Add optional output_parser param in create_react_agent

libs/langchain/langchain/agents/react/agent.py

@@ -1,26 +1,31 @@
 from __future__ import annotations
 
-from typing import Sequence
+from typing import Optional, Sequence
 
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.prompts import BasePromptTemplate
 from langchain_core.runnables import Runnable, RunnablePassthrough
 from langchain_core.tools import BaseTool
 
+from langchain.agents import AgentOutputParser
 from langchain.agents.format_scratchpad import format_log_to_str
 from langchain.agents.output_parsers import ReActSingleInputOutputParser
 from langchain.tools.render import render_text_description
 
 
 def create_react_agent(
-    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: BasePromptTemplate,
+    output_parser: Optional[AgentOutputParser] = None,
 ) -> Runnable:
     """Create an agent that uses ReAct prompting.
 
     Args:
         llm: LLM to use as the agent.
         tools: Tools this agent has access to.
         prompt: The prompt to use. See Prompt section below for more.
+        output_parser: AgentOutputParser for parse the LLM output.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -101,12 +106,13 @@ def create_react_agent(
         tool_names=", ".join([t.name for t in tools]),
     )
     llm_with_stop = llm.bind(stop=["\nObservation"])
+    output_parser = output_parser or ReActSingleInputOutputParser()
     agent = (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
         )
         | prompt
         | llm_with_stop
-        | ReActSingleInputOutputParser()
+        | output_parser
     )
     return agent