repo-memory-mcp

io.github.Umarjaum由 GitHub 帳號 Umarjaum 發布v0.2.0更新於 Oct 24, 2026

Persistent local memory for MCP-compatible AI coding agents, stored per repository in SQLite.

發布方自己的服務:Memory.fit

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

一個本機 MCP 伺服器,以 SQLite 為編碼代理提供依儲存庫隔離的持久記憶,讓修正、規則與決策跨工作階段保留。

可以做什麼
載入啟動情境,並以本機評分搜尋作用中的記憶,可依類型或狀態篩選,列出專案規則或依類型、狀態篩選記憶,儲存新的經驗教訓、長期規則與架構決策,就地更新既有記憶或封存過時記憶,在同一儲存庫的安全副本之間匯出與匯入經驗證的記憶。
適用情境
適用於編碼代理在不同工作階段反覆犯相同錯誤,而你希望把儲存庫專屬的慣例、限制與決策保存在本機、不依賴雲端服務或嵌入模型的情況。
執行需求
以 Python 套件形式透過 stdio 在本機執行(PyPI 發行名稱為 repo-memory-mcp-ai),可用 pip 安裝或以 uvx 直接執行;不需要 API 金鑰、雲端帳號或遠端資料庫。記憶存放在所偵測儲存庫內的 .repo-memory/memory.db。
安裝前請注意
何時記憶與回想由代理自行決定,不會自動擷取對話。預設會拒絕疑似金鑰,但仍不要向記憶工具提交密碼、權杖、私鑰或憑證。儲存、更新與封存記憶會寫入本機資料庫,匯入會覆寫或合併記憶,請保留備份。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 repo-memory-mcp,將其新增到工作區。
  2. 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

repo-memory-mcp

mcp-name: io.github.Umarjaum/repo-memory-mcp

Persistent memory for AI coding agents

AI coding agents are powerful, but they forget repository-specific corrections, conventions, constraints, and architectural decisions. repo-memory-mcp gives MCP-compatible coding agents a persistent, local, repository-aware memory layer.

Teach your coding agent once. Let your repository remember.

[Tests] [PyPI] [License]

What it does

MCP hosts decide when tools are called; MCP does not automatically intercept every conversation. This project exposes explicit tools that let an agent load important context at session startup, search relevant memories, store corrections and decisions, update stale guidance, and archive obsolete memories.

The memory database lives in .repo-memory/memory.db inside the detected repository. It uses SQLite, deterministic local ranking, and no cloud service.

Why developers use it

  • Persistent project memory: corrections, rules, conventions, constraints, decisions, and bug-fix lessons survive sessions.
  • Repository-aware isolation: unrelated repositories do not share memory.
  • Local-first privacy: no API keys, remote database, external embeddings, telemetry, or background upload.
  • Explicit MCP workflow: the agent chooses when to remember and recall; there is no claim of automatic conversation interception.
  • Fast and inspectable: SQLite storage, JSON export, CLI diagnostics, and human-readable records.
  • Security-minded: likely API keys, bearer tokens, passwords, cloud credentials, and private keys are rejected by default.

Install and use in two minutes

The public PyPI distribution is named repo-memory-mcp-ai because the shorter repo-memory-mcp name is already owned by another project. The MCP server and GitHub project identity remain repo-memory-mcp.

bash
pip install repo-memory-mcp-airepo-memory doctor

Start the MCP server directly:

bash
repo-memory-mcp

Or run it without a permanent installation:

bash
uvx --from repo-memory-mcp-ai repo-memory-mcp

The server is local and communicates over stdio. It does not need an API key.

Configure an MCP client

The common stdio configuration is:

json
{  "mcpServers": {    "repo-memory": {      "command": "uvx",      "args": ["--from", "repo-memory-mcp-ai", "repo-memory-mcp"]    }  }}

Use the client-specific configuration location and format documented by your MCP host. Ready-to-copy examples are in examples/:

The project is vendor-neutral and is intended for Claude Code, Cursor, Cline, Windsurf, and other MCP-compatible coding agents. Compatibility depends on the host’s MCP support and configuration format.

Recommended agent workflow

At the beginning of a coding session, call get_startup_context(). When the developer corrects a repeated mistake, call remember_correction(topic, lesson). When a relevant question arises, call recall_memory(query). When guidance becomes stale, call update_memory() or archive it with forget_memory().

Example instruction to an agent:

text
At the start of this session, call get_startup_context. If I correct a repeated mistake or establish a durable project rule, store it with the appropriate repo-memory tool. Do not store credentials or transient chat context.

Available MCP tools

ToolUse it for
get_startup_contextLoad concise, high-value repository guidance at session start
remember_correctionStore a lesson learned from a correction or bug fix
remember_ruleStore a durable project rule or convention
remember_decisionStore an architectural decision and rationale
recall_memorySearch active memory with deterministic local scoring
list_project_rulesList active project rules
list_memoriesFilter memories by type or status
update_memoryCorrect an existing memory without creating a replacement
forget_memoryArchive obsolete memory for audit-friendly retention
export_memory / import_memoryMove validated memory between safe copies of the same repository

CLI

bash
repo-memory initrepo-memory listrepo-memory rulesrepo-memory search "database migrations"repo-memory export ./memory-backup.jsonrepo-memory import ./memory-backup.jsonrepo-memory doctor

Privacy and security boundaries

Memories stay on the machine running the server. This implementation does not require API keys, cloud accounts, remote databases, external embeddings, or telemetry. It only inspects text explicitly submitted to memory tools. Your operating system, package manager, backups, and MCP host may have separate behavior.

Likely secrets are rejected by default. Do not submit passwords, tokens, private keys, or credentials to memory tools. The .repo-memory/ directory is ignored by Git so private memory is not accidentally committed.

Development

bash
git clone https://github.com/Umarjaum/repo-memory-mcp.gitcd repo-memory-mcppython -m venv .venv. .venv/bin/activatepip install -e '.[dev]'pytest

Discovery and publishing

Developer

Muhammad Umar Jabbar — Khanewal, Punjab, Pakistan. Personal website: umarjaum.netlify.app.

Roadmap

Future work may improve ranking, add confidence feedback, provide optional local embeddings, generate project context, add a memory inspection UI, and publish the companion extensions to official marketplaces. Team/shared memory remains explicitly opt-in.

Companion extensions

  • extensions/vscode provides local setup commands for VS Code.
  • extensions/bing provides a privacy-safe Manifest V3 context-menu helper for turning selected Bing research into a repo-memory prompt.

The browser companion intentionally does not invoke the local stdio MCP process. Direct browser-to-MCP integration would require a separately reviewed native-messaging bridge.

License

MIT. See LICENSE.

來源:README.md,提交 4e3e3d5

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.2.0最新Sep 25, 2026

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