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Multi-Agent Memory Management System

Multi-Agent Memory Management System for LLMs Most LLM memory systems rely on recency or full context dumps, which either lose important information or hit token limits quickly. This system takes a different approach using a 3-tier memory hierarchy (hot, cold, summary) combined with semantic search and autonomous agents. It enables: ● Persistent, structured memory across sessions ● Semantic recall using vector search (not just recency) ● Automatic summarization and token optimization ● Fully local storage with SQLite + ChromaDB ● Benchmarked at ~80–96ms latency and 35–45% token reduction The system uses three agents - Storage, Retrieval, and Consolidation to manage memory efficiently and help prevent context overflow. Built autonomously by NEO : https://marketplace.visualstudio.com/... Access project on Github : https://github.com/gauravvij/Multi-Ag...