Add Loom memory-service integration for LongMemEval#2
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Loom (https://github.com/ClickHouse/loom) is a ClickHouse-backed memory service. This adds loom/run_loom.py, which plugs Loom in at the INDEXING + RETRIEVAL stages (ingest each haystack session via memory.set_from_messages, retrieve via memory.search) and reuses the official reader prompt + the official src/evaluation/evaluate_qa.py judge. It writes a hypotheses JSONL to grade with the existing judge, and reports evidence-session recall@k. The official src/retrieval/run_retrieval.py targets in-process retrievers (BM25/Contriever/Stella/GTE) over a flat corpus and has no hook for an external memory service, which is why this adapter exists. Includes README + requirements; validated end-to-end against a live Loom server. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The dataset is category-ordered, so a bare --limit samples a single question type. --shuffle (deterministic via --seed, default 42) gives a mixed sample for quick partial runs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…nostics Applied before --shuffle/--limit so a single LongMemEval category can be run complete (e.g. all single-session-assistant) for non-noisy per-category recall. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The published memory benchmarks (mem0, Zep) each grade LongMemEval with their own semantic-equivalence judge, not the bare upstream anscheck. Comparing Loom's strict-judge number against their lenient-judge numbers is apples-to- oranges. Add a `fair` judge (default) whose every added rule is one the official prompt OR both competitor graders already apply (meaning-not-wording, superset-correct, more-precise-correct, temporal off-by-one), while excluding mem0-only catch-alls — so it sits in the field's strictness band: no benchmin, no benchmax. Report this single number, like competitors do; keep the upstream strict judge available via --judge-style official for reproducibility. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
evaluate_qa.py grades by semantic equivalence (paraphrase / superset / more- precise correct; temporal off-by-one tolerated), per question type. run_loom.py adds fact-level recall@k (is the gold answer string actually in a retrieved excerpt — the metric that tracks QA, unlike session recall), renders dated history + derived-fact blocks for the reader, and leaves the generative reranker off by default so the number reflects real retrieval latency. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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What
Adds
loom/so a Loom memory service can be benchmarked on LongMemEval.loom/run_loom.py)src/generation/run_generation.pyprompt (replicated)src/evaluation/evaluate_qa.py(run unchanged)run_loom.pyingests each question haystack into Loom (onememory.set_from_messagesper session, concurrent), retrieves viamemory.search, generates an answer with the official reader prompt, and writes a hypotheses JSONL to grade with the existing judge. It also reports evidence-session recall@k.The official
src/retrieval/run_retrieval.pytargets in-process retrievers (BM25/Contriever/Stella/GTE) over a flat corpus and has no hook for an external memory service, which is why this adapter exists (the same pattern other memory products use to plug into LongMemEval).Usage
Validation
Smoke-tested end-to-end against a live Loom server (ingest -> retrieve -> official reader -> official judge runs clean). Details in
loom/README.md.🤖 Generated with Claude Code