Compare sliding window history with rolling summary history

Both patterns exist to bound the conversation-history tokens that get fed into the model, but they bound them in different ways and pay different costs. **Sliding window** keeps the last N turns (or last K tokens) verbatim and drops everything older. It is the simplest possible policy: a FIFO queue over turns. The wins are concrete: zero LLM-call cost per turn, fully deterministic behavior, easy to reason about, easy to debug. The cost is also concrete: anything older than N turns is gone, including durable facts the user mentioned early like "I am vegetarian" or "I work in finance." The bot will silently behave as if those facts were never stated. **Rolling summary** preserves long-range information by compressing older turns into a running digest, while keeping the last few turns verbatim. In principle nothing is ever fully lost: even early facts get folded into the summary. The cost is an LLM call to refresh the summary periodically, plus the risk of summary drift, where details get smoothed away or, worse, hallucinated into the summary. In 2026, production stacks rarely pick one or the other. The pragmatic default is **rolling summary plus an extracted-memory store**. The summary handles narrative continuity, and the memory store handles durable facts the user wants preserved exactly.

Sliding window and rolling summary are the two basic shapes for bounding conversation-history tokens, and they trade off along the same axis: simplicity and determinism versus long-range information preservation. **Sliding window** is a FIFO over the last N turns or last K tokens. Its wins are operational: zero LLM-call cost per turn, deterministic behavior, dead-simple to implement and debug. Its loss is structural: anything older than the window is gone. Durable facts the user stated early ("I am vegetarian," "I work in finance," their first name) silently fall off and the bot behaves as if they were never said. Latency spikes when the budget refreshes do not exist; cost is fixed per turn. **Rolling summary** keeps older turns by compressing them into a running digest, then prepends the digest to the most recent verbatim turns. In principle, no information is lost forever, because every old turn passes through the summarizer before being dropped. In practice, prose compression loses exact numerics, rare named entities, long-distance pronoun antecedents, and verbatim artifacts like code blocks. The cost is an LLM call per refresh, plus the risk of drift across many refreshes (each refresh is a lossy encoding of an already lossy encoding). The 2026 production default is to combine both with an extracted-memory store. The **sliding window** holds the most recent K turns verbatim, the **rolling summary** captures narrative continuity for older history, and the **extracted-memory store** carries durable facts as discrete items that survive across sessions. The three layers each handle what the others cannot: the window solves short-range antecedents, the summary maintains narrative arcs, and the memory store preserves the exact specifics that prose summarization always loses.