effect-uai-auto-compaction

To compress conversation history within token budgets, you can use an auto-compaction step that summarizes older turns while preserving the most recent utterances verbatim for continuity. This state-driven workflow triggers automatically when configured thresholds are crossed.

Auto-compaction for multi-turn chats is a process that summarizes older conversation history into compact representations when turn or token budgets are exceeded. It preserves the most recent items verbatim, ensuring the AI agent maintains contextual coherence without losing critical recent state.

You should implement history summarization in AI agent state management when long-running sessions or multi-turn chats exceed context limits. It replaces older turns with compact summaries, retaining recent utterances to maintain continuity while staying within token budgets.

Yes, conversation history compaction keeps the most recent messages intact. While older turns are summarized and replaced with a compact representation to save tokens, the last items are preserved verbatim to maintain conversational coherence and state continuity.

The best way to manage context limits in long-running agent sessions is integrating an automatic compaction step into the streamTurn workflow. This summarizes older history when token or turn thresholds are exceeded, replacing it with a compact representation while keeping recent items verbatim.

Multi-turn chats lose earlier context after hitting token limits if auto-compaction is not configured. Implementing a compaction step summarizes older history into compact items and updates the state, preserving recent utterances while preventing context loss from hard truncation.