Zero-Mem: Zero-Token Memory Operations for LLM Agents

Agent memory that never calls an LLM until the final answer, and still scores higher.

0.22

s per query

Memory-operation time per query

Zero LLM tokens spent on memory, and faster than every baseline

How it works

Memory costs LLM calls
Many agent memories summarize, extract or rewrite history with extra LLM calls. That costs tokens and time, and can blur details.
Index raw traces instead
Keep original conversation turns. Build an entity graph and a time hierarchy with NER, BM25 and embeddings, then retrieve and filter deterministically.
Zero tokens, better answers
Only the final reader uses an LLM. On LoCoMo it beats GAM by 5.40 average F1 with GPT-4o-mini.

From the paper