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 Memory Arbiter goes further: it helps agents tell which facts are current, user-confirmed, stale, conflicting, superseded, or still waiting for judgment.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

Need	Why ordinary memory is not enough	Memory Arbiter's answer
Targeted recall	A flat MEMORY.md or large vector blob returns too much context.	memory(action="find") returns a small set of relevant, ranked entries instead of loading full files.
Source trust	User-confirmed facts, document extracts, and AI guesses look the same.	source_type, confidence, user_confirmed, and locked records make trust visible.
Time and evolution	Old decisions stay next to new decisions, and the model may follow the stale one.	event_time, ingest_time, version, history (memory_review), and supersede (memory_govern) preserve the evolution chain.
Conflicts	Two memories can disagree and both still be retrieved.	Conflict scan, conflict records, conflict signals, and explicit resolve/supersede tools make disagreement visible.
Write-time safety	Last-write-wins silently overwrites or piles up contradictory facts.	v0.9 structured claim gates detect deterministic claim collisions and require host-LLM judgment before use.
Long documents	The relevant paragraph is buried inside a 10K+ character memory.	Section split returns the matched sections instead of forcing the model to scan the whole document.
Project boundaries	Global memory can leak facts across unrelated projects.	Workspace isolation supports none, weak, and strict modes with alias canonicalization.
Long-running health	Users only notice memory problems after bad answers.	doctor reports config, vector readiness, split health, consistency, capacity, and conflict buildup.
Privacy and ownership	Hosted memory adds another service and another data boundary.	Local SQLite, user-owned files, optional local embeddings, no built-in LLM dependency.

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.

Compared with	Memory Arbiter focuses on
Plain markdown memory	Targeted recall instead of full prompt loading, plus history and conflict state.
Vector memory	Not just similar recall, but source trust, stale/superseded state, and conflict-aware recall.
Graph memory	Not just what is connected, but what is current, trusted, conflicting, or safe to use.
Hosted memory	Local SQLite, caller-owned policy, no hosted database, and no server-side LLM calls.
Generic MCP memory	A fact-governance layer: trust labels, time evolution, structured claim gates, doctor, and repair tools.

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.

Scenario	Full-file loading	With Memory Arbiter	Saving
Per-turn memory load	5K–20K tokens in system prompt	200–800 tokens via memory(action="find")	~80%+
Conflict detection	LLM compares pairs with large context	Structured candidates + focused judgment	~90%
Periodic audit	LLM scans the whole library	memory_review(conflicts) + memory_review(audit)	~70%
Spec handoff	Re-load full spec/design notes	Query the relevant facts and decisions	~80%+

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 instruction: what to write where

If your client also keeps local markdown such as MEMORY.md, AGENTS.md, or tool-specific notes, paste this rule into your agent instructions:

Local markdown files store only self-use information: rules, tool experience,
config notes, and agent persona. Anything that might be reused by another agent
or platform — requirements, research, decisions, progress, user preferences,
knowledge conclusions — goes into memory-arbiter.

Every write must fill: subject, tags, source_type (`user_confirmed`,
`agent_generated`, or `document_extracted`), event_time (ISO 8601), workspace,
and source_ref. Use `user_confirmed` only for facts the user explicitly verified;
it auto-locks the record.

`mema` is the short name for Memory Arbiter. In Chinese contexts, it can also be
called **迷码**. Treat user phrases such as "search mema", "check mema", "write
this to mema", "remember this in mema", "mema 查记忆", "迷码查一下", or "写到迷码"
as requests to use memory-arbiter tools, not as references to a local file. Map
search/read/update/save requests through the task-oriented `memory` tool:
`action=find`, `action=read`, `action=update`, or `action=remember`.

Use `memory(action="find")` before reading source files. When the user says a new
source-of-truth document replaces an older current document, find/read the
existing memory and call `memory(action="update")`; do not create a second active
current memory. Only use `memory_govern(action="retire")` when the user explicitly
asks to retire a whole memory. If a response returns
action_required=judge_conflict_before_use, call `memory(action="judge")` with the
included snapshot pins before using the conflicting claim.

When a todo is complete, remove the `todo` tag with
`memory(action="update", data={"memory_id": id, "tags_only": true,
"remove_tags": ["todo"]})`. Do not only write a new "done" memory, or the old
todo remains active and can mislead future searches.

Client config locations

Client	Config location
ZCode	~/.zcode/v2/ MCP config
Codex CLI	~/.codex/ MCP config
Claude Code	.mcp.json in project root
Cursor	~/.cursor/mcp.json
WorkBuddy	~/.workbuddy/mcp.json
OpenClaw	~/.openclaw/openclaw.json MCP config

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:

Tool	Description
memory	Daily memory operations: remember, find, read, update, submit conflict judgment, and status. Use action=help for command-specific fields.
memory_review	Read-only inspection: overview, doctor, conflicts, conflict detail, judgments, history, expired memories, audit, and entities.
memory_govern	Explicit user-authorized governance: retire a whole memory, resolve a conflict, confirm a memory, or correct a judgment. Do not use for ordinary updates.
memory_repair	Maintenance and repair: split, rebuild claims/embeddings, cleanup, vector resync, entity backfill, and pending activation. Prefer dry-run first.

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: 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.

Level	Write workspace	Search without workspace	Search with workspace	New workspace
none (default)	optional	full library	ignored	silent
weak	recommended	full library	same workspace boosted, cross-workspace demoted	write_hints.new_workspace_detected
strict	required	error	hard filter to canonical workspace	written as pending until memory_repair(task="activate_pending")

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

JSON path	Env fallback	Default	Use
db_path	MEMORY_ARBITER_DB_PATH	./memory_arbiter.sqlite3	Shared SQLite path.
backup_jsonl	MEMORY_ARBITER_BACKUP_JSONL	./memory_arbiter.backup.jsonl	Append-only backup when SQLite is read-only.
policy_path	MEMORY_ARBITER_POLICY	none	Optional JSON policy file.

Search tuning

JSON path	Env fallback	Default	Use
recall_pool_cap	MEMORY_ARBITER_RECALL_POOL_CAP	50	Raise to 100–200 when stores exceed ~100 entries.
content_like_cap	MEMORY_ARBITER_CONTENT_LIKE_CAP	30	Raise when many same-topic memories exist.

Structured conflicts

JSON path	Env fallback	Default	Use
structured_claim_mode	MEMORY_ARBITER_STRUCTURED_CLAIM_MODE	beta_all	Set off only as an emergency kill switch.

Workspace isolation

JSON path	Env fallback	Default	Use
isolation	MEMORY_ARBITER_ISOLATION	none	none, weak, or strict.
workspace_match_distance	MEMORY_ARBITER_WORKSPACE_MATCH_DISTANCE	0.25	Cosine cutoff for workspace alias merge.

Semantic recall

JSON path	Env fallback	Default	Use
vec.enabled	MEMORY_ARBITER_ENABLE_SQLITE_VEC	false	Enable sqlite-vec semantic recall.
vec.dim	MEMORY_ARBITER_VEC_DIM	768	Must match the embedding model.
embedding.provider	MEMORY_ARBITER_EMBEDDING_PROVIDER	inferred from model path	gguf for built-in local auto-embedding.
embedding.model_path	MEMORY_ARBITER_EMBEDDING_MODEL_PATH	none	GGUF embedding model path.
embedding.auto_query	MEMORY_ARBITER_EMBEDDING_AUTO_QUERY	true	Auto-encode plain-text queries.
embedding.auto_write	MEMORY_ARBITER_EMBEDDING_AUTO_WRITE	true	Auto-embed writes/edits.

Long-document split

JSON path	Env fallback	Default	Use
split.threshold	MEMORY_ARBITER_SPLIT_THRESHOLD	4000	Minimum character count to trigger split.
split.section_vec_distance_threshold	MEMORY_ARBITER_SECTION_VEC_DISTANCE_THRESHOLD	0.42	Section vector cutoff; recalibrate if switching models.
split.section_fulltext_threshold	MEMORY_ARBITER_SECTION_FULLTEXT_THRESHOLD	0.8	Return full text when enough sections match.
split.max_sections	MEMORY_ARBITER_MAX_SECTIONS	50	Max sections per memory.
split.max_section_chars	MEMORY_ARBITER_MAX_SECTION_CHARS	3600	Max characters per section slice.

Per-client environment

Variable	Default	Use
MEMORY_ARBITER_CLIENT	codex	Tool identity.
MEMORY_ARBITER_AGENT_ID	default	Agent identity inside the client.
MEMORY_ARBITER_WORKSPACE	default	Workspace label; isolation only applies when configured.
MEMORY_ARBITER_CONFIG	none	Alternate JSON config path.
MEMORY_ARBITER_RANKING_MODE	hybrid	hybrid or legacy bm25.
MEMORY_ARBITER_GGUF	none	Legacy GGUF path fallback; prefer config file.

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.

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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

需求	普通记忆为什么不够	memory-arbiter 的回答
精准召回	扁平 MEMORY.md 或大块向量记忆容易返回过多上下文。	memory(action="find") 只返回少量相关、排序后的条目，而不是加载全文。
来源可信度	用户确认、文档提取、AI 猜测看起来一样。	source_type、confidence、user_confirmed 和 locked 记录让可信度可见。
时间演进	旧决策和新决策并存，模型可能跟着旧口径走。	event_time、ingest_time、version、history（memory_review）、supersede（memory_govern）保留演进链。
冲突处理	两条记忆可以互相矛盾，却同时被召回。	冲突扫描、冲突记录、冲突信号、resolve/supersede 工具让矛盾可见、可处理。
写入安全	last-write-wins 会静默覆盖，或继续堆积矛盾事实。	v0.9 结构化 claim 门禁检测确定性事实碰撞，使用前要求宿主 LLM 判断。
长文档	相关段落埋在 10K+ 字符的长记忆里。	分段索引返回命中段落，而不是让模型扫整篇文档。
项目边界	全局记忆容易把无关项目事实串在一起。	workspace 隔离支持 none、weak、strict 三档，并做别名归一。
长期健康	用户往往等到回答变差才发现记忆库有问题。	doctor 检查配置、向量链、分段、claims、一致性、容量和冲突积压。
隐私和所有权	托管 memory 又引入一个服务和数据边界。	本地 SQLite、用户自有文件、可选本地 embedding、无内置 LLM 依赖。

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。

对比对象	memory-arbiter 关注什么
普通 markdown memory	不全文加载 prompt，而是精准召回，并保留历史和冲突状态。
向量 memory	不只找相似内容，还要知道来源可信度、过期状态、废弃状态和冲突状态。
图 memory	不只知道什么和什么有关，还要知道什么是当前的、可信的、冲突的、可安全使用的。
托管 memory	本地 SQLite、调用方自有策略、无托管数据库、无 server 侧 LLM 调用。
通用 MCP memory	事实治理层：可信度标签、时间演进、结构化 claim 门禁、doctor 和修复工具。

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。

场景	全文加载	使用 memory-arbiter	节省
每轮记忆加载	system prompt 塞 5K–20K tokens	memory(action="find") 返回 200–800 tokens	~80%+
冲突检测	LLM 带大上下文逐条比较	结构化候选 + 聚焦判断	~90%
定期审查	LLM 扫全库	memory_review(conflicts) + memory_review(audit)	~70%
规格交接	重复加载完整规格/设计记录	查询相关事实和决策	~80%+

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 指令：什么写到哪里

如果你的客户端也维护本地 markdown，例如 MEMORY.md、AGENTS.md 或工具自己的笔记，把下面这段贴进 agent 指令：

本地 markdown 只存自用信息：规则、工具经验、配置说明、agent persona。
凡是可能被其他 agent 或平台复用的信息——需求、调研、决策、进展、
用户偏好、知识结论——都写入 memory-arbiter。

每次写入必填：subject、tags、source_type（user_confirmed、agent_generated、
document_extracted）、event_time（ISO 8601）、workspace、source_ref。
user_confirmed 只用于用户明确确认过的事实；它会自动锁定记录。

`mema` 是 memory-arbiter 的短称，中文语境也可叫 **迷码**。用户说“mema 查记忆”、
“查一下 mema”、“迷码查一下”、“写到 mema”、“写到迷码”、“mema 记一下”、
“remember this in mema”等，都应理解为使用 memory-arbiter 工具，而不是引用
某个本地文件。查询/读取/更新/保存请求统一走任务型 `memory` 工具：
`action=find`、`action=read`、`action=update`、`action=remember`。

先 `memory(action="find")`，再读源文件补细节。用户说“以后以新文档为准”或
“替换当前文档”时，先 find/read 找到已有 current 记忆，再 `memory(action="update")`；
不要新增第二条 active current 记忆。只有用户明确要求“整条旧记忆过期/废弃”时，
才使用 `memory_govern(action="retire")`。如果响应返回
action_required=judge_conflict_before_use，用 `memory(action="judge")` 提交随响应返回的
snapshot pins 后，再使用冲突 claim。

待办完成后，用 `memory(action="update", data={"memory_id": id, "tags_only": true,
"remove_tags": ["todo"]})` 移除 todo tag。不要只写一条新的“已完成”记忆，否则旧 todo 仍保持 active，会误导后续检索。

客户端配置位置

客户端	配置位置
ZCode	~/.zcode/v2/ MCP 配置
Codex CLI	~/.codex/ MCP 配置
Claude Code	项目根目录 .mcp.json
Cursor	~/.cursor/mcp.json
WorkBuddy	~/.workbuddy/mcp.json
OpenClaw	~/.openclaw/openclaw.json MCP 配置

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：

工具	说明
memory	日常记忆操作：remember、find、read、update、judge、status。需要参数示例时用 action=help。
memory_review	只读审计：overview、doctor、conflicts、conflict_detail、judgments、history、expired、audit、entities。
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。
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 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：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。

档位	写入 workspace	不传 workspace 搜索	传 workspace 搜索	新 workspace
none（默认）	可选	全库	忽略	静默
weak	建议	全库	同 workspace 加权、跨 workspace 降权	write_hints.new_workspace_detected
strict	必填	报错	硬过滤到 canonical workspace	写为 pending，直到 memory_repair(task="activate_pending")

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

JSON 路径	env 兜底	默认值	用途
db_path	MEMORY_ARBITER_DB_PATH	./memory_arbiter.sqlite3	共享 SQLite 路径。
backup_jsonl	MEMORY_ARBITER_BACKUP_JSONL	./memory_arbiter.backup.jsonl	SQLite 只读时的追加备份。
policy_path	MEMORY_ARBITER_POLICY	无	可选策略 JSON。

检索调优

JSON 路径	env 兜底	默认值	用途
recall_pool_cap	MEMORY_ARBITER_RECALL_POOL_CAP	50	记忆超过约 100 条后可调到 100–200。
content_like_cap	MEMORY_ARBITER_CONTENT_LIKE_CAP	30	同主题记忆很多时调大。

结构化冲突

JSON 路径	env 兜底	默认值	用途
structured_claim_mode	MEMORY_ARBITER_STRUCTURED_CLAIM_MODE	beta_all	仅紧急熔断时设为 off。

Workspace 隔离

JSON 路径	env 兜底	默认值	用途
isolation	MEMORY_ARBITER_ISOLATION	none	none、weak 或 strict。
workspace_match_distance	MEMORY_ARBITER_WORKSPACE_MATCH_DISTANCE	0.25	workspace 别名合并的余弦阈值。

语义召回

JSON 路径	env 兜底	默认值	用途
vec.enabled	MEMORY_ARBITER_ENABLE_SQLITE_VEC	false	启用 sqlite-vec 语义召回。
vec.dim	MEMORY_ARBITER_VEC_DIM	768	必须和 embedding 模型一致。
embedding.provider	MEMORY_ARBITER_EMBEDDING_PROVIDER	从模型路径推断	内置本地自动 embedding 使用 gguf。
embedding.model_path	MEMORY_ARBITER_EMBEDDING_MODEL_PATH	无	GGUF embedding 模型路径。
embedding.auto_query	MEMORY_ARBITER_EMBEDDING_AUTO_QUERY	true	自动向量化纯文本查询。
embedding.auto_write	MEMORY_ARBITER_EMBEDDING_AUTO_WRITE	true	写入/编辑时自动灌向量。

长文档分段

JSON 路径	env 兜底	默认值	用途
split.threshold	MEMORY_ARBITER_SPLIT_THRESHOLD	4000	触发分段的最小字符数。
split.section_vec_distance_threshold	MEMORY_ARBITER_SECTION_VEC_DISTANCE_THRESHOLD	0.42	section 向量距离阈值；换模型需重校准。
split.section_fulltext_threshold	MEMORY_ARBITER_SECTION_FULLTEXT_THRESHOLD	0.8	命中段落占比达到多少时返回全文。
split.max_sections	MEMORY_ARBITER_MAX_SECTIONS	50	每条记忆最大 section 数。
split.max_section_chars	MEMORY_ARBITER_MAX_SECTION_CHARS	3600	每个 section 切片最大字符数。

每客户端环境变量

变量	默认值	用途
MEMORY_ARBITER_CLIENT	codex	工具身份。
MEMORY_ARBITER_AGENT_ID	default	客户端内 agent 身份。
MEMORY_ARBITER_WORKSPACE	default	workspace 标签；只有配置 isolation 后才影响召回。
MEMORY_ARBITER_CONFIG	无	指定另一个 JSON 配置文件。
MEMORY_ARBITER_RANKING_MODE	hybrid	hybrid 或 legacy bm25。
MEMORY_ARBITER_GGUF	无	旧 GGUF 路径兜底；建议改用配置文件。

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。