billy12151/memory-arbiter-mcp

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**Official site & docs: memarbiter.cn**
Memory Arbiter (迷码) is a capability amplifier for your AI agents: plug it in over MCP and every agent you use — Claude Code, Cursor, Codex, ZCode — becomes one assistant with a running memory. Decisions made in Claude today are usable in Cursor tomorrow; every fact carries its provenance, collisions get surfaced before they bite, and what you've locked cannot be silently overwritten. One SQLite file on your machine holds every fact in full; every model — embeddings, conflict judge — runs locally.
> **Current release: 0.18.0** · local-first · MCP-native · 4 tools · knowledge workbench in 0.18.0 (on main now)
In one minute: what mema does for you
Every coding agent you use — Claude Code, Cursor, Codex, ZCode — keeps its own notes and knows nothing about the others. Facts get duplicated, contradicted, and silently go stale. mema replaces that with **one trusted fact store all your agents share**:
- **Recall that actually works.** Lexical and sentence-level evidence channels search in parallel and fuse by rank: **65/65 self-recall** on the release exam. Sub-threshold candidates never reach a result page — what it returns is what you can cite — and every hit carries source offsets, so read span=[start,end] slices the exact original text.
- **Conflicts get surfaced, not buried — and not spammed.** When two facts disagree, a self-trained local mDeBERTa judge (three classes, no prompt engineering, no winner-picking) calls it: only high-confidence conflicts interrupt you, the grey zone shows up as low-key reference, and everything else quietly goes to the scheduled scan — so a strict threshold never means a lost finding, and an open gate never means noise. On the release exam, **90.9% of the conflicts it interrupts you with are real**.
- **Facts stay yours — and stay safe.** Full original text in one SQLite file on your machine, provenance on every write, version history on every edit, trust levels that lock what you confirmed. mema dials out nowhere on its own — the only optional call is a PyPI update check (the console workbench talks to the LLM endpoint you configure).
- **A knowledge base, curated by you.** The local browser console lets you pick which memories count as knowledge, browse them by workspace → tag with rendered markdown and relation graphs, and study them with a bring-your-own-LLM workbench (summarize / outline / quiz / Feynman / quiz-me / timeline) whose outputs write back through the standard pipeline.
Benchmarks
Public: LoCoMo-Refined
Evaluated on LoCoMo-Refined (all 1,382 questions, official judge Qwen3-14B):
| Configuration | Overall | Text-only |
|---|---:|---:|
| **mema + writer-agent** (deepseek-v4.1-flash writer, public rules) | **61.00%** | **65.62%** |
| **mema verbatim** (zero-LLM write path, utterances + event_time as-is) | 43.13% | **49.48%** |
Context: Mem0's full pipeline scores 48.91% on the same text-only basis (re-scored by the benchmark authors) — **bare mema with no LLM in the write path matches it**; with a writer-agent, mema's text-only score trails only MemoraX on the leaderboard. Full methodology and reproduction: evals/locomo/RESULTS.md.
Internal release exam (shipped configuration)
Every release must pass the internal exam suite — fixed fixtures: 42 recall queries across five query kinds (paraphrase / lookup / legal / far / keyword), 108 conflict pairs across four label classes (36 true conflicts), and four similarity-label groups (as of 0.17.2, shipped judge and thresholds; reproduce with eval/runner.py --suite all):
| Gate | Result |
|---|---|
| Self-recall (65 targets, top-10) | **65/65** |
| Conflict arbitration (D4 headline, any-channel) | **Recall 55.56% (20/36) · Precision 90.91% (20/22)** |
| batch_find cross-query consistency | **0 mismatches** (42 queries × 6 batches) |