Enki – memory for AI agents that keeps ~half as much and answers as well
Enki is a memory engine for AI agents that achieves comparable accuracy to mem0 while using roughly half the storage. In a 25-instance evaluation, Enki scored 14/25 vs 12/25, with notable strength in multi-session reasoning (4/5 vs 2/5). Latency averages 7.6ms on CPU.
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Enki is a memory engine for LLM agents. This repository publishes evaluation results only — the engine is closed-source. No configuration, internals, or methodology beyond what is described below is included here.
LongMemEval — Enki vs mem0 (head-to-head)
Both systems ingest identical conversation histories from LongMemEval-S. Each system's retrieved memories are answered by the same model (Claude Haiku) and graded by the same LLM-as-judge, at equal retrieval depth (K=10). The only variable is the memory layer.
Validated slice: 25 instances (full-benchmark run in progress).
Question type Enki mem0
Multi-session reasoning 4 / 5 2 / 5
Knowledge update 3 / 5 3 / 5
Single-session (user) 3 / 5 3 / 5
Single-session (assistant) 2 / 5 2 / 5
Single-session (preference) 2 / 5 2 / 5
Total 14 / 25 12 / 25
Storage: Enki answers from 0.49× the stored facts mem0 keeps on the same conversations (mean 138 vs 283).
Standout: multi-session reasoning (4/5 vs 2/5).
Honest framing. This is a small, hand-validated slice; the overall margin (14 vs 12) is modest and within what a 25-item sample can show. The robust, repeatable result is comparable answer accuracy at roughly half the memory footprint, with a clear multi-session advantage. Further evaluation is ongoing.
Retrieval latency (CPU-only)
Measured on a ~139-fact store, CPU-only (no GPU), 240 samples:
Percentile Latency (ms)
mean 7.6
p50 6.1
p95 11.9
p99 13.0
Reproducibility
Full methodology and per-question results are available on request.
Enki Labs (UK) · 2026
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