """Create an agent that uses tools.
 
@@ -21,6 +29,8 @@ def create_tool_calling_agent(
         tools: Tools this agent has access to.
         prompt: The prompt to use. See Prompt section below for more on the expected
             input variables.
+        message_formatter: Formatter function to convert (AgentAction, tool output)
+            tuples into FunctionMessages.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -89,7 +99,7 @@ def create_tool_calling_agent(
 
     agent = (
         RunnablePassthrough.assign(
-            agent_scratchpad=lambda x: format_to_tool_messages(x["intermediate_steps"])
+            agent_scratchpad=lambda x: message_formatter(x["intermediate_steps"])
         )
         | prompt
         | llm_with_tools