context-optimizer

To prevent 64k context window overflow, apply adaptive pruning and hierarchical memory archiving to automatically compress conversation history. This reduces token usage by evaluating semantic relevance and temporal priority while retaining essential context for AI agents.

Dynamic context pruning scores content relevance against current goals in real time to adjust active memory. It combines semantic, temporal, and extractive techniques to remove redundant data, moving lower-priority material into a hierarchical archive for on-demand retrieval.

You retrieve archived conversation history using embedding-based search. The hierarchical memory stores pruned material in an archive, allowing you to fetch related content on demand to enhance active context during ongoing queries.

Yes, you can monitor token usage and archive statistics in real time. Real-time monitoring tracks context health and optimization events, maintaining stable performance for chat systems operating within 64k context limits.

Context pruning preserves high-priority information by applying dynamic relevance scoring and semantic deduplication. It reduces token usage by removing low-value content while keeping recent and essential context active for AI agents.