Question

You are building a customer-support assistant on Databricks using LangChain. The assistant must handle long, multi-turn troubleshooting sessions that can exceed 30 exchanges. Early in the session, users describe their environment (OS version, product edition, error codes), and this detail must remain available for the model throughout the whole conversation. However, you are hitting the context window limit of your foundation model after about 20 turns because the full raw history is being passed each time. Which conversation memory approach best preserves the critical early context while staying within the token budget?

Accepted Answer

Use ConversationSummaryBufferMemory, which keeps recent turns verbatim and progressively summarizes older turns to retain key details within a token limit.

Answer

Use ConversationBufferMemory to always pass the complete raw message history so no detail is ever lost.

Answer

Use ConversationBufferWindowMemory with k=5 to keep only the last five exchanges and discard everything older.

Answer

Disable memory entirely and rely on the vector search retriever to fetch prior turns as needed.