CURRENT

Store conversations as embeddings, retrieve by meaning rather than exact text. Works beautifully for finding similar past interactions even when phrasing changes. Scales well, responds fast. But semantic similarity alone misses context: two conversations about "the Paris project" might mean completely different things.

Use LLMs to periodically compress conversation history into rolling summaries. Achieves 89-95% compression while keeping context bounded. The risk: details that seem trivial now might matter later. Works when storage costs outweigh the occasional missed nuance in long-running interactions.

Organize memories as nodes and edges: people, places, events, temporal connections. Enables reasoning about how information relates across sessions. Zep's temporal graphs show 18.5% accuracy gains with 90% lower latency. Choose this when agents need to understand how facts connect and change.

Mem0 moves information between short-term and long-term storage based on usage patterns, recency, significance. Mimics how human memory actually forms: selective retention instead of compressing everything. Shows 26% quality improvements while cutting token costs by 90%. Balances sophistication with practical simplicity.

Zep encodes entities and relationships in temporal knowledge graphs that update with each interaction. Tracks when facts change, invalidates outdated information while preserving history. Combines semantic embeddings, keyword search, and graph traversal for near-constant retrieval time regardless of memory size.

Pass entire conversation history directly to the model without external storage. Eliminates retrieval complexity for short interactions. Models claiming 200,000 token windows become unreliable around 130,000. Context resets with each request, so no learning across sessions or persistent identity emerges.

Full-context approaches hit 72.9% accuracy while burning through 26,000 tokens per request. Memory-optimized systems like Mem0 reach 66.9% accuracy at a fraction of that. Output tokens cost 3-8× more than input. Pick comprehensive understanding or sustainable economics.

Mem0 delivers 0.71s median latency versus full-context's 9.87s. That speed costs 6 percentage points of accuracy. Voice applications can't wait nine seconds for context assembly. You optimize for speed because the alternative makes your product unusable.

Trust decay multiplies scores by 0.95 raised to age in days, filtering entries below 0.6. Set it aggressive and you block legitimate context. Set it permissive and subtle attacks slip through. Both choices degrade the system differently.

HNSW achieves higher recall than IVF but consumes several times more memory. At 100 million vectors, 1536 dimensions requires 600GB. Drop to 768 dimensions and you fit 400 million vectors in the same RAM. Accuracy and density move opposite directions.

FLAT provides brute-force exact search but can't scale beyond small datasets. Approximate k-NN returns results in milliseconds instead of seconds. Production systems configure HNSW or IVF to exceed 95% recall. Never 100%. That last five percent costs too much.

MicroVMs eliminate shared kernel vulnerabilities through hardware virtualization. They also add 15-20% overhead. Containers are lightweight but fail when agents execute untrusted code. TEE infrastructure reduces breach liability by 85% while slowing execution and raising infrastructure spend.