
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
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
在 SourceWeft 中
- 打开 控制台中的 repo-memory-mcp,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
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.
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.
Start the MCP server directly:
Or run it without a permanent installation:
The server is local and communicates over stdio. It does not need an API key.
Configure an MCP client
The common stdio configuration is:
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:
Available MCP tools
CLI
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
Discovery and publishing
- Project site: repo-memory-mcp.negoshades.workers.dev
- Documentation: quickstart and tools
- Client guides: Claude, Cursor, VS Code, Cline, and Windsurf
- Extensions: VS Code and Bing-compatible browser companion
- Contributions: open-source contribution guide
- Source: GitHub
- Package: PyPI
- Registry metadata:
server.json - Publishing guide:
PUBLISHING.md
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/vscodeprovides local setup commands for VS Code.extensions/bingprovides 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版本历史
1- v0.2.0最新Sep 25, 2026
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