
Rememb
io.github.LuizEduPP由 GitHub 账号 LuizEduPP 发布v0.3.5更新于 Oct 24, 2026
Persistent memory for AI agents — local JSON, zero config, no server required.
概览
面向 AI 智能体的本地优先长期记忆:以 JSON 或 SQLite 存于磁盘,支持关键词搜索、版本管理、差异对比、恢复与审计记录。
- 可以做什么
- 写入、编辑、软删除与恢复记忆条目,按 project、actions、systems、requests、user、context 等分区组织
- 按关键词与词元搜索条目,再按 id 读取完整内容
- 查看版本历史、并排差异与变更审计记录
- 会话开始时注入近期条目、标签与统计,并把工具调用后与结束事件记录为交接条目
- 在本地网页界面浏览记忆、统计、设置与随包附带的智能体技能
- 适用场景
- 适合智能体在会话之间反复丢失项目事实、决策与偏好的场景,希望拥有可检查的持久记忆,而不依赖托管记忆服务或 API 密钥。适合每天使用智能体的个人开发者与团队,需要可复制、可审计、可回滚的本地存储。
- 运行要求
- 以本地进程方式从 PyPI 安装(pip install rememb),需要 Python 3.11 至 3.14。无需账号、API 密钥或云服务。MCP 服务器通过 rememb mcp 命令以 stdio 启动,也可选在本机指定主机与端口以 SSE 方式运行。存储会在 ~/.rememb 自动初始化,并可为单个项目建立独立存储。
安装
在 SourceWeft 中
- 打开 控制台中的 Rememb,将其添加到工作区。
- 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
[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
来源:README.md,提交 ab6f4dc
工具
0版本历史
1- v0.3.5最新Sep 16, 2026
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