Memory and Conversational Context in LangChain
Learn how LangChain memory keeps track of conversation history so chains and chatbots can hold coherent multi-turn conversations.
Memory and Conversational Context in LangChain is a free LangChain / RAG / Vector DBs lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LangChain / RAG / Vector DBs learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
LLMs Are Stateless
A language model has no memory between calls. Each request is independent — it only knows what you put in the current prompt. To build a chatbot that remembers, you must feed prior turns back in yourself.
What LangChain Memory Does
LangChain memory automates this: it stores the conversation and injects relevant history into the prompt on each new turn. Your chain stays simple while the model appears to remember.
Conversation Buffer Memory
The simplest memory keeps the full transcript and prepends it to every prompt. Great for short chats, but it grows with every turn.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
memory.save_context({'input': 'Hi, I am Sam'}, {'output': 'Hello Sam!'})
print(memory.load_memory_variables({}))The Context Window Limit
Every model has a finite context window (a token budget). A growing buffer eventually overflows it, causing errors or truncation. Managing history size is the central challenge of memory.
turns = 50
tokens_per_turn = 200
total = turns * tokens_per_turn
print('history tokens:', total)Window Memory
ConversationBufferWindowMemory keeps only the last k turns. It bounds token usage at the cost of forgetting older context — a simple, effective trade-off for many chatbots.
from langchain.memory import ConversationBufferWindowMemory
memory = ConversationBufferWindowMemory(k=3)
# only the most recent 3 exchanges are keptSummary Memory
ConversationSummaryMemory uses the LLM to compress old turns into a running summary. You keep the gist of a long conversation in far fewer tokens, sacrificing exact wording for breadth.
Summary Buffer: Best of Both
ConversationSummaryBufferMemory keeps recent turns verbatim and summarizes everything older. Recent context stays precise while distant context is condensed — a popular default for production chatbots.
Wiring Memory into a Chain
You attach memory to a conversational chain. On each call, the chain loads history, builds the prompt, calls the model, and saves the new turn back to memory automatically.
from langchain.chains import ConversationChain
chain = ConversationChain(llm=llm, memory=memory)
chain.predict(input='What is my name?')Memory Variables and Prompts
Memory exposes its content as a variable (often history or chat_history) that your prompt template references. The placeholder is where the stored conversation gets injected.
template = 'Conversation so far:\n{history}\nHuman: {input}\nAI:'Persisting Memory
In-process memory vanishes when the app restarts. For real users, back memory with a store — Redis, a database, or a chat-message-history backend keyed by session id — so conversations survive across requests and servers.
Choosing a Memory Type
Match memory to need: buffer for short chats, window when you only care about recent turns, summary for long sessions on a budget, and summary-buffer for the common case. Always persist memory for multi-user apps.
Quick Check
Test your understanding of LangChain memory.
Recap
You learned how LangChain gives chatbots memory:
- LLMs are stateless; memory re-injects history each turn
- Buffer, window, summary, and summary-buffer trade detail against tokens
- Memory exposes a history variable that the prompt template uses
- Persist memory per session for multi-user, multi-server apps
Frequently asked questions
Is the “Memory and Conversational Context in LangChain” lesson free?
Yes — the full text of “Memory and Conversational Context in LangChain” is free to read here on the web, and the LangChain / RAG / Vector DBs course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LangChain / RAG / Vector DBs course, upgrade to CoddyKit PRO.
What will I learn in “Memory and Conversational Context in LangChain”?
Learn how LangChain memory keeps track of conversation history so chains and chatbots can hold coherent multi-turn conversations. You practise LangChain / RAG / Vector DBs with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start LangChain / RAG / Vector DBs?
No prior experience is required. LangChain / RAG / Vector DBs on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Memory and Conversational Context in LangChain” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this LangChain / RAG / Vector DBs lesson?
Yes. Every LangChain / RAG / Vector DBs lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Setting Up Your LangChain Environment
- Prompts, LLMs, and Basic Chains
- Output Parsers and Callbacks
- Memory and Conversational Context in LangChain