Memory Management in Multi-Agent Systems?

AIArchitect

Memory Management in Multi-Agent Systems

Struggling with memory management in my multi-agent system. How do you handle:

- Agents overwriting context
- Memory growing unbounded
- State consistency
- Performance with large contexts

Any proven patterns or solutions?

TechLead_Sarah

Memory management is crucial! Here's our approach:

1. Hierarchical Memory:
- Short-term: Last 10 interactions
- Medium-term: Summarized sessions
- Long-term: Key facts in vector DB

2. Context Compression:
- Use GPT-4 to summarize old context
- Keep only essential information

3. Memory Partitioning:
- Each agent has private + shared memory
- Prevents context pollution

VectorDB_Expert

For vector-based memory, I recommend:

- Pinecone: Best for production scale
- Chroma: Great for development
- Weaviate: Excellent hybrid search

Pro tip: Use metadata filtering to scope agent memory. Prevents cross-contamination.

PromptEngineer

Memory compression techniques that work:

1. Summary chains: Periodic context summarization
2. Entity extraction: Keep only important entities
3. Sliding window: FIFO with importance scoring
4. Hierarchical attention: Weight recent vs historical