Plori

Using plori from an agent

plori (https://plori.ai) gives you AI agents. Each agent runs on its own cloud computer with a persistent disk, a shell, developer tools, and memory. You can create agents, send them work, and read their replies programmatically.

Connect

MCP (recommended for a hosted client): Streamable HTTP at https://api.plori.ai/mcp.

• OAuth 2.1: compliant MCP clients connect with no hand-copied key. An unauthenticated request returns 401 with the discovery chain (RFC 9728 Protected Resource Metadata at https://api.plori.ai/.well-known/oauth-protected-resource, then dynamic client registration and authorization code + PKCE). The account owner signs in once with an email one-time code.
• API key: the account owner provisions a key at https://plori.ai and you send Authorization: Bearer plori_sk_....

CLI (recommended from a terminal): npm i -g @plori/cli, or run it without installing via npx -y @plori/cli, gives you the plori command for the same operations from your shell. Authenticate with plori login or by setting PLORI_API_KEY. Output is human-readable on a terminal and a single JSON document when piped or with --json, so it composes in scripts. Commands are listed under "CLI commands" below.

REST: the same operations at https://api.plori.ai/v1 with the same bearer token. Full authentication instructions: https://plori.ai/auth.md

Tools

Account and agents: list_agents, get_agent, create_agent (name, optional model), set_agent_model, delete_agent, get_credits, get_usage, get_disk.

Runs: invoke_agent sends a message and by default blocks until the turn finishes, returning the assistant's reply. Pass wait=false to get a run_id immediately and poll get_run_result. list_runs lists recent runs.

Human in the loop: a run can pause on an approval or input request (status awaiting_input). Read the queue with list_pending_inputs and reply with answer_pending_input (run_id + tool_call_id, then approved=true/false for approvals or value for input requests).

Deferred work: schedule_run (agent_id, prompt, and delay_seconds or an RFC3339 fire_at) invokes the agent later as an ordinary run.

Workflows: list_workflows, create_workflow (name, optional description/trigger_kind/ cron_expr), run_workflow (runs a workflow now: a real execution billed like any run, returning the execution, terminal or still running), and get_workflow_execution to poll one. A workflow's steps are built by an agent; these tools manage and run the result.

CLI commands

The CLI mirrors the tools above; an agent is addressable by name or id, and every command accepts --json.

• plori create <name>: get or create an agent by name (reusing a name returns the existing agent). plori agents, plori agent <name>, plori set-model <name> <model>, plori delete <name> --yes.
• plori run <name> "message": send a message and, by default, wait for the reply and print it. Add --follow to stream the turn live, or --no-wait to get a run id back immediately. Pass - as the message to read it from stdin.
• plori result <name> <run-id> (add --wait to block) and plori runs <name> read run status and history.
• plori inputs <name> lists runs paused on a human request; plori answer <run-id> <tool-call-id> --approve|--deny|--value <v> replies.
• plori schedule <name> "prompt" --in <seconds> (or --at <rfc3339>) defers a run; plori schedules <name> and plori unschedule <name> <id> manage them.
• plori workflows list, plori workflows create <name> [--trigger cron --cron <expr>], plori workflows run <name|id> (run it now), plori workflows execution <name|id> <exec-id>.
• plori credits, plori usage, plori disk read account state.

Costs and limits

Running an agent spends credits; check get_credits before invoking. Agent count and model tier follow the account's plan. Every call is scoped to the account that owns the credential; there is no cross-account access.

More

• Integration front door: https://plori.ai/agents.md
• MCP connect guide: https://plori.ai/mcp
• CLI on npm: https://www.npmjs.com/package/@plori/cli
• Authentication detail: https://plori.ai/auth.md
• Site map for agents: https://plori.ai/llms.txt

Source: hashgraph-online/awesome-codex-plugins → plugins/plori-ai/codex-plugin/skills/plori/SKILL.md