PAPI
End-to-end project management for AI builders.
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.
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:
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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
