diff --git a/libs/core/tests/integration_tests/test_react_agent.py b/libs/core/tests/integration_tests/test_react_agent.py
deleted file mode 100644
index b6fc9c2255ef3..0000000000000
--- a/libs/core/tests/integration_tests/test_react_agent.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import unittest
-from unittest.mock import patch
-from langchain.agents import create_react_agent, AgentExecutor
-from langchain.prompts import PromptTemplate
-from langchain.tools import Tool
-from langchain.llms.openai import OpenAI
-
-
-class TestReActAgentIntegration(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        # Define the tools
-        def search_tool(input_text):
-            return f"Searching for: {input_text}"
-
-        def calculator_tool(input_text):
-            try:
-                return str(eval(input_text))
-            except Exception:
-                return "Invalid input"
-
-        cls.tools = [
-            Tool(name="search", func=search_tool, description="Searches for information."),
-            Tool(name="calculator", func=calculator_tool, description="Performs calculations.")
-        ]
-
-        # Define the prompt template with constraints
-        constraints = """
-Note:
-1. Do not generate additional user input requests during task execution.
-2. Use only the available tools to perform actions.
-3. If a solution cannot be determined with the given tools or information, respond:
-"I'm unable to complete this task with the current resources."
-4. Avoid infinite loops by stopping reasoning and returning an appropriate message if progress cannot be made.
-"""
-
-        cls.prompt_template = PromptTemplate(
-            input_variables=["input", "tools", "tool_names", "agent_scratchpad"],
-            template=(
-                "You are a helpful assistant. "
-                "Use the following tools to answer the question: {input}.\n"
-                "Available tools: {tool_names}\n"
-                "You can use these tools: {tools}\n"
-                "Please follow this format:\n"
-                "Thought: [your thought process]\n"
-                "Action: [tool name](input_text)\n"
-                "Observation: [result of action]\n"
-                "Agent thoughts and progress: {agent_scratchpad}\n"
-            ),
-            constraints=constraints,
-        )
-
-        # Initialize the LLM
-        cls.llm = OpenAI(temperature=0)
-
-        # Create the ReAct agent
-        cls.agent = create_react_agent(
-            llm=cls.llm,
-            tools=cls.tools,
-            prompt=cls.prompt_template,
-            max_iterations=5,
-        )
-
-        # Create the AgentExecutor
-        cls.agent_executor = AgentExecutor(
-            agent=cls.agent,
-            tools=cls.tools,
-            verbose=False,
-            handle_parsing_errors=True,
-        )
-
-    @patch('langchain.llms.openai.OpenAI._call')
-    def test_agent_solves_simple_arithmetic(self, mock_call):
-        # Mock the LLM response
-        mock_call.return_value = "Thought: I need to calculate 2 + 2.\nAction: calculator(2 + 2)\nObservation: 4\nAnswer: 4"
-        input_text = "What is 2 + 2?"
-        response = self.agent_executor.run(input_text)
-        self.assertIn("4", response)
-
-    @patch('langchain.llms.openai.OpenAI._call')
-    def test_agent_handles_unresolvable_task(self, mock_call):
-        mock_call.return_value = "I'm unable to complete this task with the current resources."
-        input_text = "Translate 'hello' to French."
-        response = self.agent_executor.run(input_text)
-        self.assertIn("I'm unable to complete this task with the current resources.", response)
-
-    @patch('langchain.llms.openai.OpenAI._call')
-    def test_agent_avoids_infinite_loops(self, mock_call):
-        mock_call.side_effect = [
-            "Thought: I need more information.\nAction: None",
-            "Thought: I need more information.\nAction: None",
-            "Thought: I need more information.\nAction: None",
-            "I'm unable to complete this task with the current resources."
-        ]
-        input_text = "What is the color of the invisible car?"
-        response = self.agent_executor.run(input_text)
-        self.assertIn("I'm unable to complete this task with the current resources.", response)
-
-    @patch('langchain.llms.openai.OpenAI._call')
-    def test_agent_does_not_request_user_input(self, mock_call):
-        mock_call.return_value = "I'm unable to complete this task with the current resources."
-        input_text = "Please ask me a question."
-        response = self.agent_executor.run(input_text)
-        self.assertIn("I'm unable to complete this task with the current resources.", response)
-        self.assertNotIn("What would you like to know?", response)
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/libs/langchain/langchain/agents/react/agent.py b/libs/langchain/langchain/agents/react/agent.py
index be1455156ae99..49fad544d99c4 100644
--- a/libs/langchain/langchain/agents/react/agent.py
+++ b/libs/langchain/langchain/agents/react/agent.py
@@ -133,7 +133,7 @@ def create_react_agent(
         llm_with_stop = llm
 
     class CustomReActOutputParser(ReActSingleInputOutputParser):
-        def parse(self, text: str):
+        def parse(self, text: str) -> str:
             if "iteration limit exceeded" in text.lower():
                 return {"error": "Agent terminated due to iteration limit."}