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Tex scores 93.3% overall on LoCoMo with the full system. Tex scores 92.2% on LongMemEval_S with active retrieval only. This page shows the category tables, latency, tokens, and methodology behind those numbers.

LoCoMo · 93.3%

Full Tex system vs published baselines. EverMemOS was the prior headline number at 92.3%.

LongMemEval_S · 92.2%

Active retrieval track vs other retrieval-first systems. Emergence AI posted 86.0% on comparable reporting.
We generated answers with gpt-4o-mini and graded them with gpt-4o in an LLM-as-judge setup. Each evaluation ran on a single machine. The exact setup is in Methodology below.

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LoCoMo

LoCoMo evaluates long-conversation memory across 10 multi-session conversations with 1,984 questions spanning 5 categories: single-hop retrieval, multi-hop reasoning, temporal reasoning, open-domain inference, and adversarial abstention.

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Per-category results

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Competitive landscape

On adversarial items Tex is 99.33%. It declines to answer when the transcript does not support an answer. If a public benchmark skips that bucket, compare headline numbers carefully.

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LongMemEval_S

LongMemEval_S evaluates memory over 500 questions across ~48 sessions each (~115K tokens), testing information extraction, multi-session reasoning, temporal reasoning, knowledge updates, and abstention.

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Per-ability results

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Competitive landscape

92.2% is close to Oracle GPT-o3 (92.0%) and well above Oracle GPT-4o (82.4%). That means retrieval is finding evidence close to what you would hand-pick for each question.

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Latency

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Token efficiency

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Per-question breakdown (Tex)

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LLM tokens consumed on LoCoMo (1,984 questions)

Ingest here costs zero LLM tokens. This run used offline embeddings only. If a system runs an LLM per message during ingest, that cost should show up in token usage.

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Headline efficiency claims

Compared with published Mem0 numbers, Tex is about 27% higher on accuracy, uses roughly 87% fewer tokens, and runs at about 95% lower latency. Against MemMachine’s memory-only configuration, Tex is about 5.8% more accurate on 43% fewer tokens. On LoCoMo, Tex also has the lowest tokens-per-correct-answer in this comparison: about 1,296.

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Ablation: what each part of the pipeline adds

On LongMemEval_S, removing pieces of the retrieval pipeline changes accuracy like this:

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Methodology

Answers came from gpt-4o-mini. We graded them with an LLM-as-judge setup built on gpt-4o, using category-specific prompts, binary pass/fail per item, and a straight average inside each category. LoCoMo used all ten provided conversations (1,984 question-answer pairs) against the full Tex system (not retrieval-only). LongMemEval_S used 500 questions with about 48 sessions each (~115K tokens per trace) against Tex Active retrieval only. The full pipeline ran on a single machine. There was no multi-node orchestration.

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On our roadmap for evals

We plan to add MemoryAgentBench (ICLR 2026), stronger multi-step reasoning for questions that need counting or arithmetic over evidence, and LongMemEval_M for questions that span hundreds of sessions.

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References

  1. Maharana et al. LoCoMo: Long-Context Conversations for Evaluating Conversational Memory. EMNLP 2024.
  2. Wu et al. LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory. ICLR 2025.
  3. Hu et al. Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions. ICLR 2026.
  4. MemMachine. MemMachine v0.2 Delivers Top Scores on LoCoMo. Dec 2025.
  5. Emergence AI. SOTA on LongMemEval with RAG. 2025.
  6. Supermemory. State-of-the-Art Agent Memory Research. 2026.
  7. Mastra. Observational Memory: 95% on LongMemEval. 2026.

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