Memtether

io.github.lanbass869-cellv0.1.0a16更新于 Oct 3, 2026

Cross-client AI memory hub: agents share one SQLite memory.db via file-level pointers.

已验证STDIO仅桌面DatabasesKnowledge & Memory

概览

AI 生成的概览

为助手提供共享的本地 SQLite 记忆库，让多个 AI 客户端读写同一份记忆。

功能
MemTether 是一个本地记忆中枢，把记忆保存在一个 SQLite 文件（memory.db）中，供多个 AI 客户端共同指向。它支持记住、搜索、更正和停用记忆，并通过向量检索、FTS5 全文、字面匹配和实体图的多路召回进行查找。它还提供来源归属、双时间轴 as-of 查询、以取代而非删除的方式管理旧记忆、基于使用量的排序，以及可选的 MEMORY.md 投影用于上下文注入。
适用场景
当你在多个 AI 编程客户端之间切换、希望上下文能够延续而不是每次切换都丢失时，适合使用。它也适合希望记忆完全保存在本机、而非云端服务的用户。
运行要求
作为本地进程运行，可从 PyPI 安装（pip install memtether）或从源码安装，语义检索和 REST 服务为可选附加组件。需要 Python 和本地 SQLite 数据库文件；可选环境变量 MEM_DB 和 MEM_DEFAULT_SOURCE 用于覆盖数据库路径和默认来源归属。未声明需要账号或 API 密钥。
安装前请注意
它会读写一个被多个客户端共享的本地记忆数据库，因此某个客户端写入的记忆对其他客户端可见。connect 命令会修改客户端配置文件，README 指出某些深度自定义配置无法安全重写，建议先预览变更。语义检索依赖可选依赖，缺少时会退化为关键词搜索。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Memtether，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

Quick Start

mcp-name: io.github.lanbass869-cell/memtether

bash
# 1. Installpip install memtether
# 2. Initialize (creates a demo memory DB)memtether init
# 3. Connect all detected AI clients (Claude Code, Cursor, Windsurf, etc.)memtether connect --all

Try it:

bash
memtether search "shared memory"memtether remember "my first shared memory"memtether stats

Or on Windows, double-click install.bat for one-click setup.

More install options
bash
# From sourcegit clone https://github.com/MemTether/MemTether.git && cd MemTether && pip install -e .
# With semantic search (local embedding, no cloud)pip install "memtether[vector]"
# With REST API serverpip install "memtether[server]"
# Dockerdocker build -t memtether . && docker run -p 8420:8420 -v ./data:/app/data memtetherdocker-compose up

Try it without installing:

bash
python scripts/make_demo_db.pyMEM_DB=~/.memtether/demo.db python mem.py search "shared memory"

Why MemTether

The problem

You use Claude Code for coding, Cursor for refactoring, and Windsurf for exploration. Each has its own memory. Switch tools and your AI forgets everything.

MemTether's answer is simpler than you'd expect: make them all point to the same file.

How it is different

Common approachProblemMemTether approach
Per-client memorySwitch tools, lose contextFile-level pointer: all clients read/write same memory.db
Cloud-hosted memoryPrivacy + API fees + downtimeLocal-first: SQLite on your machine, zero cloud
Delete old memoriesCannot trace what was knownSupersession: old memories marked, never deleted
Single time axisCannot distinguish when true vs when learnedBi-temporal: dual T/T-prime axes with as-of queries
Equal treatment of memoriesUseful memories get buriedQ-Value: used memories rank higher
Single search pathMisses keyword matches4-path recall: vector + FTS5 + literal + entity graph
No concurrent protectionSimultaneous writes = data lossHubguard: file lock + atomic write

Feature comparison

FeatureMemTethermem0cogneezep
Local-firstYesNo (cloud)YesNo (cloud)
Cross-client sharedYes (23)NoNoNo
Source attributionYesNoNoYes
Bi-temporalYesNoNoYes
Q-Value rankingYesNoNoNo
SupersessionYesNoNoYes
4-factor re-rankingYesNoNoNo
ScaffoldsYesNoNoNo
MCP serverYesYesYesYes
Eval suite includedYesYesNoNo
No API key neededYesNoNoNo
REST APIYesYesYesYes
DockerYesYesYesYes
Full feature list
  • Cross-client shared memory: 23 adapters (Claude Code, Cursor, Windsurf, VS Code, Zed, JetBrains, Cline, Roo Code, Kilo Code, Continue, Cody, Amazon Q, Gemini CLI, Neovim, Claude Desktop, Codex, WorkBuddy CN/Intl, CodeBuddy, ZCode, DSH, OpenClaw, Agents-Neutral)
  • Source attribution: every memory knows which client wrote it
  • Bi-temporal: T (when true) + T-prime (when recorded), as-of queries
  • Supersession: old memories marked superseded, never deleted, full audit trail
  • Q-Value: usage-based ranking (0.3 + 0.7 x q_value multiplier)
  • 4-factor re-ranking: semantic (0.45) + recency (0.25) + frequency (0.05) + importance (0.10), blended 70/30 with RRF
  • Deterministic scaffolds: counting/temporal/comparison/aggregation prepended to top result
  • Consolidation index: 2708 topics + 315 chains + 37 standing instructions as bonus recall
  • FTS5 triggers: SQLite-level full-text sync (INSERT/DELETE/UPDATE triggers)
  • Hubguard: cross-process concurrent write lock + atomic write + format fallback
  • Conflict detection: 89 quantified conflict patterns
  • LLM auto-extraction: extract structured memories from conversation text
  • Projection: auto-generates MEMORY.md (3980 char budget) for context injection
  • Multi-path search: vector + FTS5 BM25 + literal + entity graph PPR, RRF fused
  • Three-layer dedup: supersession-aware, content exact, tag-signature
  • Low-confidence rejection: marks results when keyword empty AND vector < 0.50
  • TTL expiry: expired conclusions downweighted with annotation
  • Self-reference suppression: meta-discussion ranked below answers

Architecture

+---------+   +---------+   +---------+   +---------+|  Claude |   | Cursor  |   |Windsurf |   | VS Code |  ... 23 adapters|  Code   |   |         |   |         |   |         |+----+----+   +----+----+   +----+----+   +----+----+     |              |              |              |     +--------------+------+-------+--------------+                          |                   +------v------+                   |  MemTether  |                   | Memory Hub  |                   |             |                   | SQLite      |  <- one physical memory.db                   | FTS5        |  <- full-text search (triggers)                   | ChromaDB    |  <- vector search (bge-m3)                   | Bi-temporal |  <- T + T-prime dual time axes                   | Q-Value     |  <- usage-based ranking                   | Hubguard    |  <- concurrent write lock                   +-------------+
Tech stack
ComponentTechnologyPurpose
DatabaseSQLite (WAL mode)Single-file, zero-config
Full-textFTS5 trigram + triggersO(1) BM25, SQL-level sync
VectorChromaDB + bge-m3 (1024-dim)Semantic search, local
FusionReciprocal Rank Fusion (K=60)Merge multi-path results
Re-ranking4-factor (sem .45 + rec .25 + freq .05 + imp .10)Z-score + sigmoid
GovernanceSupersession + bi-temporal + conflictNever delete
ConcurrencyHubguard (file lock + atomic write)Cross-process safe
APIFastAPI REST + MCP serverAny language

Connect Your Clients

bash
python -m memtether connect --allpython -m memtether verifypython -m memtether detectpython -m memtether selftest

Supported (23): Claude Code, Cursor, Windsurf, VS Code, Zed, JetBrains, Cline, Roo Code, Kilo Code, Continue, Cody, Amazon Q, Gemini CLI, Neovim, Claude Desktop, Codex, WorkBuddy (CN + Intl), CodeBuddy, ZCode, DSH, OpenClaw, Agents-Neutral

Memory Operations

bash
python -m memtether remember "User prefers dark theme" --source claude-codepython -m memtether search "theme preference"python -m memtether correct <uid> "Updated text"python -m memtether retire <uid> "No longer relevant"python -m memtether statspython -m memtether as-of 2026-09-15 --kind knownpython -m memtether rebuild

Benchmarks

LongMemEval (500 questions, full run, 2026-10-02)

MetricScoreNotes
Strict match (global)62.6%209/334 applicable
LLM judge (global)54.6%263/482
Multi-session strict48.8%Above industry avg 27.9%
Multi-session LLM judge60.0%
E-Hybrid73.3%11/15 (small sample)
Per-type breakdown
TypenStrictLLM Judge
knowledge-update7876.6%64.4%
multi-session13348.8%58.4%
single-session-assistant5640.4%42.9%
single-session-preference30N/A33.3%
single-session-user7086.4%80.0%
temporal-reasoning13360.7%41.4%

Results use our own harness. Not directly comparable with mem0 reported numbers.

Test suite

TestResult
hard_bench62/62
asset_bench23/23
pytest31/31
e2e_verify13/13
refuse_bench26/26
concurrent_stress4/4 PASS

Known Limitations

  1. tether_connect detect may falsely report "not connected" for same-source-different-path configs. Use verify for accurate results.
  2. DSH cordis.patch.yml deep customizations cannot be safely rewritten. Use plan to preview.
  3. memory_hub (production) and memtether (open source) are two copies. Changes need directional sync.
  4. Semantic search requires optional deps (chromadb, onnxruntime). Without them, degrades to keyword search.
  5. Windows-first. macOS/Linux should work but not fully tested.

Relationship to Other Projects

  • mem0: managed memory with cloud API. MemTether is for people who want everything local.
  • cognee: knowledge graph + pipeline. MemTether is lightweight operational memory (SQLite, no Neo4j).
  • letta (MemGPT): agent framework. MemTether works with existing agents you already use.
  • engram: Go + SQLite + FTS5 + MCP. MemTether adds bi-temporal, source attribution, Q-Value, 23 adapters.

You can use MemTether alongside any of these.

License

Apache 2.0 - see LICENSE

Contributing

Issues and PRs welcome. See CONTRIBUTING.md.

Star History

[Star History Chart]

中文文档 | Security | Changelog

来源：README.md，提交 3852976

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版本历史

1
  1. v0.1.0a16最新Oct 3, 2026