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ai.plori/plori

Create and drive plori cloud agents and workflows over MCP; each agent has its own environment.

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README

From the repo.

plori

plori (plori.ai): a cloud AI agent with its own persistent environment - durable disk, real CLI tools, and memory.

plori provides the agent: each one gets a persistent machine with a real disk, real tools, and memory of its own. Idle agents scale to zero. 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

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

Codex CLI

codex mcp add plori --url https://api.plori.ai/mcp
codex 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):

npx plori-mcp
# headless / CI: authenticate with an API key instead of the OAuth flow
npx 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.

curl -fsSL https://plori.ai/install.sh | sh
plori 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, the disk, and the files are the agent's own cloud environment.

Verify the connection

Ask your client:

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

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

What the tools do

The server exposes 25 tools in five groups.

  • Agents (the Plori Router picks each agent's model per task): list_agents (your agents, with model and live session status), get_agent (one agent's name, type, model, and status, plus its mailbox of mail from other agents on the account), create_agent (get or create an agent by name, which reuses an existing agent of that name instead of making a duplicate), delete_agent (permanently delete an agent and revoke its disk).
  • Runs: invoke_agent (send a message 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), 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 (invoke an agent once later, after a delay or at a timestamp).
  • Human-in-the-loop: list_pending_inputs (runs paused on an approval or an input request), answer_pending_input (approve, deny, or answer one, which starts a continuation run).
  • Workflows: list_workflows (every workflow, or one agent's with agent_id, or the unassigned ones with agent_id="none"), get_workflow (metadata and the step projection pinned for execution), get_workflow_version (one exact version's full definition and parameter values), create_workflow (an empty workflow on a manual, cron, or webhook trigger, for an agent to build), edit_workflow (a batch of constrained edits as one new draft, under compare-and-swap on base_version), set_workflow_agent (assign one of your agents to a workflow, typically after delete_agent reports paused workflows or run_workflow returns workflow_agentless), run_workflow (run a built workflow now, as a real, billed execution), list_workflow_executions (recent executions with status, fault, trigger source, timing, and credits), get_workflow_execution (one execution's per-step input and output payloads).
  • Account: get_credits (balance and plan), get_usage (spend by meter and by agent), get_disk (included, purchased, and used bytes), empty_trash (permanently empty an agent's trash so deleted files stop counting against the disk; only while that agent's pod is asleep), list_connections (your third-party OAuth providers with status, authorization and expiry times, and the scopes configured for each, never tokens or client secrets).

A turn that is still running when the hold ends continues on the server: invoke_agent 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: creating and 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

Config for your environment

Use the endpoint URL below in your config. No API key — you connect directly.

Tool

OS

Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "mcp-server": {
      "url": "https://api.plori.ai/mcp"
    }
  }
}

Paste into mcpServers in the config file. Restart Cursor after saving.

If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.