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MEME-BENCH: code
Code for MEME-BENCH, a benchmark of long-term memory over longitudinal patient histories: the evaluation runner and judge, the adapters of the long-context baseline and the eleven evaluated memory systems, the pipeline that constructed the benchmark, and its prompts. The benchmark data (23 patients, 15,049 visit dialogues, 1,646 questions) is released separately.
Contents
code/
memebench/ evaluation runner, judge, method adapters, configs -> docs/EVALUATION.md
vendor/ licenses, patches, and the download script of the engines
services/ backing services for MemOS
construction/ the pipeline that built the benchmark (reference) -> construction/build_patient.sh
requirements/ pinned environments
prompts/ the prompts of the construction pipeline, the evaluation and the methods -> prompts/README.md
docs/ EVALUATION.md
Data
The evaluation reads the patient folders of the data release (P01 ... P23, each with dialogues.jsonl and
questions.jsonl). Place them under data/ next to code/: the default patient list,
code/memebench/configs/patients.yaml, reads ../data/P*. --patients-config takes another such file, listing
other locations.
Quick start
cd code
# set up the environment as in docs/EVALUATION.md (Environment)
export OPENAI_API_KEY=...
# set every <...> value in memebench/configs/backbone.yaml and max_context_tokens at the top of
# memebench/configs/methods.yaml
python -m memebench.run_eval --method bm25_rag --patient <patient>
Results are written to code/outputs/<method>_<answer_model>/<patient>/; see
docs/EVALUATION.md. The construction pipeline (code/construction/) documents how the
benchmark was built; two of its inputs are not distributed, so it cannot be run end to end.
License
Apache-2.0 (LICENSE); the third-party engines keep their own licenses (THIRD_PARTY_NOTICES.md).
Citation
@misc{memebench2026,
title = {MEME-BENCH: Long-Term Memory over Longitudinal Patient Histories},
author = {Anonymous},
note = {Under review},
year = {2026}
}