Th Memory Mcp

io.github.worakorn-prince由 GitHub 账号 worakorn-prince 发布v2.4.3更新于 Nov 8, 2026

Local long-term memory MCP server (SQLite) for AI coding agents — OpenCode, Claude Code, Cursor

发布方自己的服务:Memory.fit

已验证STDIO仅桌面知识与记忆开发者工具

概览

AI 生成的概览

一个基于本地 SQLite 的长期记忆服务器,让编程智能体跨会话记住偏好、经验教训和过往交互。

可以做什么
  • 保存并更新偏好、经验教训和个人档案,并按关键词或混合检索召回
  • 在令牌预算内获取与任务相关的上下文,可选记忆图谱扩展
  • 搜索过往提示词和近期交互,并从中提取或提炼记忆候选
  • 管理记忆生命周期:关联、合并、更新、取代、整合和删除单条记录
  • 查看统计信息,并以 JSON 文件导出或导入记忆
适用场景
适合希望编程智能体在会话之间延续偏好、纠正和经验习惯、且不依赖任何云服务的场景。最适用于单台机器上的单个用户,想要一个私有、基于文件的记忆库并在多个支持 MCP 的工具间共享。
运行要求
通过 npm 包 th-memory-mcp 以 stdio 在本地运行,需要 Node.js(仅限已修补的 22、24 或 26 版本,Node 20 不受支持)以及用于安装和构建的 npm。无需账号、API 密钥或外部服务。需设置 MEMORY_DB_PATH 指向 SQLite 文件,服务器与自动捕获插件必须使用同一路径。
安装前请注意
数据库是未加密的普通 SQLite 文件,任何能访问文件系统的人都能读取,敏感数据请依赖全盘加密。没有身份验证或授权:调用方提供的用户、会话和项目 ID 会被直接信任,因此不要将同一服务器或数据库共享给不受信任的用户。工具可以删除、合并、取代和导入记录,导入和提取仅在明确确认后才会写入。请使用已修补的 Node.js 版本,因为 npm audit 不覆盖 Node 核心漏洞。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Th Memory Mcp,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

th-memory-mcp

Requirements

  • Node.js 22.23.4+, 24.22.1+, or 26.12.1+ — patched releases only; the server uses Node-only APIs (better-sqlite3 native build, import.meta.url). Node.js 20 is EOL and unsupported. CI tests the minimum patched version of each supported line (22/24/26).
  • npm — for npm install, npm run build, npm test. No external services, accounts, or API keys — everything lives in a single local SQLite file.
  • OpenCode — the host that loads the MCP server and the auto-capture plugin (any build with MCP over stdio + plugins). The plugin uses node:sqlite on OpenCode Desktop/Node 22.5+ and falls back to bun:sqlite on the Bun-based CLI.
  • OS: Windows / macOS / Linux — cross-platform (Node); Windows: set MEMORY_DB_PATH with setx, macOS/Linux: export in your shell profile.

Security: Use a patched Node.js release — check your runtime with npx is-my-node-vulnerable. npm audit covers npm dependencies only, not Node.js core CVEs.

Audit: Independently smoke-tested by AddonAudit on v2.4.1 — an automated test, not a security guarantee; the one high advisory it found was fixed in 2.4.2, and fresh installs from 2.4.3 onward report 0 critical/high advisories.

Quick Start

Fastest path: npm run quickstart (after cloning) builds, wires opencode.json/opencode.jsonc, deploys the plugin, and sets MEMORY_DB_PATH — the steps below are the manual equivalent. npm alternative: npm install -g th-memory-mcp (or npx th-memory-mcp) and point mcp.command to th-memory-mcp instead of dist/index.js (plugin still from this repo; not on GitHub Packages anymore).

bash
# 1. Clone and buildgit clone https://github.com/worakorn-prince/th-memory-mcp.gitcd th-memory-mcp && npm install && npm run build# 2. Share one DB between the server and the pluginsetx MEMORY_DB_PATH "$PWD/data/memory.db"      # Windows (PowerShell)# export MEMORY_DB_PATH="$PWD/data/memory.db"  # macOS/Linux — add to your shell profile (~/.zshrc)
  1. Merge this into ~/.config/opencode/opencode.json (replace <REPO> with the absolute clone path; ready-made block in opencode.example.json). MEMORY_DB_PATH must be the SAME file for server and plugin — a system/user-level env var covers both; environment below covers the server only. Example rules: AGENTS.memory.example.md (attachable at project level).
json
{ "instructions": ["<REPO>/AGENTS.memory.example.md"],  "mcp": { "memory": { "type": "local", "command": ["node", "<REPO>/dist/index.js"], "enabled": true, "environment": { "MEMORY_DB_PATH": "<REPO>/data/memory.db" } } } }
  1. (Optional) Auto-capture: deploy the dual-mode plugin to a directory (OpenCode V2 only auto-discovers local plugins from a folder) — src/plugin/learning-capture.ts → ~/.config/opencode/plugins/learning-capture/index.ts
  2. Restart OpenCode (config loads at startup only), then try: "Remember that I prefer pnpm" → new session → "What package manager do I prefer?"

The plugin works in both modes: OpenCode V2 calls the default export's id + setup(ctx) (domain hooks for prompt/tool/compaction/failure events), while V1 (1.18.29+) calls the same default export's server(), which returns the legacy hook object. One file, no runtime import of @opencode/plugin (types only), so it loads under both Node and Bun.

If startup reports Failed to load plugin ... Received protocol 'bun:' or the plugin is silently ignored on V2, you are on an older install layout. Re-deploy the current src/plugin/learning-capture.ts to ~/.config/opencode/plugins/learning-capture/index.ts; a legacy ~/.config/opencode/plugins/learning-capture.ts is renamed to .bak-<timestamp> by npm run quickstart (never deleted). It loads node:sqlite first and only falls back to bun:sqlite under Bun.

Architecture

OpenCode loads the plugin learning-capture (dual-mode V1/V2, Node/Bun) — auto-captures prompt/tool/error into the DB, injects the profile on compaction — and the MCP server th-memory-mcp (Node stdio) exposing the 16 tools on that same SQLite DB; global rules (memory-protocol.md) teach the AI to use them.

Why th-memory-mcp?

  • Context-based learning, not fine-tuning — captures your preferences, corrections, and habits, then recalls them next session; no data leaves your machine.
  • 100% local & private — one SQLite file, no cloud, no external API; secrets (api_key=, password:, token) filtered before storage.
  • Low overhead & resilient — tool calls are capped (latency < 10 ms, bounded output) and degrade gracefully if the DB is unavailable.
  • Open & extensible — MIT licensed; 16 documented tools, rule-based distill, auto-capture plugin; Node + better-sqlite3 only (no extra native extensions).

Highlights

  • Structured memory — preferences with confidence scoring plus lesson records (situation → mistake → correction), not just flat facts.
  • Lifecycle, temporal & conflict-aware — states (active/stale/superseded/archived), confidence/importance/salience scoring, per-type decay, validity intervals for point-in-time truth and supersession chains; duplicate/contradiction resolution keeps both sides of ambiguous evidence.
  • Hybrid retrieval — get_context blends FTS5 keyword search with dependency-free lexical fuzzy matching (hashed n-gram similarity, 512-dim FNV-1a) via RRF fusion, then assembles a token-budgeted context with optional memory-graph expansion.
  • Consolidation — clusters similar memories into derived ones with full provenance (derived_from links).
  • First-class Thai / i18n — Thai-aware tokenization in distill; Thai and English accepted interchangeably.

Works with other harnesses

A standard MCP server: 16 tools anywhere MCP-over-stdio works; all harnesses share one SQLite file via MEMORY_DB_PATH. Auto-capture needs a hook runtime (built into OpenCode, bridged for Claude Code).

FeatureOpenCodeClaude CodeQwen CodeCodexCursor
16 MCP tools✅✅✅✅✅
Auto-capture (background)✅ plugin✅ hooks⚠️ adapter❌ manual❌ Rules
Profile injection✅ compaction✅ UserPromptSubmit❌ get_profile❌ get_profile❌ get_profile
Lexical fuzzy matching✅ (v2.0)✅ (v2.0)✅ (v2.0)✅ (v2.0)✅ (v2.0)

Scripts

CommandDescription
npm run buildcompile TypeScript → dist/
npm startrun the MCP server (stdio) from dist/index.js
npm run distillinteractions → profile sections + prune old data (RETENTION_DAYS, default 30)
npm testbuild + full suite (46 files, 148 tests: 147 pass, 1 skip)
node test/<name>.test.mjsrun one suite: capture, distill, lifecycle, temporal, conflict, retrieval, graph, context, consolidation, benchmark, security
node test/smoke.mjsend-to-end smoke test over JSON-RPC (16 tools)

Tools (16)

ToolDescription
rememberupsert preference (category+key); re-save +0.1 confidence (cap 1.0)
recallsearch preferences + lessons (FTS5) + recent matching interactions — run before a new task
get_profileprofile sections + top preferences + 5 most recent lessons
save_lessonrecord a lesson from a correction (situation / mistake / correction)
search_historysearch past prompts by keyword (200-char snippets)
forgetdelete one row (preference/lesson/interaction) by id (+type avoids cross-table id clash)
memory_statscounts by kind, DB size, oldest/newest interaction, profile sections
get_recent_interactionsrecent raw interactions (filter by kind) — Smart Distill feedstock
export_memoryexport to JSON under data/exports/ only (filename auto-sanitized)
get_contexttask-relevant memories via hybrid retrieval (+ graph expansion), token-budgeted
consolidatecluster similar memories via embedding similarity; optionally create derived memories linked via derived_from
link_memorytyped link between two memories: supports/contradicts/supersedes/derived_from/related_to/caused_by/depends_on
merge_memorymerge a duplicate into canonical memory (source superseded; provenance in metadata.merged_from)
update_memoryupdate fields in place, or create a superseding memory when content changes (supersede=false edits in place)
import_memoryimport from JSON (validates type, dedupes, never overwrites blindly); dry-run by default, apply=true to insert
extract_memoriesscan recent interactions for memory-intent phrases, propose candidates (deterministic, no LLM); dry-run by default, apply=true to create (source=captured)

CLI (th-memory)

BinaryEntryPurpose
th-memory-mcpdist/index.jsMCP server (stdio) — the 16 tools above
th-memorydist/cli.jszero-dep local CLI (same DB via MEMORY_DB_PATH)

Usage: th-memory [--db <path>] [--json] [--plain] <command> [options] — per-command help via th-memory <command> --help. highlight/recall --highlight: matches underlined (ESC[4m) on a color TTY, else [mem]…[/mem] markers (piped, --plain, --json).

FlagEffect
--db <path>use this SQLite file (sets MEMORY_DB_PATH)
--jsonprint JSON {ok,data} instead of plain text
--plain / --no-colordisable colors
-h, --help / -V, --versionshow help / print version
CommandUsage
rememberremember --category <c> --key <k> --value <v|-> — save a preference (--value - reads from stdin)
recallrecall <topic> [--limit <n>] [--highlight] — search memory
forgetforget <id> [--type <t>] — delete by id (t: memory|preference|lesson|interaction)
exportexport [--include-interactions] [--filename <n>] — export to data/exports/*.json
importimport (--file <p>|--json <s>) [--apply] [--user-id <id>] — import backup (dry-run by default)
statsstats — memory statistics
profileprofile — distilled user profile
historyhistory [--query <q>] [--limit <n>] — search past prompts (no query = recent prompts)
recentrecent [--limit <n>] [--kind <k>] — recent interactions (k: prompt|tool_call|error)
highlighthighlight [text...] -q <topic> [--limit <n>] — highlight topic matches (empty text = stdin pipe)
bash
th-memory remember --category coding_pref --key package_manager --value pnpmth-memory recall pnpm --limit 5echo "I prefer pnpm for installs" | th-memory highlight -q pnpm

Daily usage

The AI accepts Thai and English interchangeably; long-term care: npm run distill summarizes stats and prunes interactions older than 30 days.

  • "Remember that..." → remember — save a preference
  • "Summarize memory" / "distill memory" → Smart Distill — AI reads get_recent_interactions, finds patterns, saves insights itself
  • "How is my memory?" / "memory status" → memory_stats
  • "Export memory" / "backup memory" → export_memory
  • "Search history..." → search_history · "Forget..." → forget

data/ structure

data/├── memory.db          # SQLite (WAL mode) — main DB (+ .db-wal, .db-shm); path overridable via MEMORY_DB_PATH└── exports/           # JSON files from export_memory (writeable only in this dir)# everything in data/ is git-ignored

Known Limitations

  • No encryption at rest — data/memory.db is plain, unencrypted SQLite (WAL mode, better-sqlite3); "100% local & private" means no cloud/network exfiltration, not encrypted at rest — anyone with filesystem access can read it. For sensitive data: OS full-disk encryption (BitLocker / FileVault / LUKS) or an opt-in SQLCipher build (native rebuild + key management); no in-code encryption by default (src/db/index.ts documents this).
  • Single-user, no auth layer (Batch B-3) — userId / sessionId / projectId are caller-supplied with no authentication or authorization check inside the server; scope isolation (retrieval, get_context, graph, consolidate, link_memory, merge_memory) binds only to caller-sent values, so any scope is readable/writable by passing a different id. Suitable for a single-user local process; do not share one server/DB across untrusted users without an auth layer in front enforcing the correct userId (projectId / sessionId). No auth is built in by design.

License

MIT © 2026 worakorn-prince — see LICENSE for the full text.

来源:README.md,提交 7047183

工具

16

remember

Remember preference

Save or update a user preference (category+key upsert). Re-saving the same key increases confidence by 0.1 (cap 1.0). Returns the row id for forget().

输入参数key必填value必填category必填

recall

Recall memory

Search preferences + lessons via full-text index, plus recent matching interactions. Use before starting a new task.

输入参数limittopic必填

get_profile

Get user profile

Get the distilled user profile: profile sections, top preferences by confidence (max 15), and the 5 most recent lessons. Optional userId/sessionId/projectId scope the auto-projected memories; without them only GLOBAL memories are included.

输入参数userIdprojectIdsessionId

save_lesson

Save lesson

Record a lesson learned from a correction: what situation, what mistake, what is the correct way. Call immediately after the user corrects your work.

输入参数mistake必填situation必填correction必填

search_history

Search prompt history

Search past user prompts (kind='prompt') by keyword. Returns timestamped snippets truncated to 200 chars each.

输入参数limitquery必填sessionId

forget

Forget a memory entry

Delete one memory row (preference, lesson or interaction) by id and sync the search index. Pass type when you know it (ids from remember are preference ids, from save_lesson are lesson ids).

输入参数typetarget_id必填

memory_stats

Memory statistics

Summarize memory usage: interaction counts by kind, preference/lesson totals, DB file size, oldest/newest interaction timestamps, and profile sections.

无需输入参数。

get_recent_interactions

Recent interactions

List recently captured raw interactions (newest first) as [id] ts [kind] content lines. Optionally filter by kind. Use for auditing history or before distilling memory.

输入参数kindlimitsessionId

export_memory

Export memory to JSON

Export preferences, lessons, profile (and optionally raw interactions) to a JSON file under data/exports/. Only writes inside that directory. Returns the file path, size in bytes and a JSON preview.

输入参数filenameincludeInteractions

get_context

Get assembled context

Assemble relevant memories for the current task via hybrid retrieval (+ optional memory-graph expansion), with token budgeting. Use to load memory into context before a task.

输入参数limitqueryuserIdmaxTokensprojectIdsessionIdincludeGraphincludeHistory

consolidate

Consolidate memories

Cluster similar memories via embedding similarity and optionally create derived/consolidated memories linked via 'derived_from'. Use during periodic consolidation.

输入参数deriveuserIdprojectIdsessionIdthresholdminClusterSize

merge_memory

Merge memories

Merge a duplicate/near-duplicate memory into a canonical one. The source becomes superseded and provenance is recorded in metadata.merged_from.

输入参数sourceId必填targetId必填

update_memory

Update a memory

Update mutable fields (summary/importance/confidence/valid_from/valid_until/metadata) in place. If content changes, a superseding memory is created by default (set supersede=false to edit in place).

输入参数id必填contentsummarymetadatasupersedevalidFromconfidenceimportancevalidUntil

import_memory

Import memories

Import memories from a JSON array or a .json file inside data/exports/. Validates type, dedupes against existing memories, and never overwrites active memory blindly. Dry-run by default; pass apply=true to insert.

输入参数filejsonapplyuserIdtrusted

extract_memories

Extract memories from interactions

Scan recent captured interactions for memory-intent phrases and propose memory candidates (deterministic, no LLM). Dry-run by default; pass apply=true to create them (source=captured).

输入参数kindapplylimituserIdsessionId

版本历史

7
  1. v2.4.3最新Nov 7, 2026
  2. v2.4.1Oct 29, 2026
  3. v2.4.0Oct 23, 2026
  4. v2.3.5Oct 21, 2026
  5. v2.3.4Oct 21, 2026
  6. v2.3.2Oct 18, 2026
  7. v2.0.0Sep 16, 2026

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其他 Memory 集成