repo-memory-mcp

io.github.Umarjaum由 GitHub 账号 Umarjaum 发布v0.2.0更新于 Oct 24, 2026

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

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

一个本地 MCP 服务器,用 SQLite 为编码智能体提供按仓库隔离的持久记忆,让纠正、规则和决策跨会话保留。

可以做什么
加载启动上下文,并用本地评分检索活跃记忆,可按类型或状态筛选,列出项目规则或按类型、状态筛选记忆,存储新的经验教训、长期规则和架构决策,就地更新已有记忆或归档过时记忆,在同一仓库的安全副本之间导出和导入经过校验的记忆。
适用场景
适用于编码智能体在不同会话中反复犯同样错误,而你希望把仓库特有的约定、约束和决策保存在本地、不依赖云服务或嵌入模型的场景。
运行要求
以 Python 包形式通过 stdio 在本地运行(PyPI 发行名为 repo-memory-mcp-ai),可用 pip 安装或用 uvx 直接运行;无需 API 密钥、云账户或远程数据库。记忆保存在所检测仓库内的 .repo-memory/memory.db。
安装前请注意
何时记忆和回忆由智能体自行决定,不会自动捕获对话。默认会拒绝疑似密钥,但仍不要向记忆工具提交密码、令牌、私钥或凭据。存储、更新和归档记忆会写入本地数据库,导入会覆盖或合并记忆,请保留备份。

安装

在 SourceWeft 中

  1. 打开 控制台中的 repo-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

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.

来源:README.md,提交 4e3e3d5

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.2.0最新Sep 25, 2026

评分

已满足适用检查项的 50%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通25 分

    请保持端点能够响应 MCP 握手;SourceWeft 每 30 天探测一次。

  • 已声明认证方式10 分

    请在注册中心条目中声明凭据请求头,或保持端点能够响应探测,以便判定其认证方式。

  • 已列出工具15 分

    请让服务器在无凭据时响应 tools/list,或发布可在沙箱中无凭据启动的软件包。

  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分

其他 Memory 集成