tiered-memory

A tiered memory system organizes agent knowledge into hot, warm, and cold storage tiers to optimize retrieval. It uses LLM semantic reasoning to retrieve relevant information and compresses conversations into structured facts.

You can automate memory consolidation by running Python scripts that use a distillation engine to compress conversations into structured facts. It supports quick, daily, monthly, and full consolidation modes to maintain memory across multiple scoped agents.

Yes, automated agent memory management requires Python 3.8+, a Turso database for cold storage, and an LLM endpoint for semantic reasoning. These dependencies are necessary to execute the retrieval and distillation scripts.

The best way to extract structured metadata is to use a distillation engine that identifies and parses URLs, commands, and file paths from text. This enhances semantic search capabilities within the agent memory system.

You synchronize hot state memory and tree indexes using the critical sync feature, which pushes this data to cloud storage. This ensures data consistency and provides disaster recovery for multi-agent systems.

Yes, you can track memory system metrics and observability for multiple AI agents with scoped data operations. The system provides built-in tracking to analyze retrieval performance and memory completeness validation.