Multi-Agent Memory Architecture: Targeted Retrieval vs. Unified Store
Fun-Following-1723 · reddit · 2026-09-01
The author shares their experience building a V1 memory architecture for a multi-agent system (supervisor/sub-agents). The core pipeline involves: logging raw conversations, batch-extracting episodic memories, promoting key facts to semantic memory, and maintaining procedural memory as a structured skill registry.
For retrieval, the author tests an intent-based routing strategy (User Query → Scope/ACL → Intent/Task Router → Targeted Store Retrieval), directing queries to specific stores based on intent (e.g., procedures, history, preferences) rather than searching all stores universally.
Initial observations include:
- Storing raw events in Mem0 added noticeable latency and cost.
- Exact matching was cleaner than vector retrieval for workflows.
- Batch consolidation yielded cleaner facts than turn-by-turn extraction.
The author seeks feedback on: the reliability of intent routing, the necessity of separate storage backends, the scalability of treating procedural memory as code, and potential failure cases.
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