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Semvec vs. mem0

mem0 is the most-deployed agentic memory layer in 2026 and the most direct functional alternative to Semvec. This page compares the two on architecture and on the only head-to-head benchmark we have run against it: LongMemEval-S.

Architectural differences

Property Semvec mem0
Per-turn input footprint Constant — fixed-size compressed state (~150–350 tokens) Linear in the number of retrieved records placed in the prompt
Ingest LLM calls per turn 0 — pure mathematical EMA over the embedding LLM-driven fact extraction (~50 internal calls per turn observed on LongMemEval-S)
Recall procedure Deterministic (cosine over fixed-size state + literal cache) LLM-extracted facts retrieved from store
Numeric / exact-value safety Verbatim cache with Decimal precision (IBANs, amounts, IDs, dates) Embedded into semantic records — lossy under cosine retrieval
Determinism on replay Bit-exact across replays Probabilistic (LLM extraction temperature)
Self-hosted Yes (proprietary license, on-prem) Yes (OSS)
Multi-agent coordination Built-in (Cortex: aggregations + 5-level consensus) Manual orchestration

Both are self-hosted. The architectural split is deterministic vs. probabilistic at ingest and constant vs. linear at the prompt boundary.

Head-to-head benchmark — LongMemEval-S

LongMemEval (Wu et al., 2024) is the established multi-session memory benchmark for LLMs. Each of the 500 tasks consists of ~40 prior chat sessions followed by a question whose answer is distributed across the history.

Setup: model and judge gpt-oss-120b on H100, temperature 0.0. mem0 v1.0.11.

System Accuracy 95 % CI Total wall-clock
Semvec (Multi-PSS, 3 vectors) 42.8 % [38.5 ; 47.2] 2.77 h
mem0 v1.0.11 36.2 % [32.1 ; 40.5] 47.04 h
Full-history baseline 23.2–24.4 % — —

McNemar test on the 191 discordant pairs: p = 0.020 — the lead is statistically significant at α = 0.05.

Per-category breakdown

Semvec wins 4 of 6 question categories. Strongest deltas:

  • single-session-assistant: +34 pp (p = 0.0003)
  • temporal-reasoning: +10.6 pp (p = 0.039)

Cost dynamics

  • Semvec ingest: 0 LLM calls per turn — embeddings only.
  • mem0 ingest: ~50 internal fact-extraction calls per turn on LongMemEval-S, totalling roughly 25 000 LLM calls across the benchmark. At ~2 000 tokens per call, that is in the 50–75 M-token range — orders of magnitude above Semvec.
  • Per-entry latency: Semvec 19.9 s vs. mem0 338.7 s average.

When to pick which

Pick Semvec when:

  • per-turn input cost must be O(1) — fixed system-prompt budget,
  • ingest must be free of LLM cost and deterministic across replays,
  • numeric / IBAN / amount / date values must round-trip with Decimal precision,
  • you need an append-only event store with deterministic replay and signed deletion certificates,
  • you're regulated and need every mutation reconstructable from an audit log.

Pick mem0 when:

  • you want an OSS-licensed turnkey memory layer with an established Python / TypeScript API,
  • LLM-driven fact extraction is acceptable for your latency / cost budget,
  • you're integrating into an OSS-only stack where proprietary licensing is a no-go.

Reproducibility

The LongMemEval harness ships with Semvec via pip install "semvec[benchmarks,mem0]". The exact command we ran:

.venv/bin/python -m semvec.benchmarks.longmemeval \
    --variant S --multi-pss --temperature 0.0 \
    --embed-device cuda \
    --per-type 10 --n-judges 3 \
    --output results/semvec_full.json

See the benchmarks overview for hardware setup and the parity envelope for the determinism guarantees that make replays bit-comparable.

Sources

  • LongMemEval (Wu et al., 2024): https://arxiv.org/abs/2410.10813
  • mem0: https://github.com/mem0ai/mem0 (v1.0.11 used in this comparison)