
Repomemory
io.github.DanielGuru由 GitHub 帳號 DanielGuru 發布v1.3.0更新於 Oct 24, 2026
Persistent memory for AI coding agents. Hybrid search, auto-session capture, context routing.
發布方自己的服務:RepoMemory
概覽
為 AI 程式開發代理提供持久、可搜尋的專案記憶,透過工具對儲存庫的 .context 知識庫進行搜尋、讀取、寫入與刪除。
- 可以做什麼
- 搜尋與定位:在儲存庫與全域內容中進行關鍵字加語意的混合搜尋,並一次回傳索引、偏好與近期工作階段的定位資訊。
- 讀取與瀏覽:讀取完整條目,優先儲存庫、再回退全域,並依來源標籤列出條目。
- 寫入與維護:依作用域路由記錄事實、決策、回歸問題與偏好,並刪除過時知識。
- 風險評估:針對整個程式碼庫或指定檔案回報熱點、隱藏耦合與巴士因子。
- 工作階段與歷史:關閉時自動儲存工作階段摘要,並可將 git 歷史同步為變更日誌。
- 適用情境
- 適合在同一個程式碼庫上跨多次 AI 工作階段時使用,讓代理不再重複摸索架構、重複爭論已定決策或重新引入已修復的錯誤。也適合希望共享可搜尋專案知識、而非依賴靜態說明檔案的團隊。
- 執行需求
- 透過 npx 在本機執行(需要 Node.js)。安裝指令會向 Claude Code、Cursor、Copilot、Windsurf、Cline、Aider、Continue 等工具寫入 MCP 設定與規則。AI 分析需要模型供應商金鑰,如 ANTHROPIC_API_KEY、OPENAI_API_KEY、GEMINI_API_KEY 或 GROK_API_KEY;Cursor 方式可不用外部金鑰。選用的嵌入使用 Gemini 或 OpenAI 金鑰。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Repomemory,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
repomemory
Your codebase never forgets.
AI agents lose context every session. repomemory fixes that — one command creates a persistent, searchable knowledge base that any AI tool can read, search, and write to.
The Problem
Every time you open a project with Claude Code, Cursor, Copilot, or any AI coding agent:
- It re-discovers your architecture from scratch
- It proposes changes that were already debated and rejected
- It re-introduces bugs that were already fixed
Your CLAUDE.md / .cursorrules helps, but it's static and gets stale.
The Solution
Facts tell agents how things work. Decisions prevent re-debating. Regressions prevent re-breaking. Preferences teach agents how you code.
Quick Start
With an API key (Claude Code, terminal workflows)
One command: sets up global profile, creates .context/, configures Claude Code + Cursor, runs AI analysis, prints CLAUDE.md instructions.
With Cursor (no API key needed)
This installs everything Cursor needs:
- MCP server in
~/.cursor/mcp.json(auto-starts repomemory in every project) - Rules in
.cursor/rules/repomemory.mdc(teaches Cursor's AI to use context) - 6 commands in
.cursor/commands/(run with/in Cursor chat)
Then in Cursor chat, type:
Cursor's own AI scans your repo and populates .context/ via the MCP tools. No external API key required — your Cursor subscription handles it.
Available Cursor commands:
Guided wizard
Walks through provider selection, tool integration, and first analysis. If no API keys are detected, offers a "None — I use Cursor" option that skips external analysis entirely.
Non-interactive (CI-safe)
No prompts when --yes / --defaults / --no-prompt is used.
MCP Server — Agents With Real Memory
The real power is the MCP server. When configured via repomemory setup claude, it auto-starts with Claude Code and gives agents 7 tools:
Sessions are auto-captured on shutdown. Zero config — repomemory setup claude handles everything.
Supported Tools
Cursor users: You don't need any API key. Run
npx repomemory setup cursor, then use/repomemory-analyzein Cursor chat. Cursor's AI does the analysis using the MCP tools — whatever model Cursor is using (it can even switch models mid-task).
Supported Providers
Embeddings (optional, for semantic search): Gemini text-embedding-004 (free, default) or OpenAI text-embedding-3-small. Auto-detected from available API keys.
All Commands
Configuration
.repomemory.json in your repo root (all fields optional):
Custom ignorePatterns and keyFilePatterns are additive — they extend built-in defaults, not replace them.
Why Not Just CLAUDE.md?
repomemory doesn't replace CLAUDE.md — it complements it. CLAUDE.md is for instructions and rules. .context/ holds the knowledge that grows over time.
Contributing
See CONTRIBUTING.md for development setup, testing, and contribution guidelines.
License
MIT
Built for developers who are tired of AI agents forgetting everything between sessions.
來源:README.md,提交 b5b9d25
工具
0版本歷史
1- v1.3.0最新Sep 16, 2026
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