Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Your AI starts every session from zero. Your project stays on course.

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.
Free to start. The whole plan β build β review β 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, and the issue tracker. PAPI's engine is closed source and hosted β you connect to it, you don't build it from here. The
@papi-ai/serverpackage on npm is the supported local runtime. The contents of this repo are MIT.
Let your AI install it. Paste this to your assistant β Cursor, Claude Code, Windsurf, Codex, VS Code, or any other MCP client:
Read https://getpapi.ai/llms.txt and set up PAPI
That's the whole install. Your assistant reads the runbook for whichever tool it's running in and wires up the connection itself. (Same instructions live in this repo as llms.txt and llms-install.md if your assistant can't fetch URLs.)
Then authenticate β this part is yours. PAPI signs in over OAuth, and no AI can click through a browser consent screen for you. Your assistant will tell you exactly where to click; until you do, the server sits at Needs authentication and no tool call will work. This is the step people miss.
Once you're connected, tell your assistant:
Run the
setuptool to scaffold this project, then runorientand tell me which cycle this project is on.
In Claude Code, the shortest path is the plugin β two lines, nothing to copy or edit:
The plugin carries the server config, plus two skills: check-mcp diagnoses a connection that isn't working, and papi-verify 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:
Either way, finish with /mcp β papi β Authenticate.
DeepSeek Harness users can install the repository-owned bundle after creating a PAPI connection token:
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.
The methodology is the product: a plan, build, review, release loop your assistant runs with you, with memory that compounds. PAPI has been built with PAPI for every cycle in the badge above.

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

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.
Unlike Linear, Jira, Asana or Notion, this was not built for humans and then opened to agents. Those boards assume a human writes the ticket and a human reads it, in a tab your AI can't see. PAPI's board is written and read by your assistant as a side effect of working: starting a build opens the task, finishing it files the report, releasing closes the cycle. You approve the plan and you sign off the review. The ticket admin in between is the part that disappears. You manage the outcome, not the keystrokes.
Unlike Taskmaster and other in-repo task files, PAPI's state isn't a file one tool generates once and then drifts from. It's hosted and structured β cycles, build reports, review verdicts, and Active Decisions carrying confidence levels that change as evidence arrives. The same project memory is there from Claude Code, Cursor, VS Code, or Codex, so switching tools doesn't reset your context, and last cycle's learnings are an input to the next plan rather than something you have to remember to mention.
Neither of those is a knock on the tools. They're solving a different problem to the one that breaks every time your assistant opens a fresh window.

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.

Every cycle leaves a trail, so the reasoning behind the project is still there months later β including for the assistant reading it back.
PAPI exposes these MCP tools to your assistant. The whole loop is a handful of calls.
Core loop
Board and backlog
Strategy and intelligence
Docs and projects
In this repo:
| Doc | What it covers |
|---|---|
| llms.txt | The agent runbook β point your AI at this (live version: getpapi.ai/llms.txt) |
| llms-install.md | Per-tool install instructions for AI assistants |
| docs/install.md | Install paths for every supported tool |
| docs/how-it-works.md | Cycles, handoffs, decisions, and how the pieces fit |
| docs/troubleshooting.md | Connection problems, project routing, common fixes |
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