为代理添加记忆与对话状态
为 LangChain 代理添加短期和长期记忆，让它们记住多轮对话中的上下文，并生成连贯的多步骤对话。
为代理添加记忆与对话状态 是 CoddyKit 上的免费 AI Agents with LangChain & Autonomous Workflows 课时。 这是第 4 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AI Agents with LangChain & Autonomous Workflows 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
Why Agents Need Memory
LLMs are stateless: each call knows nothing about the last unless you tell it. Without memory, an agent forgets your name the instant you say it.
The Context Window
Memory ultimately means stuffing prior info into the context window. That window is finite, so the real challenge is deciding what to keep and what to drop.
Buffer Memory
Buffer memory stores the whole conversation and replays it every turn. Accurate, but it grows without bound and eventually overflows the context window.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.save_context({'input': 'Hi, I am Lena'}, {'output': 'Hello Lena!'})Windowed Memory
Windowed memory keeps only the last N exchanges. It bounds size and forgets older context — perfect when only recent turns matter.
from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(k=4)Summary Memory
Summary memory periodically condenses older turns with the LLM, keeping the gist plus recent messages — preserving meaning in a small footprint.
from langchain.memory import ConversationSummaryMemory
memory = ConversationSummaryMemory(llm=llm)Short-Term vs Long-Term
Two flavors serve different needs: short-term memory holds the current chat in the prompt, while long-term persists facts across sessions in a store.
Long-Term Memory with Vectors
For knowledge that must survive sessions, store messages as embeddings in a vector store and retrieve the most relevant ones by similarity instead of replaying everything.
from langchain.memory import VectorStoreRetrieverMemory
memory = VectorStoreRetrieverMemory(retriever=vectorstore.as_retriever())Wiring Memory into a Chain
Wire memory into a conversation chain. Each call loads prior context, runs the model, and saves the new exchange automatically.
from langchain.chains import ConversationChain
chain = ConversationChain(llm=llm, memory=memory)
print(chain.predict(input='What is my name?'))Session and User Scoping
Real apps serve many users at once. Scope memory by session or user id so separate conversations never leak into each other.
store = {}
def get_memory(session_id):
if session_id not in store:
store[session_id] = ConversationBufferMemory()
return store[session_id]Cost and Privacy Tradeoffs
More memory means more tokens — higher cost and latency. Long-term stores may hold sensitive data, so mind retention limits and what you're allowed to keep.
Choosing a Memory Strategy
Choosing a strategy: buffer or window for short chats, summary for long ones, vector for cross-session facts — always scoped per user and mindful of cost.
Quick Check
You've met several memory types — which fits when? Time to put it to the test.
Recap
Recap: memory feeds prior context into a finite window. Buffer, window, and summary trade accuracy for size, vector stores enable long-term recall — always scope and watch cost.
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常见问题解答
「为代理添加记忆与对话状态」课时是免费的吗？
是的 — 「为代理添加记忆与对话状态」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Agents with LangChain & Autonomous Workflows 课程的其余内容，请升级到 CoddyKit PRO。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。
「为代理添加记忆与对话状态」这节课中我会学到什么？
为 LangChain 代理添加短期和长期记忆，让它们记住多轮对话中的上下文，并生成连贯的多步骤对话。 你通过在浏览器中直接运行的动手代码来练习 AI Agents with LangChain & Autonomous Workflows，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 AI Agents with LangChain & Autonomous Workflows 需要有经验吗？
无需任何先前经验。CoddyKit 上的 AI Agents with LangChain & Autonomous Workflows 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 4 节课，共 4 节。
「为代理添加记忆与对话状态」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 AI Agents with LangChain & Autonomous Workflows 课中编写并运行代码吗？
能。每节 AI Agents with LangChain & Autonomous Workflows 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 理解人工智能代理与 LLM
- LangChain 核心组件详解
- 构建您的第一个简单智能体
- 为代理添加记忆与对话状态