PAPI: Persistent Adaptive Project Intelligence

io.github.getpapi由 GitHub 账号 getpapi 发布v0.402.225更新于 Nov 6, 2026

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

已验证Streamable HTTP可网页运行效率与工作流开发者工具

概览

AI 生成的概览

托管式项目管理层,为 AI 编程助手提供跨会话持久的计划、构建、评审与发布循环。

可以做什么
  • 运行核心循环:会话开始时 orient、为新项目做 setup、用 plan 拆分任务、列出并执行构建、提交评审、发布或关闭周期
  • 读取和编辑项目看板,记录周期外的工作,收集想法并提交缺陷
  • 进行策略评审、记录带取代历史的 Active Decisions,并做跨周期的回顾
  • 注册和检索项目参考文档,列出、切换或创建项目
  • 加入队友邀请的项目,查看和管理贡献者
适用场景
适合助手在多个会话中长期推进同一项目、容易在聊天之间丢失方向的场景:当前周期、进行中的任务和决策记录对受支持的客户端都可见。也适合希望按计划到发布的流程推进、又不想手工维护工单系统的开发者。
运行要求
需要 getpapi.ai 的免费账号。可连接托管的 streamable-HTTP 端点并用浏览器 OAuth 登录,或用项目 id 与连接令牌,通过 Authorization bearer 头和 x-papi-project-id 头传入。也提供本地 npm 运行时(@papi-ai/server,用 npx 启动)。多数客户端只在新会话中加载新连接。
安装前请注意
引擎闭源且托管,项目数据存放在厂商服务器上,使用前请查看其信任说明。连接令牌和项目 id 是凭据,会写入工具配置。助手会写入看板并记录决策,计划由你批准、评审由你签核。免费版最多三个项目,另有付费方案。

安装

在 SourceWeft 中

  1. 打开 控制台中的 PAPI: Persistent Adaptive Project Intelligence,将其添加到工作区。
  2. 连接 所需的 凭据。它们会被私密存储,绝不会在公开页面上展示。
  3. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.getpapi%2Fpapi/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。 客户端会在首次连接时完成 OAuth 登录。

{
  "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.

来源:README.md,提交 46a4d10

工具

0
该服务器需要登录,因此 SourceWeft 无法在没有账号的情况下读取其工具,也未收录。已连接账号所能看到的内容可能因账号及其权限而异。

版本历史

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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