Conversation Memory

Raw chat logs grow unbounded. Conversation memory balances fidelity (don't lose important details) with efficiency (don't blow the token budget).

Conversation memory is meeting minutes — not a verbatim transcript, but enough to continue where you left off.

Scenario

After 30 turns of debugging, conversation memory summarizes turns 1–25 into 3 paragraphs and keeps turns 26–30 verbatim.

Solution

In Agent Memory, apply Conversation Memory to this scenario: After 30 turns of debugging, conversation memory summarizes turns 1–25 into 3 paragraphs and keeps turns 26–30 verbatim. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Conversation Memory (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Conversation Memory happens in the code.

1from langchain.memory import ConversationSummaryBufferMemory  # import dependencies2from langchain_openai import ChatOpenAI  # import dependencies3
4memory = ConversationSummaryBufferMemory(  # key line for Conversation Memory5    llm=ChatOpenAI(model="gpt-4o-mini"),6    max_token_limit=2000,7    return_messages=True,8)9memory.save_context({"input": "I'm building a RAG app"}, {"output": "Great! What vector DB?"})  # key line for Conversation Memory

• Treating Conversation Memory as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for conversation memory

Quick recap — the most important points from this module.