
Project Memory
io.github.Dankaro-projects由 GitHub 帳號 Dankaro-projects 發布v0.5.0b9更新於 Oct 24, 2026
Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.
發布方自己的服務:Memory.fit
概覽
把單一專案的計畫、證據、決策、結果與已採納的經驗保存在本機 SQLite 資料庫,助理透過 MCP 工具讀寫這些紀錄。
- 可以做什麼
- 記錄與讀取專案記憶:含目標、完成標準、允許路徑與下一步行動的工作項目,以及帶證據、替代方案與結果的決策
- 依產品、顧問或自動化範本蒐集啟動問答與初始文件,並提出需求供你核准
- 追蹤經驗、守衛規則、範圍放寬、相依關係,以及從匯入、清單與匯出的 n8n 工作流程靜態推導出的架構
- 在唯讀的本機控制面板或離線 HTML 快照中檢視全部內容,並把紀錄匯出為 Markdown 筆記供 Obsidian 使用
- 透過已安裝的 Codex 或 Claude Code CLI 執行代理檢查與委派工作項目,並由另一個宿主審查變更差異
- 適用情境
- 適合希望助理在單一專案的多個工作階段之間延續脈絡,並讓決策、證據與經驗可追溯的情境,不論是軟體產品、顧問專案或 n8n 工作流程自動化。
- 執行需求
- 需要 Python 3.11 或更新版本以及 uv,透過 PyPI 上的 uvx 或 uv tool install 安裝。安裝程式會把 MCP 伺服器與生命週期掛鉤接入 Codex、Claude Code 或其他本機 stdio 用戶端。代理檢查與委派工作需要已安裝的 Codex 或 Claude Code CLI,並消耗該帳號的用量。不需帳號、託管資料庫或遙測。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Project Memory,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
Project Memory
Project Memory keeps the plan, the evidence, the decisions, the outcomes and the accepted lessons of one project in a local SQLite database. Your assistant reads and writes those records through three MCP tools. You follow the same records in a local control panel, which is read only, and you decide in the chat what is approved, accepted and merged.
It works for a software product, a consulting engagement and a workflow automation built in n8n. Work items can be code, documents, deliverables or exported workflows.
Public beta. Python 3.11 or newer is required. The runtime uses only the Python standard library. There is no telemetry, no hosted database and no account. Agent checks and delegated work run through the Codex or Claude Code CLI that you already have installed, and they consume that account's usage.
Install
Install uv, then run this inside the project you want to remember:
For Claude Code, replace --client codex with --client claude. Setup preserves existing records and settings, connects the MCP server and the lifecycle hooks, and opens the control panel. --trust approves the project integration in that client; omit it to approve the connection yourself. Start a new assistant session afterwards. Add --no-view for a headless installation.
For a permanent command:
To install the published wheel directly from GitHub instead of PyPI:
Other local MCP clients use setup --client mcp and the stdio server. Setup and lifecycle covers client configuration, the plugins, upgrades, backups and removal.
Start a project
A new project starts from one of three templates:
init creates the folder, initialises git unless you pass --no-git, writes any starter document that does not exist yet, connects the clients you name and records each phase as a work item. Running it again creates nothing a second time and never overwrites a document you have edited.
The kickoff conversation
Each template carries seven kickoff questions. Your assistant reads them with memory_get kickoff and asks you, for example what problem the product solves, who the stakeholders are, or which systems the process uses. It records your answers with memory_write answer_kickoff, fills the starter documents from those answers and proposes requirements. Only you approve the requirements baseline. Until that approval exists, the control panel opens on a kickoff checklist that shows the open questions, the research still needed, the starter documents that are still empty and the phases.
The everyday loop
Tell the assistant the result you want, the constraints and what would count as complete. It then records a work item with its objective, its completion criterion, the next action and the files it may change, continues from that record in later sessions, and records each decision with its evidence, alternatives, uncertainty and expected consequence. After acting, it records what actually happened. A failure stays visible after a later success.
You approve requirements, accept or reject proposed lessons, widen the allowed paths, and decide what is merged. Capturing a document does not approve its contents, and a plan does not authorise work.
The control panel
Run project-memory view in the project, or ask the assistant to open Project Memory. The panel reads the same database and refreshes while it is open. It is laid out as a Notion workspace: a sidebar of views, database tables with views, filters, sorting and grouping, and each item opening as a page beside the list, in a light or a dark theme.
project-memory view --output review.html --include-bodies --no-open writes an offline snapshot instead. See the control panel guide.
Read the records in Obsidian
project-memory export --obsidian <vault> writes the records as a folder of Markdown notes with frontmatter and wikilinks, which Obsidian opens as a vault. A work item is a folder holding its own decisions, outcomes and plans, and sources, lessons and requirement revisions sit in shared folders beside it. The graph, the backlinks and the search of Obsidian then work on the project history, while the panel stays the place where you decide.
The export runs in one direction. The database is the source of truth, the export owns one folder inside the vault and touches nothing outside it, and a note edited inside that folder is lost on the next run. After a session that wrote records, the session stop hook refreshes the vault in a separate process.
Agent checks and delegated work
When Codex or Claude Code is configured, Project Memory can start short, separate host processes. An agent check reads the records and the project and reports on an outcome, on the current intent or on an unconfirmed execution. Delegated work runs a work item in its own git worktree, limited to the paths in its plan, and a second host reviews the resulting diff before you merge it. If one host reports a usage limit or a rate limit, the run reroutes once to the other configured host.
These runs use your installed Codex or Claude Code account and consume that account's usage. A reviewer cannot edit plans, accept lessons or merge anything. See agent checks and delegated work.
Guards and scope
An accepted lesson with a trigger becomes a guard. When a guard matches the paths or the wording of a work item, a decision must list that lesson in lessons_considered with an explicit yes or no and a reason, otherwise the decision is rejected. When a work item has allowed paths, the lifecycle hooks block an edit outside them, record the block and ask you to extend the scope in the chat. The record fields document defines the pattern rules.
Honest boundaries
- A recorded plan is not permission. Autonomy and scope are advisory records; your host's own permissions and your current instructions decide what may run.
- The scope guard reads the file paths of edit tools, patch markers and the targets of MCP write tools. It does not parse shell redirection or other indirect writes inside shell commands.
- Hooks record events mechanically. Only the assistant can record what those events meant, and a connected panel does not prove that the recording is complete.
- An agent verdict is an interpretation. A passing check does not prove that the work is correct, and an empty findings list is not proof either.
- Architecture is read statically from imports, manifests and exported workflow files. Dynamic loading is not detected.
- Bounded retrieval limits the characters a call returns. It cannot control the complete model input of the host.
- Daily productivity, token usage, correction rates and human task time are not measured, and this project makes no claim about them. Testing and limitations states what is verified and what is not.
Documentation
- User guide, from the first conversation to delivery
- Control panel and agent checks and delegated work
- Setup and lifecycle and record fields
- Testing and limitations, contributing, security and releasing
Packages are published on PyPI and in GitHub Releases.
來源:README.md,提交 247860c
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0版本歷史
1- v0.5.0b9最新Sep 16, 2026
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