cognitive-memory

A multi-agent memory system with audit trails encodes information across episodic, semantic, procedural, and vault stores, applying decay-based relevance scoring and logging mutations to track recall history. You initialize core memory, configure trigger-driven routing, and enable sleep-time reflection to consolidate context.

Sleep-time reflection in a multi-agent memory architecture is a consolidation process that reviews and reinforces stored information during inactive periods. It improves long-term understanding and accountability by updating knowledge graphs and decay scores without interrupting active agent operations.

Trigger-driven memory routing for multi-agent collaboration is set up by adding agent instructions to AGENTS.md and configuring memory triggers. This routes information to the appropriate memory stores, enabling agents to access consolidated knowledge and audit-ready history during collaborative tasks.

Yes, the memory system supports graph-based knowledge representation for governance and compliance. It maintains audit-ready history by tracking mutations across memory stores, enabling teams to review recall paths and ensure accountability in multi-agent environments.

Yes, you need Python3, Git, and Bash to use this cognitive memory architecture. These dependencies support the scripts and assets required to initialize memory stores, configure decay scoring, and maintain the event log and audit mechanism for tracking mutations.

The best way to prevent memory bloat in long-running multi-agent systems is applying decay-based relevance scoring alongside sleep-time reflection. This architecture automatically consolidates necessary information and reduces the relevance of outdated data across episodic, semantic, and procedural stores while preserving an audit trail.