Plori

ai.plori由 plori.ai 發布官方發布者v0.27.2更新於 Nov 8, 2026

Plori runs AI agents in cloud Workspaces.

已驗證Streamable HTTP可網頁執行雲端與基礎設施開發者工具AI 與機器學習

概覽

AI 產生的概覽

Plori 是遠端 MCP 伺服器,讓助理在雲端 Workspace 中執行 AI 代理,可建立工作區、發起執行並合併代理變更。

可以做什麼
  • 管理工作區與帳戶:列出、建立、刪除工作區,讀取與上傳工作區檔案,查詢執行費用與點數餘額
  • 執行代理:向工作區對話送出訊息並等待回覆,取得執行結果、執行紀錄,取消執行,預約稍後執行
  • 處理人工介入:列出因審核或提問而暫停的執行,回答或拒絕輸入請求
  • 協調工作代理:在各自的工作區檔案副本上啟動代理,列出與檢視狀態,向執行中的代理送出訊息,停止任務群組
  • 審閱變更集:提交、列出、檢視、接受或拒絕工作代理的變更集,使變更成為新的工作區版本
適用情境
適合想把程式開發、研究或內容工作交給各自在副本上作業、再由 Plori 合併結果的雲端代理,並從自己的 MCP 用戶端而非 Plori 網頁應用程式驅動這些代理的情境。
執行需求
遠端 streamable HTTP 端點,無需在本機安裝。首次連線需在瀏覽器中透過 OAuth 2.1 登入;無介面或 CI 環境可使用在 Plori 儀表板產生的 API 金鑰。需要已註冊的 Plori 帳戶。僅支援 stdio 的用戶端需使用 plori-mcp npm 包裝套件或 mcp-remote。
安裝前請注意
執行代理會消耗帳戶中的預付點數,讀取操作免費。工具可建立並永久刪除工作區及其檔案、版本與對話,可覆寫檔案,接受變更集會取代工作區檔案。MCP 用戶端無法核准動作,每個暫停的審核都需在 Plori 網頁應用程式中開啟;預約執行也需在那裡確認後才會排入佇列。API 金鑰可存取帳戶,應妥善保管並可隨時撤銷。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Plori,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/ai.plori%2Fplori/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": {
    "plori": {
      "type": "http",
      "url": "https://api.plori.ai/mcp"
    }
  }
}

README

plori

Plori runs AI agents that collaborate on one product in the cloud.

Plori runs AI agents that collaborate on one product in the cloud. Code, research, content: each works on its own copy, and Plori merges their changes back. The agents work in a cloud Workspace that keeps the product's files with saved revisions. Only the Workspace files persist between runs. You talk to your agents in the web app, or drive them from your own tools over MCP and REST.

This repository is the integration front door. The product itself lives at plori.ai; the remote MCP server lives at https://api.plori.ai/mcp.

Connect your MCP client

plori is a remote MCP server (streamable HTTP). There is nothing to install or run locally. Sign-in happens in your browser via OAuth 2.1 the first time your client connects; headless environments can use an API key instead.

Claude Code

Paste this into your Claude Code conversation:

Set up https://plori.ai/SKILL.md

Claude reads the setup instructions and configures MCP if needed. If the new server has not loaded, type /reload-plugins when Claude asks, then continue in the same conversation. With pairing, open the short address Claude shows, enter the code, sign in, and approve. You can use a phone while Claude Code runs on a remote machine. No installed skill or plugin is required.

Cursor

Use the one-click Add to Cursor button, or add manually: Settings -> MCP -> Add server with URL https://api.plori.ai/mcp.

VS Code

sh
code --add-mcp '{"name":"plori","type":"http","url":"https://api.plori.ai/mcp"}'

Codex CLI

sh
codex mcp add plori --url https://api.plori.ai/mcpcodex mcp login plori

Codex auto-detects plori's OAuth on login. One-install alternative with the skill bundled: codex plugin marketplace add plori-ai/codex-plugin then codex plugin add plori@plori.

Cline

Follow llms-install.md, written for Cline's automated installer.

Any other client

Native streamable-HTTP clients connect to https://api.plori.ai/mcp directly. Clients that only speak stdio can bridge with the plori-mcp npm package (a thin wrapper around mcp-remote with the endpoint pinned; this repository is its source):

sh
npx plori-mcp# headless / CI: authenticate with an API key instead of the OAuth flownpx plori-mcp --header "Authorization: Bearer plori_sk_..."# equivalent, without the wrapper:npx mcp-remote https://api.plori.ai/mcp

API keys are minted in Dashboard -> Settings on a registered account.

Or skip MCP: your own terminal

The plori CLI is not an MCP client. It is a door of its own, and it opens the same live session the web app shows: the recent history, a prompt, streaming output, and the approval queue in one place. A turn you send in the terminal appears in an open browser tab as it streams.

sh
curl -fsSL https://plori.ai/install.sh | shplori login && plori attach <agent-name>

The installer drops one static binary in ~/.local/bin and needs no Node; if that directory is not on your PATH yet, the script prints the line to add. npm i -g @plori/cli works too. The argument to attach is an agent name, an agent id, or a session id, so a session id copied out of the web app works on its own. Ctrl-D detaches and leaves the run going on the server.

The terminal does not give the agent access to your local files. The shell and the Workspace files are in Plori's cloud.

Verify the connection

Ask your client:

List my plori Workspaces and tell me how many credits I have left.

You should see list_workspaces and get_credits tool calls and a real answer.

What the tools do

The server exposes 24 tools in five groups. A Workspace holds durable files with saved revisions, a main conversation, and run history. An agent runs in a Workspace with a shell and the Workspace files under /workspace.

  • Workspaces and account: list_workspaces (the Workspaces you own, with state, default agent, current revision, and storage_readiness while storage starts), create_workspace (a Workspace with its first revision. Unless you pass default_manager_agent_id, it also creates a default agent. idempotency_key makes a retry return the same Workspace), delete_workspace (permanently delete a Workspace with its files, revisions, copies, and conversations), read_workspace_file (a file or a directory listing in a saved revision), upload_workspace_file (write a file into a Workspace's current files, replacing any file at path; pass UTF-8 text as content, or omit content for a one-time upload_url for a larger or binary file), get_workspace_costs (the amount charged for the runs of a Workspace or of one task group in a time window), get_credits (prepaid balance, plan, and limits).
  • Runs: send_workspace_message (send a message to a Workspace conversation and wait for the reply, with wait_seconds to set how long to hold, idempotency_key to make a retry return the original run, and callback_url plus callback_secret to post a signed status notification to your endpoint), get_run_result (a run's status, timestamps, credits, tokens, tool progress, and the reply once it finishes; for a Workspace manager that waits for its workers, worker_inputs lists the workers' open questions and approvals and next_run_id names the run that continues it), list_runs (an agent's run history, most recent first), cancel_run (stop an in-flight run, which reports cancelling and then cancelled), schedule_run (run an agent once later, after a delay or at a timestamp. It returns awaiting_confirmation and a confirm_url. Plori queues the run only after you confirm it in the Plori web app).
  • Human-in-the-loop: list_pending_inputs (runs paused on an approval or an input request), answer_pending_input (deny one, or answer an agent's question, which starts a continuation run). An MCP client cannot approve an action: each paused approval carries an approve_url that you open in the Plori web app.
  • Workers: create_workspace_worker (start an agent on its own copy of the Workspace files, so several workers run at the same time without changing each other's files), list_workspace_workers and get_workspace_worker (status, run, and whether Plori saved the worker's files), steer_workspace_worker (send a message into a running worker's turn), stop_workspace_task_group (stop every worker of a task group).
  • Changesets: when a worker's run completes normally and changes at least one file, Plori submits its saved files and accepts a clean comparison itself. submit_workspace_changeset (submit the saved files of a worker whose run ended in error, was cancelled or was stopped, for comparison with the Workspace's current files), list_workspace_changesets and get_workspace_changeset (state, phase, and changed and conflicting paths), accept_workspace_changeset (the worker's changes become the Workspace's current files as a new revision), reject_workspace_changeset (keep the Workspace files as they are).

A turn that is still running when the hold ends continues on the server: send_workspace_message returns a run_id with status running and a poll_after_seconds delay, and you read the answer with get_run_result using wait=true (or your own wait_seconds, up to 1800).

Costs: running agents spends plori credits from your account. Reading (lists, results, balances) is free. The pricing page has the details. Revoke a client's access any time in your client's settings, or revoke the API key in Dashboard -> Settings.

For AI agents reading this

The machine-readable entry points:

  • Front door: plori.ai/agents.md
  • Site index: plori.ai/llms.txt
  • Skill: SKILL.md (index: /.well-known/agent-skills/index.json)
  • MCP server card: https://api.plori.ai/mcp/server-card
  • OAuth discovery: RFC 9728 protected-resource metadata on api.plori.ai, dynamic client registration supported
  • Registry entry: ai.plori/plori in the official MCP Registry

Every page on plori.ai is also served as Markdown: append .md to the path or send Accept: text/markdown.

Docs and support

來源:README.md,提交 287eeff

工具

0
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版本歷史

20
  1. v0.27.1Nov 6, 2026
  2. v0.27.0Nov 3, 2026
  3. v0.26.2Oct 31, 2026
  4. v0.26.1Oct 29, 2026
  5. v0.26.0Oct 26, 2026
  6. v0.25.0Oct 22, 2026
  7. v0.24.0Oct 18, 2026
  8. v0.23.0Oct 14, 2026
  9. v0.22.0Oct 13, 2026
  10. v0.20.1Oct 12, 2026
  11. v0.20.0Oct 10, 2026
  12. v0.18.0Oct 5, 2026
  13. v0.17.0Oct 1, 2026
  14. v0.16.4Sep 28, 2026
  15. v0.16.3Sep 26, 2026
  16. v0.16.2Sep 21, 2026
  17. v0.16.1Sep 20, 2026
  18. v0.16.0Sep 19, 2026
  19. v0.15.0Sep 18, 2026
  20. v0.14.2Sep 16, 2026

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