
Rememb
io.github.LuizEduPP由 GitHub 帳號 LuizEduPP 發布v0.3.5更新於 Oct 24, 2026
Persistent memory for AI agents — local JSON, zero config, no server required.
概覽
為 AI 代理提供本地優先的長期記憶:以 JSON 或 SQLite 存於磁碟,支援關鍵字搜尋、版本管理、差異比對、還原與稽核紀錄。
- 可以做什麼
- 寫入、編輯、軟刪除與還原記憶條目,依 project、actions、systems、requests、user、context 等分區整理
- 以關鍵字與詞元搜尋條目,再依 id 讀取完整內容
- 檢視版本歷程、並排差異與變更稽核紀錄
- 工作階段開始時注入近期條目、標籤與統計,並把工具呼叫後與結束事件記錄為交接條目
- 在本地網頁介面瀏覽記憶、統計、設定與隨套件附帶的代理技能
- 適用情境
- 適合代理在工作階段之間反覆遺失專案事實、決策與偏好的情境,希望擁有可檢視的持久記憶,而不依賴託管記憶服務或 API 金鑰。適合每天使用代理的個人開發者與團隊,需要可複製、可稽核、可回復的本地儲存。
- 執行需求
- 以本地程序方式從 PyPI 安裝(pip install rememb),需要 Python 3.11 至 3.14。無需帳號、API 金鑰或雲端服務。MCP 伺服器透過 rememb mcp 指令以 stdio 啟動,也可選在本機指定主機與連接埠以 SSE 方式執行。儲存會於 ~/.rememb 自動初始化,並可為單一專案建立獨立儲存。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Rememb,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
[Rememb MCP server] [MCP Badge]
Operate AI agents without losing context between sessions. rememb is local-first long-term memory: structured entries, keyword search, versioning, diff, restore, and audit trail — no cloud, no API keys.
[rememb chat demo]
The problem
Teams using agents at real velocity rarely fail because they lack generation. They fail because operating agents every day creates context debt:
- too much re-explaining project facts every session
- too little durable memory outside the chat window
- too little audit trail for why something changed
- too much noise when recalling the right context
Every team or solo developer operating agents professionally hits this wall:
Existing solutions often center on hosted memory layers, API keys, or opaque context pipelines. What you actually need is to resume the next session with the minimum correct context and a trail you can inspect.
rememb is built around four memory problems:
- durable facts and decisions instead of session-only chat memory
- keyword search instead of rereading everything (agents judge relevance)
- non-destructive versioning instead of silent overwrites
- local-first audit trail for AI work, not opaque cloud logs
Install
Quick Start
With MCP (recommended)
1. Install the door, or add the MCP block by hand:
That writes the MCP server, Memory Rules, and SessionStart / PostToolUse / Stop hooks when the host has them (Cursor and Claude today). Without --apply it only prints the plan. --host limits the target; omit it to write every host rememb install knows (cursor, claude, codex, vscode, copilot, windsurf, continue, gemini, opencode, zed, cline).
Or add this to your IDE's MCP config and paste the rules from MCP_TOOLS.md:
Add repeatable --project /path/to/repo args for per-project stores alongside global ~/.rememb. Details: MCP_TOOLS.md.
2. Restart your IDE.
SessionStart injects recent entries, tags, and a short stats block. The agent writes durable memory when something changes, and searches only when targeted recall is needed.
No extra storage setup is required. In MCP mode, rememb auto-initializes ~/.rememb and optional --project stores on first use. Public tools: MCP_TOOLS.md (18 tools).
If you want multiple MCP clients on the same machine to reuse one already-running rememb process, start a persistent local SSE transport:
This keeps one MCP process alive, so repeated clients can connect through http://127.0.0.1:8765/sse and http://127.0.0.1:8765/messages/.
Do not put --transport sse inside a stdio MCP client config. stdio clients expect JSON-RPC on stdin/stdout; the SSE mode exposes an HTTP endpoint and must be started separately.
Local usage without MCP
How it works
A local store on disk. Your agent can read prior decisions, search by keywords and tokens, update entries without losing history, and restore previous versions without depending on a cloud memory service. Copy ~/.rememb/ anywhere to move the store.
Writes, edits, and deletes go through rememb_write, rememb_edit, and rememb_delete. Host hooks also write into actions via rememb hook post-tool / stop. For the full MCP surface, see MCP_TOOLS.md.
Search uses keyword and token matching over entry content and tags. rememb_search returns id, score, tags, date, and a short excerpt (search_excerpt_chars, default 160). Full body is rememb_get / rememb_read on the ids the agent picked, or max_chars=0 on search. The agent applies semantic relevance judgment. No API keys, no cloud, no embedding model download at runtime.
config.json is written during initialization with all supported knobs:
Set storage_backend to sqlite for larger stores. The Web UI and MCP migrate existing JSON entries automatically when you switch backends.
entry_batch_size and entry_load_threshold control pagination in the web UI — how many cards load at once and when to trigger "load more".
Section names are normalized to lowercase, duplicates are ignored after normalization, and removing a section with existing entries automatically migrates those entries to uncategorized. meta.json is kept in sync with the current effective section list.
Older stores may still contain legacy embedding-related config keys; they are dropped the next time configuration is loaded or saved.
Memory sections
Web UI
rememb includes a local web interface for supervision — browse memory, inspect history, and tune runtime settings. The SPA has no create/edit/delete controls. The HTTP API still has write, delete, and restore routes.
Overview with entry totals and recent memory activity.
Stats with totals, section breakdown, date range, and recent entries.
Settings for limits, storage backend, section colors, and maintenance actions.
Skills browser for bundled agent skills included with rememb.
Views:
- Overview — entry totals, deleted count, store size, and recent memory
- Memory — browse, keyword search, filter by section, sort, and include deleted entries
- Stats — totals, backend, section bars, oldest/newest timestamps, and recent entries
- Settings — edit limits, storage backend, section colors, consolidate duplicates, and save runtime config
- Skills — browse bundled agent skills (61 skills shipped in the package)
Entry inspection from the UI includes version history and side-by-side diff. Entry mutations and restore are MCP (rememb_write, rememb_edit, rememb_delete, rememb_restore) or the HTTP API, not SPA controls.
CLI
Compatibility
The current compatibility surface is tracked explicitly in COMPATIBILITY.md.
Short version:
- Python 3.11 to 3.14 are covered by CI
- CLI contract and MCP tool schema (18 tools) have automated test coverage
- stdio MCP is the primary documented integration path
- SSE MCP is documented and partially tested at the route level
- release automation and Trusted Publishing are documented in RELEASE.md
Design
- Local first — plain JSON or SQLite on disk
- Portable — copy
~/.rememb/anywhere, it works - Agnostic — any agent, any IDE (MCP or CLI)
- No lock-in — no servers, no API keys, no accounts
Core capabilities:
- structured memory with sections and tags
- keyword search with agent-side relevance judgment (
rememb_searchis an index: id, score, excerpt; full body via get/read) - host door: SessionStart injects recent + tags + stats; PostToolUse / Stop write into
actions; Stop tagshandoffso the next host sees it first rememb installwrites MCP, Memory Rules, and hooks (hooks for Cursor and Claude)- non-destructive versioning, diff, restore, and soft delete
- duplicate consolidation (exact content) and store stats
- config and maintenance via Web UI (settings and consolidate; entry mutations via MCP or HTTP API)
- 61 bundled agent skills via Web UI and MCP
Contributing
PRs welcome. Issues welcome. Stars welcome. 🌟
License
MIT
來源:README.md,提交 ab6f4dc
工具
0版本歷史
1- v0.3.5最新Sep 16, 2026
評分
已滿足適用檢查項的 50%
該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。
- 近 30 天內可連通25 分
請保持端點能夠回應 MCP 握手;SourceWeft 每 30 天探測一次。
- 已宣告認證方式10 分
請在註冊中心條目中宣告憑證請求標頭,或保持端點能夠回應探測,以便判定其認證方式。
- 已列出工具15 分
請讓伺服器在無憑證時回應 tools/list,或發布可在沙箱中無憑證啟動的軟體包。
- 描述必需15 分
- 圖示10 分
- 文件15 分
- 授權條款10 分