🧠 Mneme

A cue-indexed tag memory for conversational agents — no embeddings, no LLM in the memory path.

Most agent memories embed every turn and distill it with a write-time LLM, then search by vector similarity — expensive and opaque. Mneme instead does what human recall does: index by cues, reinstate by cue-match, and spread activation along both shared meaning and the thread of conversation — with a plain inverted index over deterministic tags. No vector database, no language model, no embeddings.

At zero memory cost, under an identical controlled comparison, on each benchmark's primary metric Mneme is on par with a strong dense retriever (LoCoMo F1 52.4±0.1 vs 52.9; LongMemEval acc 62.8 vs 62.2) and beats the cheaper baselines (mem0 and BM25 on LoCoMo, BM25 on LongMemEval). Same answerer, judge, prompt, and budget; only the memory differs.

✨ Why Mneme

• Zero cost. No write-time LLM calls, no embeddings, sub-millisecond recall, storage is plain JSON files.
• Competitive quality. Matches or beats embedding + LLM memory on standard benchmarks (see below).
• Interpretable. Retrieval is explicit cue overlap ranked by IDF — no opaque similarity threshold to tune.
• Human-like forgetting. What has no distinctive cue, or whose cues never recur, is simply never surfaced — forgetting as retrieval failure, for free.
• Local & sovereign. Your memory is a folder of files you can read, edit, or delete. English by default; Chinese optional.

📊 Benchmarks (controlled: same answerer/judge/prompt/budget, only the memory differs)

LoCoMo — low-distractor dialogue (F1 / accuracy):

LongMemEval — high-distractor, ~50 sessions per question (accuracy):

Zero-cost Mneme is on par with dense retrieval on both benchmarks, at no embedding or write-time cost, and it is reader-agnostic (near-identical with two different answerer LLMs) and interpretable. Which edge type helps is regime-sensitive: discourse edges clearly help clean dialogue (~5 points on LoCoMo), while on high-distractor histories the edge configurations differ only within run-to-run noise.

🚀 Install

pip install mnemekit
python -m spacy download en_core_web_sm     # English model (required)

pip install "mnemekit[zh]"                   # optional: Chinese (jieba)

New in 0.3

Mneme now compiles typed propositions alongside its original tags in the same deterministic spaCy pass. The stored representation includes provenance-linked memory atoms, event collections, participant roles, salience, repetition, and access history. These structures support ontology recall and inspectable evidence cards while keeping the memory path free of embeddings and language models. The existing Memory.recall interface and old stored turns remain compatible.

⚡ Quick start

from mneme import Memory

m = Memory(root="~/.mneme")

m.remember("My dog Lucky is a golden retriever", "Cute! How old is Lucky?",
           session_id="chat-42", round_id=1)

m.recall("what breed is my dog")     # -> [Turn(...), ...]

Turns are stored one file per round at {root}/{session_id}/{YYYY-MM-DD}/{round_id}.json, so any session is trivial to inspect, export, or delete.

🔍 How it works

INGEST (no LLM, no embedding)
  turn ──▶ tag extraction (spaCy NER + lemmas / jieba) ──▶ inverted index  (tag → turns)
                                                           + event tag inheritance

RECALL (per query) — spread over an ephemeral heterogeneous graph, then discard it
  query ─▶ ① lexical: IDF cue reinstatement ─seeds─┬─▶ ② associative edge (shared tag, 1 hop)
                                                   └─▶ ③ discourse edge (adjacent turn in event)
                                                                        │
                                                          recalled context ─▶ your reader

1. Cue index. Each turn is tagged deterministically; tags form an inverted index (a sparse cue index, à la hippocampal indexing). Keyword-free follow-ups inherit their event's tags, so they stay recallable.
2. Reinstatement, then two kinds of edge. A query reinstates directly-cued turns lexically, then spreads activation over an ephemeral graph with two edge types: associative edges (shared discriminative cue → one hop) reach the associative tail a direct cue can't; discourse edges (adjacent turns in the same event) pull in the local conversational context around a hit. Neither is materialized — both are read straight off the index and event structure, used for the one query, and discarded.

🀄 Chinese & custom dictionaries

m = Memory(root="~/.mneme", userdict="my_terms.txt")   # jieba user dictionary
m.remember("我养了只金毛狗，叫 Lucky", "可爱！", session_id="u1", round_id=1)

Chinese is segmented by jieba; a custom dictionary keeps vertical-domain terms intact.

📄 Paper & reproduction

The write-up (method, cognitive grounding, controlled comparison, ablations) is in paper/; the evaluation harness (LoCoMo / LongMemEval / mem0) is in eval/. The frozen reproduction version is on the paper-repro branch.

📌 Citation

@misc{mneme2026,
  title  = {Do Conversational Agents Need Vectors? A Zero-LLM, Zero-Embedding Tag Memory},
  author = {Hao, Shaochun},
  year   = {2026},
  note   = {https://github.com/FTP2026/Mneme}
}

License

MIT