How do you integrate memory with agents?

By default, AgentExecutor has no memory — each invocation is stateless. To give an agent conversation memory, pass a memory object to AgentExecutor. This is distinct from return_intermediate_steps (which stores tool call history within a single run); memory stores the dialogue across multiple separate invocations.

from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentExecutor

memory = ConversationBufferMemory(
    memory_key="chat_history",  # must match prompt variable
    return_messages=True,
)

executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True,
)

executor.invoke({"input": "My name is Alice."})
executor.invoke({"input": "What did I just tell you?"})  # recalls "Alice"

The prompt used by the agent must include a {chat_history} variable (or whatever memory_key is set to) so the history is injected on each call. For multi-user scenarios, each user needs their own memory object — or use LangGraph's checkpointing with thread IDs to manage per-conversation state.

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