Evaluation · Benchmark

The LOCOMO Benchmark for Agent Memory

LOCOMO benchmarks long-conversation memory — testing whether agents recall facts from thousands of tokens earlier in the chat — and is the standard citation for comparing Engram, Mem0, Zep, Letta and other memory frameworks.

LOCOMO tests

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Long chat
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Distant fact
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Recall probe

Definition

What LOCOMO measures

Long-dialogue recall tasks — question answering on facts buried in distant context within a single long conversation.

LOCOMO (Long Conversation Memory) tests whether agents remember what was said thousands of tokens earlier in the same chat. It is the primary benchmark for in-session memory depth — distinct from cross-session evals like LongMemEval.

Long context vs memory

Scoring

How LOCOMO scores work

LLM-as-a-Judge (J) accuracy on memory probes — higher is better.

The Mem0 paper (Chhikara et al., 2025) reports overall J scores across LOCOMO tasks. The same evaluation run scored the full-context baseline at 72.9 J — selective memory frameworks can beat raw long-context on efficiency while approaching or exceeding recall quality.

Also reported: retrieval latency (Mem0 median search 0.148 s) and token usage (~1,800 vs ~26,000 per query).

Results

Framework results on LOCOMO

Last updated: July 2026. N/A = no public result — we never estimate.

FrameworkLOCOMO JSourceLatency (LOCOMO eval)
EngramN/AGA June 2026 — pendingN/A
Mem066.9Chhikara et al., 20250.71 s p50 / 1.44 s p95
Zep66.0Rasmussen et al., 2025 (Mem0 paper eval)1.29 s p50 / 2.93 s p95
LangMem58.1Chhikara et al., 2025 eval18.5 s p50
Letta (MemGPT)N/A on LOCOMODMR 93.4% (Packer et al., 2023)N/A
Full-context baseline72.9Chhikara et al., 202517.1 s p95

Best AI memory tools

Comparison

LOCOMO vs LongMemEval

LOCOMO = long single conversation; LongMemEval = cross-session recall with temporal gaps.

Use both: LOCOMO for in-session depth; LongMemEval for multi-session persistence. Zep reports LongMemEval 71.2% with gpt-4o (+18.5% vs baseline, Rasmussen et al., 2025).

LongMemEval benchmark

How-to

How to run LOCOMO on your agent

  1. Obtain the LOCOMO dataset
  2. Integrate your memory framework (Engram, Mem0, Zep, etc.)
  3. Run the eval harness with your LLM backend
  4. Compare J scores, latency and token usage across configs
  5. Gate production deploys on recall@k regression

Add memory to an AI agent · Memory metrics

FAQ

Frequently asked questions

What is the LOCOMO benchmark?

LOCOMO tests long-conversation memory — whether agents recall facts from thousands of tokens earlier in the same chat. Standard eval for comparing memory frameworks.

Who published LOCOMO?

LOCOMO is widely cited in agent-memory research. The Mem0 paper (Chhikara et al., 2025) publishes comparative LOCOMO J scores for Mem0, Zep, LangMem and full-context baselines.

What is Mem0's LOCOMO score?

LOCOMO J 66.9 (Chhikara et al., 2025). Median search 0.148 s; ~1,800 vs ~26,000 tokens per query.

What is Letta's LOCOMO score?

N/A on LOCOMO publicly. The MemGPT paper (Packer et al., 2023) reports 93.4% on Deep Memory Retrieval (DMR) — a different benchmark.

LOCOMO vs LongMemEval?

LOCOMO = long single conversation recall. LongMemEval = cross-session recall with temporal gaps. Use both. See LongMemEval.

Can you pass LOCOMO with long context only?

Full-context baseline scored 72.9 J but uses ~26,000 tokens and 17.1 s p95 latency vs Mem0's ~1,800 tokens and 1.44 s p95 (Chhikara et al., 2025).

What is Engram's LOCOMO score?

N/A publicly as of July 2026 — Engram reached GA in June 2026. See Engram explained.

Where is the LOCOMO leaderboard?

No single official leaderboard — scores are published in framework papers. We track published results on best AI memory tools.