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Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Agent memory should be a managed lifecycle, not just a database, to cut cost and forgetting.
92.0%
LongMemEval overall accuracy
Uses the smaller gpt-5-mini as both answer model and judge.
How it works
Agents drown in history
Re-sending the full chat every turn makes token cost grow quadratically. Crude summaries drop key facts and hurt accuracy.
Five-part context lifecycle
Design memory per agent, extract structure, scope retrieval by user and org, prefetch ahead, and compact with validation.
High memory benchmark scores
Synap scores 92.0% on LongMemEval and 93.2% on LoCoMo. Multi-session reasoning stays weakest at 75.2%.
From the paper