Your agent is hitting the context window limit after 20 turns — what is your strategy to manage message history without losing critical context?

The optimal strategy is to use a sliding window that keeps the most recent 8-10 turns (the most relevant context) while summarizing earlier messages into a concise SystemMessage. This preserves high-level context without exceeding token limits. LangGraph provides built-in support for both approaches. You can implement a conditional node that triggers summarization when the message count exceeds a threshold, replacing messages older than the window with a summary generated by a cheap model (e.g., GPT-4o-mini). This ensures that the agent retains memory of the conversation's key points without keeping every raw message.