How do you implement a sliding window memory — keeping only the last N messages — without losing the system prompt? | Messages | langchain Interview Questions

Difficulty: 5/10

sliding window memory, LangChain prompts, session state management

Implement sliding window memory by using a custom reducer or a node that trims the message list to the last N messages, while always preserving the first system message (if present). You can also use LangChain's `trim_messages` utility inside a node.

To keep only the last N messages while preserving the system prompt, you can write a custom reducer function or a dedicated node that runs after each update. The reducer approach is more efficient because it runs during state updates. The logic is: filter the message list to keep the system message (if any) plus the last N messages from the end, ensuring the system message is never dropped even if it falls outside the window.

Sliding Window Reducer Implementation

Scenario Questions

0-2 years experience

2-5 years experience

1You added a custom SlidingWindowMemory to your LangChain app, but after a few interactions the system prompt disappears from the prompt template. Walk me through how you'd debug this issue.

How do you implement a sliding window memory — keeping only the last N messages — without losing the system prompt?

Implement sliding window memory by using a custom reducer or a node that trims the message list to the last N messages, while always preserving the first system message (if present). You can also use LangChain's trim_messages utility inside a node.

Scenario Questions

Follow-up Questions

Implement sliding window memory by using a custom reducer or a node that trims the message list to the last N messages, while always preserving the first system message (if present). You can also use LangChain's `trim_messages` utility inside a node.