| from langchain.memory import ConversationBufferMemory | |
| # Create a memory instance to store the conversation context. | |
| memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| # Initialize an agent with memory integration. | |
| agent_with_memory = initialize_agent( | |
| tools=tools, | |
| llm=llm, | |
| agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, | |
| verbose=True, | |
| memory=memory | |
| ) | |
| # Example query using memory | |
| query_memory = "Now, using our previous conversation, calculate 2+2." | |
| response_memory = agent_with_memory.run(query_memory) | |
| print("Agent Response with Memory:", response_memory) |