PAPI: Persistent Adaptive Project Intelligence

io.github.getpapi由 GitHub 帳號 getpapi 發布v0.402.225更新於 Nov 6, 2026

Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.

已驗證Streamable HTTP可網頁執行效率與工作流開發者工具

概覽

AI 產生的概覽

託管式專案管理層,為 AI 程式助理提供跨工作階段持續的規劃、建置、審查與發佈循環。

可以做什麼
  • 執行核心循環:工作階段開始時 orient、為新專案 setup、用 plan 拆解任務、列出並執行建置、提交審查、發佈或關閉週期
  • 讀取與編輯專案看板,記錄週期外的工作,收集想法並回報缺陷
  • 進行策略審查、記錄帶取代歷程的 Active Decisions,並做跨週期的回顧
  • 註冊與搜尋專案參考文件,列出、切換或建立專案
  • 加入隊友邀請的專案,檢視與管理貢獻者
適用情境
適合助理要在多個工作階段中長期推進同一專案、容易在對話之間失去方向的情況:目前週期、進行中的任務與決策紀錄對支援的用戶端都可見。也適合想依規劃到發佈流程推進、又不想手動維護工單系統的開發者。
執行需求
需要 getpapi.ai 的免費帳號。可連線託管的 streamable-HTTP 端點並以瀏覽器 OAuth 登入,或以專案 id 與連線權杖,透過 Authorization bearer 標頭與 x-papi-project-id 標頭傳入。另提供本機 npm 執行環境(@papi-ai/server,以 npx 啟動)。多數用戶端只會在新對話中載入新連線。
安裝前請注意
引擎為閉源且託管,專案資料存放在廠商伺服器上,使用前請查看其信任說明。連線權杖與專案 id 是憑證,會寫入工具設定。助理會寫入看板並記錄決策,計畫由你核准、審查由你簽核。免費方案最多三個專案,另有付費方案。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 PAPI: Persistent Adaptive Project Intelligence,將其新增到工作區。
  2. 連接 所需的 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.getpapi%2Fpapi/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。

{
  "mcpServers": {
    "papi-persistent-adaptive-project-intelli": {
      "type": "http",
      "url": "https://mcp.getpapi.ai/mcp"
    }
  }
}

README

PAPI

[npm @papi-ai/server] [MCP Registry] [Cycles shipped] [GitHub stars] [License]

End-to-end project management for AI builders.

Your AI starts every session from zero. Your project stays on course.

[A cycle running in PAPI: the hub, the board, the decision log, and the reviews]

AI coding tools are great at writing code and terrible at holding a direction. Scope grows, plans change, and the decisions behind them get made in a chat window and lost there. PAPI keeps the project on course. It gives your assistant structured plan, build, review and release cycles, plus a decision trail recording what was chosen, what was dropped, and why. Your assistant writes and reads it while it works, so it stays current without anyone maintaining it.

You connect it once. From then on, your assistant starts every session knowing which cycle you're on, what's in flight, and what to do next.

If PAPI is useful, star the repo. It's how other people building with AI assistants find it.

Free to start. The whole plan → build → review → release loop, on up to three projects, no card. Free runs a real project start to finish; it isn't a trial. Pricing.

What this repo is. Documentation, install guides, plugin and extension manifests, the PAPI agent skills, and the issue tracker. PAPI's engine is closed source and hosted — you connect to it, you don't build it from here. The @papi-ai/server package on npm is the supported local runtime. The contents of this repo are MIT.

Quick start

Let your AI install it. Paste this to your assistant — Cursor, Claude Code, Windsurf, Codex, VS Code, or any other MCP client:

Read https://getpapi.ai/llms.txt and set up PAPI

Your assistant reads the runbook and walks you through the whole first run: checking it's in the right project folder, connecting, setting up your project, planning your first cycle, and building your first task. It works in coding tools and in chat apps like claude.ai and ChatGPT, and you don't need to be a developer. (The same runbook lives in this repo as llms.txt and llms-install.md if your assistant can't fetch URLs.)

The one thing only you can do. You need a free account at getpapi.ai. After that there are two ways to connect:

  • Connection token (default). Open the Connect panel on getpapi.ai, copy the project id and connection token under "This is your project", and hand them to your assistant. It puts them in your tool's config and PAPI works straight away, with no browser step.
  • Browser sign-in. Your assistant adds the server and you approve it in the browser. No assistant can click through that screen for you, and until you do the server shows Needs authentication.

Most tools only load a new connection when a conversation starts, so open a fresh conversation after connecting and ask your assistant to run orient.

Prefer to wire it up yourself?

In Claude Code, the shortest path is the plugin — two lines, nothing to copy or edit:

/plugin marketplace add getpapi/papi/plugin install papi@papi

The plugin carries the server config and the PAPI skills for each phase of the cycle: planning, building, reviewing and releasing. It also includes check-mcp, which diagnoses a connection that isn't working, and papi-verify, which health-checks the current cycle.

Every tool also takes the same streamable-HTTP endpoint directly, https://mcp.getpapi.ai/mcp. In Claude Code that's:

claude mcp add --transport http papi https://mcp.getpapi.ai/mcp

Either way, finish with /mcp → papi → Authenticate. Or skip the browser step entirely with the connection token from the Connect panel:

claude mcp add --transport http papi https://mcp.getpapi.ai/mcp --header "Authorization: Bearer YOUR_CONNECTION_TOKEN" --header "x-papi-project-id: YOUR_PROJECT_ID"

The DeepSeek Harness bundle is in this repo but is not yet published to npm, so the command below won't resolve until it is. After creating a PAPI connection token, it will install with:

text
dsh plugin --profile web add @papi-ai/deepseek-harness

See PAPI for DeepSeek Harness for token handling, verification, compatibility, and removal.

Per-tool config for Cursor, VS Code, Windsurf, Codex, and any generic MCP client is in docs/install.md.

What you get

  • plan breaks your goals into a cycle of right-sized tasks, each with a build handoff your assistant can execute directly.
  • build tracks what was built, what surprised you, and what was discovered along the way.
  • review and release close the loop, so every cycle feeds the next plan.
  • strategy reviews every few cycles step back and check direction, not just velocity.
  • A dashboard at getpapi.ai shows your cycles, board, and decisions, so you can see the state of the project without asking.

The methodology is the product: a plan, build, review, release loop your assistant runs with you, where each cycle's learnings feed the next plan. PAPI has been built with PAPI for every cycle in the badge above.

[The PAPI hub: what needs your attention, where the current cycle stands, and the next move]

The hub opens on one question — what happens next. The cycle's progress through plan, build, review and release sits under it.

[The PAPI board: tasks grouped by status with priority, size, type and module]

The board is the full picture. Your assistant writes to it as a side effect of working, so it is current without anyone maintaining it.

How PAPI differs from a tracker

Linear, Jira, Asana and Notion are built for people to write tickets and other people to read them. They suit that job well, and plenty of teams keep using them.

PAPI's board is written and read by your assistant as it works. Starting a build opens the task, finishing it files the report, and releasing closes the cycle. You approve the plan and sign off the review.

Taskmaster is a good way to break work into tasks inside a repo. PAPI adds state that lives outside the repo and persists across tools: cycles, build reports, review verdicts, and Active Decisions whose confidence changes as evidence arrives. The same project is available from Claude Code, Cursor, VS Code, Codex and the others, so switching tools doesn't reset it. Moving from Taskmaster? See PAPI vs Taskmaster.

[Active Decisions in PAPI: each decision with its confidence level and the cycles it has survived]

Active Decisions are the part a task tracker has no field for: what was decided, why, what was rejected, and when to revisit it. Confidence moves as evidence arrives.

[Reviews in PAPI: what shipped, where the project stands, and the direction for the next cycles]

Every review leaves a trail, so the reasoning behind the project is still there months later, including for the assistant reading it back.

Tools

PAPI exposes these MCP tools to your assistant. The whole loop is a handful of calls. The server exposes more than these, including claims and sessions for teammates, conventions, document registration and project export. The Tool Reference lists all of them.

Core loop

  • orient — one call returns the current cycle, what's in flight, and the recommended next action. Run it at the start of every session.
  • setup — scaffold PAPI onto a new project.
  • plan — break goals into a cycle of right-sized tasks, each with a build handoff your assistant can execute directly.
  • build_list — list the current cycle's tasks and their handoffs.
  • build_execute — start a task (creates a branch and handoff) and complete it (records the build report).
  • review_list / review_submit — surface finished builds and record accept / request-changes / reject verdicts.
  • release / close_cycle — finish the cycle, with a published version (release) or without one (close_cycle).
  • tutorial_mode — turn on short explanations and one clear next step in every reply. Handy for your first few cycles.

Board and backlog

  • board_view — read the project board and any task.
  • board_edit — change a task's status, cycle, priority, or notes.
  • ad_hoc — record quick work done outside the cycle so it shows in project history.
  • idea — capture a feature, bug, or research note into the backlog.
  • bug — file a bug against the board.

Strategy and intelligence

  • strategy_review — step back every few cycles to check direction, not just velocity.
  • strategy_change — record an Active Decision, with supersede history.
  • zoom_out — a periodic retrospective across many cycles.

Docs and projects

  • doc_register / doc_search — register and find project reference docs.
  • project_list / project_switch / project_create — manage multiple PAPI projects.

Teams

  • join — join a project a teammate invited you to.
  • contributor_list / contributor_add — see and manage who works on the project.

Documentation

In this repo:

DocWhat it covers
llms.txtThe agent runbook — point your AI at this (live version: getpapi.ai/llms.txt)
llms-install.mdPer-tool install instructions for AI assistants
docs/install.mdInstall paths for every supported tool
docs/how-it-works.mdCycles, handoffs, decisions, and how the pieces fit
docs/troubleshooting.mdConnection problems, project routing, common fixes

Full documentation on the website — no account needed:

PageWhat it covers
Quick StartZero to your first cycle plan in under 5 minutes
WorkflowThe full plan → build → review → release loop
ConceptsCycles, handoffs, Active Decisions — the vocabulary
Cheat SheetEvery command on one page
Tool ReferenceEvery PAPI MCP tool with parameters and use cases
TroubleshootingConnection and auth first aid
HandbookFor teams: reading dashboards, cycle reports, review flow

Community and support

Stuck, or something's broken? Open an issue — there are templates for each case, and the connection problem one is the one to reach for if PAPI won't connect or won't authenticate, which is where people get stuck most:

Links

  • Website and dashboard: getpapi.ai
  • Pricing: getpapi.ai/pricing — free tier, no card
  • What shipped in each cycle: getpapi.ai/changelog
  • Data, access, and what PAPI doesn't have yet: getpapi.ai/trust
  • Licence: the contents of this repo (docs, guides, config examples, Dockerfile) are MIT. That licence covers this repository only — not the PAPI engine, and not the PAPI name or logo, which are trademarks.

來源:README.md,提交 46a4d10

工具

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版本歷史

7
  1. v0.402.225最新Nov 6, 2026
  2. v0.402.220Nov 3, 2026
  3. v0.402.208Oct 30, 2026
  4. v0.402.206Oct 30, 2026
  5. v0.402.157Oct 15, 2026
  6. v0.402.35Sep 26, 2026
  7. v0.7.85Sep 16, 2026

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