code: # Import langchain modules
  from langchain.memory import Memory, ConversationBufferMemory
  from langchain.agents import BaseMultiActionAgent, AgentExecutor
  
  # Import other modules and classes
  from research_agent import ResearchAgent
  
  class ConversationMemory(Memory):
      def __init__(self):
          # Initialize the parent class with an empty dictionary
          super().__init__(memory={})
          # Initialize the conversation buffer memory attribute
          self.conversation_buffer_memory = ConversationBufferMemory()
  
      def set(self, key: str, value: str) -> None:
          # Store the value in the memory dictionary
          self.memory[key] = value
          # Add the value to the conversation buffer memory
          self.conversation_buffer_memory.add(value)
  
      def get(self, key: str) -> str:
          # Retrieve the value from the memory dictionary
          return self.memory.get(key)
  
  # Create a ConversationMemory instance
  conversation_memory = ConversationMemory()
  
  # Create an AgentExecutor instance with conversation_memory as a parameter
  agent_executor = AgentExecutor(
      agent=ResearchAgent(prompt_template, language_model, stop_sequence, output_parser),
      memory=conversation_memory,
      max_turns=10
  )
  
  # Run the AgentExecutor
  agent_executor.run()
  
apis: langchain.memory.ConversationBufferMemory
extract_api: [((505, 531), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {}), '()\n', (529, 531), False, 'from langchain.memory import Memory, ConversationBufferMemory\n'), ((1122, 1198), 'research_agent.ResearchAgent', 'ResearchAgent', (['prompt_template', 'language_model', 'stop_sequence', 'output_parser'], {}), '(prompt_template, language_model, stop_sequence, output_parser)\n', (1135, 1198), False, 'from research_agent import ResearchAgent\n')]