mcp-name: io.github.billy12151/memory-arbiter-mcp

memory-arbiter-mcp

中文 | English

English

Memory Arbiter is a trustworthy local fact layer for AI agents.

Chinese name: 迷码. Short name / CLI alias: mema.

It can be used as shared memory, but its real job is fact governance: keeping long-running project context searchable, traceable, source-aware, conflict-aware, and safe to recall.

Shared memory lets every tool see the same data. Memory Arbiter goes further: it helps agents tell which facts are current, user-confirmed, stale, conflicting, superseded, or still waiting for judgment.

# Instead of dumping 20K tokens of MEMORY.md into every prompt:
memory(action="find", data={"query": "auth migration plan"})  → 3 laser-relevant entries, ~400 tokens

Shared memory is the starting point. Fact governance is the moat.

Fully local by default: one SQLite database, no Postgres, no Redis, no hosted memory service, and no server-side LLM calls. Optional semantic recall uses your own local GGUF embedding model; optional update checks can be disabled.

The problem

Many memory systems focus on how agents remember. Memory Arbiter focuses on what happens after memory becomes shared.

When Claude Code, Cursor, Codex, ZCode, WorkBuddy, OpenClaw, or other tools all write into the same long-term context, forgetting is no longer the only failure mode. The harder failures are:

• stale facts mixed with current decisions;

• AI guesses treated like user-confirmed truth;

• contradictory conclusions written by different tools;

• long project histories drowning the few facts that matter;

• tool switching causing either context loss or repeated context pollution;

• local memory growing until every prompt starts with thousands of irrelevant tokens.

Memory Arbiter turns those risks into explicit data structures: source labels, confidence, event time, version history, supersede chains, conflict records, structured claim gates, section indexes, workspace boundaries, and doctor diagnostics.

The model still does semantic reasoning. Arbiter keeps the input side cleaner.

What Memory Arbiter does

Daily mental model

Most agents only need four product tools (the default MCP surface):

1. memory — daily operations: remember new facts, find active facts, read a memory by ID, update an existing current memory, judge conflicts, and status. Call action=help for field examples.

2. memory_review — read-only inspection: overview, doctor, conflicts, conflict detail, judgments, history, expired memories, audit, and entities.

3. memory_govern — explicit user-authorized governance: retire a whole memory, resolve a conflict, confirm a memory, or correct a judgment. Not for ordinary updates.

4. memory_repair — maintenance: section split, rebuild claims/embeddings, cleanup, vector resync, entity backfill, and pending activation. Prefer dry-run first.

Low-level tool implementations remain in the codebase and are reused by the product tools, but their schemas are not exposed by default. Set MEMORY_ARBITER_TOOL_PROFILE=legacy_full (or full) to expose them alongside the product tools.

How it differs

Memory Arbiter does not compete by saying that other tools cannot share memory. Shared memory is becoming standard. Memory Arbiter focuses on what shared memory needs next.

Graph-like signals exist where they help governance: event time, ingest time, entity/scope, conflict edges, supersede chains, sections, and workspace boundaries. Memory Arbiter treats original facts as the primary asset and derived indexes as support structures.

Token savings are a side effect

The main value is better context quality. Token savings are the most visible effect.

Same model. Better input. Better output.

Works with one tool. Scales to many.

With one tool, Memory Arbiter upgrades local memory from flat files into a queryable fact layer with trust labels, history, conflict signals, and diagnostics.

With multiple tools, it also becomes shared memory: Tool A writes, Tool B searches, Tool C audits. No file handoff, no copy-paste, no version drift.

Example pipeline:

1. OpenClaw writes a spec with memory(action="remember").

2. OpenDesign reads the spec with memory(action="find"), writes back design decisions.

3. ZCode searches once and gets both the spec and design decisions.

Three tools, one local fact layer.

For concrete usage patterns and a cross-tool walkthrough, see docs/INTEGRATION.md.

Core capabilities

• Targeted retrieval — return the relevant entries instead of loading full memory files every turn.

• Trust levels — separate user-confirmed facts, document extracts, AI-generated notes, and unknown sources.

• Temporal history — track event time, ingest time, versions, history snapshots, and supersede chains.

• Conflict arbitration — discover, record, inspect, resolve, or supersede contradictory memories.

• Structured claim gates — v0.9 write/edit-time deterministic claim detection with required host-LLM judgment before use.

• Long-document section split — split long memories into searchable sections and return matched paragraphs.

• Workspace isolation — choose none, weak, or strict isolation with workspace alias canonicalization.

• Smart tag ranking and filters — tags act as discrete ranking/filter labels, not weak text fragments.

• Semantic recall — optional local GGUF embeddings for meaning-based recall, while lexical recall remains the default.

• Doctor diagnostics — read-only health checks for config, vector readiness, split, claims, consistency, capacity, and conflicts.

• Graceful degradation — sqlite-vec → FTS5 → LIKE → JSONL backup, so the server keeps working when optional pieces are unavailable.

• Local-first storage — pure SQLite, no hosted database, no Redis/Postgres requirement, no server-side LLM dependency.

What it is not: Memory Arbiter is not an LLM and does not replace your AI client. It is a structured storage, retrieval, arbitration, and diagnostics layer underneath the model.

Quick Start

Requirements: Python 3.11+ (3.11, 3.12, or 3.13).

# Clone
git clone https://github.com/billy12151/memory-arbiter-mcp.git
cd memory-arbiter-mcp

# Setup — use whichever python3.1x you have (>=3.11)
python3.11 -m venv .venv
source .venv/bin/activate
pip install -e .

# Optional: semantic recall via sqlite-vec
pip install -e '.[vec]'

# Run (short alias)
mema

# Compatible long names still work:
# memory-arbiter
# memory-arbiter-mcp

Zero-install via uvx

If you just want to run the server without managing a Python environment, install uv once, then:

uvx --from memory-arbiter-mcp mema

This pulls the published package and launches the mema entry point. mema is the short alias for Memory Arbiter; memory-arbiter-mcp and memory-arbiter remain compatible long names. uvx only shortens the install path; embedding models and sqlite-vec still need separate setup if you want semantic recall.

Setup helper

Instead of editing config.json by hand, run:

mema setup

The helper writes a working config to ~/.config/memory-arbiter/config.json, checks your environment, and prints the exact commands or download URLs you still need. It does not run pip or download models for you.

Useful flags: --print-config, --no-config, --force.

Local Console MVP

Start the read-only local governance Console:

mema console

It opens http://127.0.0.1:18876 by default. Optional flags:

mema console --no-open       # start the server without opening a browser
mema console --port 18877    # use a different port when 18876 is busy

The Console listens on 127.0.0.1 by default and is local-only in this version. It is a visibility and review surface, not a memory editor: Overview, Conflicts, Conflict Detail, Memories, Doctor, and Settings are read-only. The UI switches between English (mema Console) and Chinese (迷码 Console); the CLI remains English-only and uses the mema alias.

The Support Panel offers GitHub Star, feature request, UX feedback, and bug report shortcuts through prefilled public issue links. It does not upload memory content automatically, does not store GitHub tokens, and does not call GitHub APIs.

Useful boundary: do not expose the Console port publicly. It can display memory content from your local database.

Connect your tool

Add Memory Arbiter to your MCP config. With a local virtualenv:

{
  "mcpServers": {
    "memory-arbiter": {
      "command": "/path/to/memory-arbiter-mcp/.venv/bin/memory-arbiter-mcp",
      "env": {
        "MEMORY_ARBITER_CLIENT": "zcode",
        "MEMORY_ARBITER_AGENT_ID": "zcode-default"
      }
    }
  }
}

Or via uvx:

{
  "mcpServers": {
    "memory-arbiter": {
      "command": "uvx",
      "args": ["--from", "memory-arbiter-mcp", "memory-arbiter"],
      "env": {
        "MEMORY_ARBITER_CLIENT": "zcode",
        "MEMORY_ARBITER_AGENT_ID": "zcode-default"
      }
    }
  }
}

Change MEMORY_ARBITER_CLIENT for each tool (openclaw, zcode, codex, cursor, claude-code, workbuddy, ...). Put shared database, vector, and model settings in ~/.config/memory-arbiter/config.json; keep per-client identity in the MCP env block.

New session required: MCP servers are loaded at session startup. Already-open sessions will not see newly added tools.

Agent onboarding guide

For the compact rule agents should save to their persistent local memory file, see the local memory_arbiter/AGENT_ONBOARDING.md file or the GitHub copy at https://github.com/billy12151/memory-arbiter-mcp/blob/main/memory_arbiter/AGENT_ONBOARDING.md. Agents can also read the same guide through memory(action="help", data={"topic": "agent_onboarding"}).

Client config locations

OpenDesign and OpenClaw GUI tools run on top of a host CLI. They inherit whatever MCP server the host client has loaded.

MCP tools

v0.11.0 introduces a task-oriented default MCP surface. New clients see four product tools instead of the legacy low-level tool list:

Low-level tool implementations remain inside Memory Arbiter and are reused by the product tools, but their schemas are not exposed by default. This keeps ordinary Agent context smaller and makes the daily path easier to choose.

Advanced compatibility: set MEMORY_ARBITER_TOOL_PROFILE=legacy_full (or full) to expose the legacy low-level MCP tool surface alongside the product tools.

Optional: write-time semantic conflict check

Memory Arbiter can optionally run a local Qwen2.5-0.5B model after writes to produce semantic conflict notices — lightweight, reviewable hints that a new memory may semantically conflict with an existing one. The model is only a candidate signal, never the final judge: whether a candidate becomes a visible notice is decided by pair-text gates (medium by default for balanced recall; strong for lower-noise, higher-confidence notices).

Pipeline: metadata-overlap coarse recall (subject + tags) → 0.5B pair classification → pair-text gate → semantic_notices row. Notices are viewed and dismissed via memory_repair(task="notice", ...), and runtime control is via memory_repair(task="semantic_control", ...). This is currently the only automated conflict-candidate source: the legacy vector conflict-candidate scan has been removed from the tool surface. embedding/sqlite-vec remain supported for semantic recall, section recall, and workspace aliasing, but no longer feed a conflict scanner.

The model is not bundled with the default PyPI/uvx package. Install the local runtime extra and point it at a GGUF file:

pip install "memory-arbiter-mcp[semantic-local]"   # pulls llama-cpp-python

Then set semantic_conflict.model_path (or MEMORY_ARBITER_SEMANTIC_CONFLICT_MODEL_PATH) and enable semantic_conflict.enabled. The feature is off by default. Processing is local-only; no memory content leaves the machine unless a remote backend is explicitly configured in a future version.

Optional: Semantic Recall

By default, Memory Arbiter uses lexical recall: FTS5 trigram + BM25 + soft rerank. This is local, lightweight, and enough for many projects.

For meaning-based recall, enable sqlite-vec and bring your own embedding model. The built-in automatic path supports local GGUF models through llama-cpp-python; remote embedding APIs can also be used by your own scripts through memory_repair(task="resync_vectors") (or the legacy memory_store_embedding under legacy_full).

pip install memory-arbiter-mcp[vec]
pip install llama-cpp-python

Recommended local model: embeddinggemma-300m-qat-Q8_0.gguf (768 dimensions). Configure it in ~/.config/memory-arbiter/config.json, restart the MCP server, then backfill existing memories with docs/semantic_example.py if you are using a source checkout.

Semantic candidates receive a floor score below strong subject/tag matches. They help find meaning-equivalent memories without letting fuzzy vector matches override precise labels.

Tag scoring and filters

Tags are treated as discrete labels, not as a sentence. A memory tagged v0.7.2 and release should outrank a subject that only incidentally contains one query word.

memory(action="find") supports (forwarded to the low-level search):

• tags_filter: strict AND over tags;

• after_time / before_time: ingest-time bounds;

• source_type: source filter;

• has_more and total_estimate: signals that results may continue.

Use whitespace between mixed ASCII/CJK tokens, such as "v0.7.2 发版", so token matching works as intended.

Workspace isolation: none / weak / strict

By default, workspace is a stored label and does not filter recall. If you need project isolation, set isolation.

Under strict, by-id/detail paths (read, history, conflict detail, judgments, audit, and explicit-workspace mutation helpers) also use the caller workspace and may return forbidden_strict_workspace / not-found style responses when the record is outside that workspace.

Use weak when unsure. strict trades recallability for isolation: a wrong workspace can make memories silently unrecallable.

Workspace aliases are canonicalized by embedding similarity. The default cosine cutoff is 0.25.

Optional: Long-document Section Split

Long memories create two problems: search may miss the relevant paragraph, and even successful recall may return the whole document.

Section split breaks long documents into searchable sections. Queries can return only the matched sections while preserving the original memory.

memory(action="remember", data={"content": long_doc})
  → saves original content first
  → if vec ready and content > split.threshold:
      - Markdown headings that fit limits → async rule-based split
      - otherwise → split_request for agent-side continuation

memory(action="find", data={"query": "query"})
  → matched sections when section search is confident
  → full memory when section coverage is high or no section match is available

memory(action="read", data={"memory_id": id, "sections": "catalog" | "all"})
  → inspect or fetch section bodies

Section split is bound to vector readiness. There is no separate on/off switch in v0.8.0+. Short notes stay unsplit and pay no cost.

Configuration

Configuration is read from MEMORY_ARBITER_CONFIG, then ~/.config/memory-arbiter/config.json, then environment variables/defaults. Durable database, vector, and model settings belong in the config file; per-client identity usually belongs in the MCP env block.

Storage and access

Search tuning

Structured conflicts

Workspace isolation

Semantic recall

Long-document split

Per-client environment

Data migration

Moving to a new machine is just copying the SQLite database and reinstalling the package:

scp ~/.local/share/memory-arbiter/memory.sqlite3 newmachine:~/.local/share/memory-arbiter/

python3.11 -m venv .venv
source .venv/bin/activate
pip install -e .

Doctor: health diagnostics

When search feels wrong, embeddings may be misconfigured, or the database might be degraded, run:

mema doctor
mema doctor --json
mema doctor --deep
mema doctor --db PATH

Doctor is read-only and runs outside the MCP server, so it can diagnose even when the MCP process is down. It checks config integrity, vector enablement, split state, claim indexing, data consistency, capacity, conflict backlog, and update-check state. Exit codes are script-friendly: 0 clean, 1 warnings, 2 critical findings.

Testing

python3.11 -m pip install -r requirements.txt
python3.11 -m pytest

License

Apache License 2.0. Copyright (c) 2026 张志维 (billy12151).

Memory Arbiter version 0.8.2 and later are offered under Apache-2.0 going forward. Prior MIT grants remain valid for copies previously distributed under MIT, including 0.8.0 and 0.8.1. Versions before 0.8.2 were released under MIT.

Related MCP server: memory-mcp

中文

memory-arbiter 是 AI Agent 的本地可信事实层。中文名：迷码。短称 / CLI alias：mema。

它可以作为共享记忆层使用，但真正价值不是“把记忆放到同一个地方”，而是事实治理：让长期项目上下文变得可检索、可追溯、可信度可区分、冲突可发现，并且可以安全召回。

共享记忆让每个工具都能看到同一份数据。memory-arbiter 进一步帮助 agent 判断：哪些事实是当前的，哪些是用户确认的，哪些已经过期，哪些互相矛盾，哪些已被废弃，哪些还需要裁决。

# 不用每轮把 2 万 token 的 MEMORY.md 塞进 prompt：
memory(action="find", data={"query": "认证迁移方案"})  → 3 条精准结果，约 400 token

共享记忆只是起点，事实治理才是护城河。

默认完全本地：一个 SQLite 数据库，不需要 Postgres、Redis、托管 memory 服务，也不在 server 内调用大模型。可选语义召回使用你自己的本地 GGUF embedding 模型；可选更新检查可以关闭。

它解决什么问题

很多 memory 工具解决的是“怎么让 agent 记住”。memory-arbiter 关注的是共享之后更难的问题。

当 Claude Code、Cursor、Codex、ZCode、WorkBuddy、OpenClaw 或其他工具都能写入同一份长期上下文时，真正的风险不再只是“忘记”，而是：

• 旧事实和新决策混在一起；

• AI 猜测被当成用户确认事实；

• 不同工具写入互相矛盾的结论；

• 长期项目历史越来越长，真正相关的事实被噪音淹没；

• 切换工具时，不是上下文丢失，就是重复加载、重复污染；

• 本地记忆增长到每轮 prompt 都先消耗几千到几万无关 token。

memory-arbiter 把这些风险变成显式的数据结构：来源标签、可信度、事实时间、写入时间、版本历史、废弃链、冲突记录、结构化 claim 门禁、分段索引、workspace 边界和 doctor 体检。

模型仍然负责语义理解。arbiter 负责把输入侧变干净。

memory-arbiter 做什么

日常心智模型

大多数 agent 只需要四个产品工具（默认 MCP 工具面）：

1. memory —— 日常操作：remember 写新事实、find 搜活跃事实、read 按 ID 取记忆、update 更新已有 current 记忆、judge 提交冲突判断、status 看运行状态。不确定字段时用 action=help。

2. memory_review —— 只读审计：overview、doctor、conflicts、conflict_detail、judgments、history、expired、audit、entities。

3. memory_govern —— 用户授权治理：整条记忆过期、关闭冲突、确认记忆、纠正 judgment。不要用于普通更新。

4. memory_repair —— 维护修复：分段、重建 claims/embeddings、清理、向量状态同步、entity 回灌、pending 激活。优先 dry-run。

低层工具实现仍保留在代码库内并由产品工具复用，但默认不暴露它们的 schema。设置 MEMORY_ARBITER_TOOL_PROFILE=legacy_full（或 full）可同时暴露低层工具。

和其他 memory 的区别

memory-arbiter 不靠“别人不能共享，我们能共享”来做差异化。shared memory 正在成为标准能力。memory-arbiter 关注的是 shared memory 之后更深一层的问题。

memory-arbiter 有轻量图关系信号：事实时间、写入时间、entity/scope、冲突边、废弃链、分段和 workspace 边界。但它不把产品定位成重型图数据库；原文事实是主资产，派生索引用来辅助治理。

省 token 是副作用

核心价值是上下文质量更高。省 token 是最直观的结果。

同一个模型，输入更干净，输出更准。

一个工具能用，多个工具更有价值

只用一个工具时，memory-arbiter 把本地记忆从扁平文件升级成带可信度、历史、冲突信号和诊断的可查询事实层。

多个工具一起用时，它同时成为共享记忆：工具 A 写，工具 B 搜，工具 C 审计。零文件传递，零复制粘贴，零版本漂移。

示例管线：

1. OpenClaw 用 memory(action="remember") 写入规格。

2. OpenDesign 用 memory(action="find") 读取规格，并写回设计决策。

3. ZCode 一次搜索拿到规格和设计决策。

三个工具，一层本地事实层。

完整跨工具示例见 docs/INTEGRATION.md。

核心能力

• 精准召回 —— 返回相关条目，而不是每轮加载完整 memory 文件。

• 可信度分层 —— 区分用户确认、文档提取、AI 生成和未知来源。

• 时间历史 —— 跟踪事实时间、写入时间、版本、历史快照和废弃链。

• 冲突仲裁 —— 发现、记录、查看、关闭或废弃矛盾记忆。

• 结构化 claim 门禁 —— v0.9 在写入/编辑时检测确定性 claim 冲突，使用前要求宿主 LLM 判断。

• 长文档分段 —— 把长记忆拆成可搜索段落，返回命中段而不是整篇。

• workspace 隔离 —— 支持 none、weak、strict 三档和别名归一。

• tag 精排与过滤 —— tag 是离散标签信号，不是弱文本片段。

• 语义召回 —— 可选本地 GGUF embedding；默认仍是轻量字面检索。

• doctor 体检 —— 只读检查配置、向量链、分段、claims、一致性、容量和冲突。

• 逐级降级 —— sqlite-vec → FTS5 → LIKE → JSONL 备份，缺可选组件也继续工作。

• 本地优先 —— 纯 SQLite，无托管数据库，无 Redis/Postgres 要求，无 server 侧 LLM 依赖。

它不是什么： memory-arbiter 不是 LLM，也不替代你的 AI 客户端。它是模型下面的一层结构化存储、检索、仲裁和诊断工具。

快速开始

要求： Python 3.11+（3.11、3.12、3.13 均可）。

# 克隆
git clone https://github.com/billy12151/memory-arbiter-mcp.git
cd memory-arbiter-mcp

# 安装 —— 用任意 Python 3.11+
python3.11 -m venv .venv
source .venv/bin/activate
pip install -e .

# 可选：启用 sqlite-vec 语义召回
pip install -e '.[vec]'

# 启动（短命令）
mema

# 兼容长命令仍可用：
# memory-arbiter
# memory-arbiter-mcp

用 uvx 零安装启动

只想跑起来、不想管理 Python 环境时，先安装 uv，然后：

uvx --from memory-arbiter-mcp mema

这会拉取已发布包并启动 mema 入口。mema 是 Memory Arbiter / 迷码的短命令；memory-arbiter-mcp 和 memory-arbiter 仍作为兼容长命令保留。uvx 只省安装步骤；如果要启用语义召回，embedding 模型和 sqlite-vec 仍需单独配置。

配置助手

不想手写 config.json 时运行：

mema setup

它会把可用配置写到 ~/.config/memory-arbiter/config.json，检查环境，并打印你还需要执行的命令或模型下载链接。它不会替你运行 pip 或下载模型。

常用参数：--print-config、--no-config、--force。

本地 Console MVP

启动只读本地治理控制台：

mema console

默认打开 http://127.0.0.1:18876。可选参数：

mema console --no-open       # 只启动服务，不自动打开浏览器
mema console --port 18877    # 18876 被占用时换一个端口

Console 默认监听 127.0.0.1，当前版本仅限本地。它是可见性与审计入口，不是记忆编辑器：总览、冲突列表、冲突详情、记忆查看、体检、设置页都只读。UI 可在英文（mema Console）和中文（迷码 Console）间切换；CLI 仍只使用英文 mema alias。

支持面板提供 GitHub 点星、提需求、体验反馈和反馈问题入口，通过预填公开 issue 链接跳转。Console 不会自动上传记忆内容，不保存 GitHub token，也不调用 GitHub API。

边界提醒：不要把 Console 端口暴露到公网。页面会展示本地数据库里的记忆内容。

接入工具

把 memory-arbiter 加进 MCP 配置。本地 venv 方式：

{
  "mcpServers": {
    "memory-arbiter": {
      "command": "/path/to/memory-arbiter-mcp/.venv/bin/memory-arbiter-mcp",
      "env": {
        "MEMORY_ARBITER_CLIENT": "zcode",
        "MEMORY_ARBITER_AGENT_ID": "zcode-default"
      }
    }
  }
}

或使用 uvx：

{
  "mcpServers": {
    "memory-arbiter": {
      "command": "uvx",
      "args": ["--from", "memory-arbiter-mcp", "memory-arbiter"],
      "env": {
        "MEMORY_ARBITER_CLIENT": "zcode",
        "MEMORY_ARBITER_AGENT_ID": "zcode-default"
      }
    }
  }
}

每个工具设置不同的 MEMORY_ARBITER_CLIENT（如 openclaw、zcode、codex、cursor、claude-code、workbuddy）。共享数据库、向量、模型配置放 ~/.config/memory-arbiter/config.json；每客户端身份放 MCP env 段。

需要新建会话： MCP server 在会话启动时加载。已经打开的会话不会看到新加的工具。

Agent onboarding guide

Agent 应持久化到本地 agent 记忆文件的短规则见本地 memory_arbiter/AGENT_ONBOARDING.md，也可查看 GitHub 版本：https://github.com/billy12151/memory-arbiter-mcp/blob/main/memory_arbiter/AGENT_ONBOARDING.md。Agent 也可以通过 memory(action="help", data={"topic": "agent_onboarding"}) 读取同一份指南。

客户端配置位置

OpenDesign 和 OpenClaw GUI 工具运行在宿主 CLI 之上，会继承宿主客户端已经加载的 MCP server。

MCP 工具

v0.11.0 起默认 MCP 工具面改为任务型接口。新客户端默认只看到 4 个产品工具，而不是原来的低层工具长列表：

低层工具实现仍保留在 Memory Arbiter 内部，并由上述产品工具复用，但默认不再把它们的 schema 暴露给 Agent。这样可以减少常驻 MCP 工具 token，也让日常路径更容易选择。

高级兼容：设置 MEMORY_ARBITER_TOOL_PROFILE=legacy_full（或 full）可同时暴露旧的低层 MCP 工具面。

可选：写入时语义冲突检测

memory-arbiter 可以可选地在写入后运行本地 Qwen2.5-0.5B 模型，生成语义冲突 notice——一种可审阅的轻量提示，表示新写入的记忆可能与某条已有记忆在语义上冲突。模型只做候选信号，不做最终裁决；是否变成可见 notice 由 pair 文本 gate 决定（默认 medium 平衡召回，strong 更低打扰、更高置信）。

链路：metadata-overlap 粗召回（subject + tags）→ 0.5B pair 分类 → pair 文本 gate → semantic_notices 行。notice 通过 memory_repair(task="notice", ...) 查看/关闭，运行时控制走 memory_repair(task="semantic_control", ...)。这是当前唯一的自动冲突候选来源：旧的向量冲突候选 scan 已从工具面移除。embedding/sqlite-vec 仍用于语义召回、分段召回和 workspace alias，但不再喂给任何冲突扫描器。

模型不随默认 PyPI/uvx 包内置。安装本地运行 extra 并指向一个 GGUF 文件：

pip install "memory-arbiter-mcp[semantic-local]"   # 拉取 llama-cpp-python

再设置 semantic_conflict.model_path（或 MEMORY_ARBITER_SEMANTIC_CONFLICT_MODEL_PATH）并开启 semantic_conflict.enabled。该功能默认关闭。处理完全本地，除非未来版本显式配置远程后端，否则记忆内容不会离开本机。

可选：语义召回

默认使用字面召回：FTS5 trigram + BM25 + 软重排。它完全本地、轻量，对很多项目已经够用。

需要“按意思找”时，启用 sqlite-vec 并自带 embedding 模型。内置自动路径支持通过 llama-cpp-python 使用本地 GGUF；远程 embedding API 也可以由你自己的脚本调用，再通过 memory_repair(task="resync_vectors") 写入（或在 legacy_full 下用 memory_store_embedding）。

pip install memory-arbiter-mcp[vec]
pip install llama-cpp-python

推荐本地模型：embeddinggemma-300m-qat-Q8_0.gguf（768 维）。把它配置到 ~/.config/memory-arbiter/config.json，重启 MCP server；源码 checkout 用户可以用 docs/semantic_example.py 给旧记忆补向量。

语义候选会拿到低于强标题/tag 命中的保底分。它帮助召回语义相近内容，但不会让模糊向量结果压过精确标签。

Tag 评分与过滤

tag 被当作离散标签，而不是一句普通文本。带有 v0.7.2 和 release tag 的记忆，应该胜过 subject 只是偶然含一个 query 词的记录。

memory(action="find") 支持（转发到低层 search）：

• tags_filter：严格 AND；

• after_time / before_time：按 ingest_time 过滤；

• source_type：按来源过滤；

• has_more 和 total_estimate：提示结果是否还有更多。

中英混合或版本号+CJK 查询建议用空格分词，例如 "v0.7.2 发版"。

Workspace 隔离：none / weak / strict

默认 workspace 只是存储标签，不过滤召回。需要项目隔离时设置 isolation。

在 strict 下，按 ID/detail 路径（read、history、conflict detail、judgments、audit，以及显式传 workspace 的变更工具）也使用 caller workspace；记录不属于该 workspace 时会返回 forbidden_strict_workspace 或 not-found 风格结果。

不确定时用 weak。strict 是用召回性换隔离性：workspace 传错会让记忆静默不可召回。

workspace 别名通过 embedding 相似度归一，默认余弦阈值是 0.25。

可选：长文档分段

长记忆有两个问题：搜索可能找不到相关段落；即使找到了，也可能把整篇文档都返回给模型。

分段会把长文拆成可检索 section。查询可以只返回命中段，同时保留原始完整记忆。

memory(action="remember", data={"content": long_doc})
  → 先保存原文
  → vec ready 且内容 > split.threshold 时：
      - Markdown 标题符合限制 → 后台规则分段
      - 否则 → 返回 split_request 给 agent 续接

memory(action="find", data={"query": "query"})
  → section 匹配有把握时返回命中段
  → 覆盖率高或没有 section 匹配时返回完整 memory

memory(action="read", data={"memory_id": id, "sections": "catalog" | "all"})
  → 查看或获取 section 正文

分段能力绑定向量 readiness。v0.8.0 起没有单独开关。短笔记不会触发分段，也不会产生额外成本。

配置

配置读取顺序：MEMORY_ARBITER_CONFIG → ~/.config/memory-arbiter/config.json → 环境变量/default。数据库、向量、模型等耐久配置建议放配置文件；每客户端身份通常放 MCP env 段。

存储与访问

检索调优

结构化冲突

Workspace 隔离

语义召回

长文档分段

每客户端环境变量

数据迁移

换机器只需要复制 SQLite 数据库并重新安装包：

scp ~/.local/share/memory-arbiter/memory.sqlite3 新机器:~/.local/share/memory-arbiter/

python3.11 -m venv .venv
source .venv/bin/activate
pip install -e .

Doctor：健康体检

当搜索不对、embedding 可能配置错、或数据库疑似降级时运行：

mema doctor
mema doctor --json
mema doctor --deep
mema doctor --db PATH

doctor 只读，并且在 MCP server 外运行，所以 MCP 进程挂了也能诊断。它检查配置完整性、向量启用链、分段状态、claim 索引、数据一致性、容量、冲突积压和更新检查状态。退出码适合脚本：0 正常，1 有 warning，2 有 critical。

测试

python3.11 -m pip install -r requirements.txt
python3.11 -m pytest

License

Apache License 2.0。版权所有 (c) 2026 张志维 (billy12151)。

Memory Arbiter 0.8.2 及后续版本从现在起按 Apache-2.0 授权；此前已经按 MIT 分发的副本，其既有 MIT 授权继续有效，包括 0.8.0 和 0.8.1。0.8.2 之前版本按 MIT 发布。