# 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()

langchain.memory.ConversationBufferMemory
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