agentrun-cli 0.1.0

CLI for AgentRun — manage AI agent infrastructure from the command line

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AgentRun CLI

Command-line tool for managing AI-agent infrastructure on the AgentRun platform.

ar (or agentrun) is a single-binary CLI that wraps the AgentRun Python SDK. It lets developers, CI pipelines, and LLM-powered agents create and operate sandboxes, tools, skills, model services and — most importantly — super agents: platform-hosted AI agents that you configure declaratively without writing or deploying any runtime code.

Features

• One-command super agent — ar super-agent run creates a hosted agent and drops you into a chat REPL in seconds.
• Declarative deployment — Kubernetes-style YAML (ar sa apply -f superagent.yaml) for reproducible, version-controlled agents.
• Six resource groups — config, model, sandbox, tool, skill, super-agent, all following the same ar <group> <action> pattern.
• Multi-profile config — store multiple sets of credentials in ~/.agentrun/config.json and switch with --profile.
• Multiple output formats — json (default), table, yaml, and quiet for shell piping.
• Agent-friendly — JSON-by-default output, deterministic exit codes, no interactive prompts when stdin isn't a TTY.
• Rich sandbox primitives — code execution, file system, process management, and CDP/VNC-backed browser automation.
• Single-file distribution — PyInstaller produces standalone ar / agentrun binaries for Linux, macOS and Windows (x86_64 + arm64).

Installation

Prebuilt binary (recommended)

Download a single self-contained binary from Releases. No Python required.

Linux / macOS (x86_64 or arm64):

curl -fsSL https://raw.githubusercontent.com/Serverless-Devs/agentrun-cli/main/scripts/install.sh | sh

Windows (x86_64, PowerShell):

irm https://raw.githubusercontent.com/Serverless-Devs/agentrun-cli/main/scripts/install.ps1 | iex

Pin a specific version with AGENTRUN_VERSION=v0.1.0 …. Change the install directory with AGENTRUN_INSTALL=…. Both installers verify the SHA256 checksum before placing the binary.

Or download the archive manually from the Releases page — naming scheme:

agentrun-<version>-<os>-<arch>.<ext>
# e.g. agentrun-0.1.0-linux-amd64.tar.gz
#      agentrun-0.1.0-darwin-arm64.tar.gz
#      agentrun-0.1.0-windows-amd64.zip

From PyPI

pip install agentrun-cli

From source

git clone https://github.com/Serverless-Devs/agentrun-cli.git
cd agentrun-cli
make install            # editable install into .venv
make build              # standalone binary → dist/agentrun

Verify

ar --version            # or: agentrun --version

Prerequisites

Two one-time setup steps are required before ar super-agent will work:

1. Authorize the AliyunAgentRunSuperAgentRole

AgentRun uses a custom RAM service role — AliyunAgentRunSuperAgentRole — to manage runtime resources on your behalf. Open the link below and confirm in the RAM console:

→ Create AliyunAgentRunSuperAgentRole

Without this role, ar super-agent run / apply will fail at creation time.

2. Grant AliyunAgentRunFullAccess to your AccessKey

The AccessKey you save with ar config set access_key_id ... must belong to a RAM user (or role) that has the AliyunAgentRunFullAccess system policy attached. If you see exit code 3 or AccessDenied, this is almost always the cause.

Want more than QuickStart? Use the console

This CLI covers the QuickStart conversational flow end-to-end. For the full AgentRun experience, head to the Function Compute AgentRun console: https://functionai.console.aliyun.com/cn-hangzhou/agent/

Quickstart

Step 1 — Configure credentials

ar config set access_key_id     LTAI5t...
ar config set access_key_secret ***
ar config set account_id        1234567890
ar config set region            cn-hangzhou

Credentials land in ~/.agentrun/config.json under the default profile. Use --profile staging on any command to target a named profile.

Step 2 — Spin up a super agent and chat

$ ar super-agent run --prompt "You are a Python expert"
Creating super agent: super-agent-tmp-20260420213045 ...
Ready. Type your message (/help for commands).

> Write a quicksort in Python
def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left  = [x for x in arr if x < pivot]
    mid   = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + mid + quicksort(right)

> /exit
─────────────────────────────────────────────
Super agent created: super-agent-tmp-20260420213045
Last conversation:  conv-9f8e7d6c-xxx
Resume:  ar sa chat super-agent-tmp-20260420213045
Delete:  ar sa delete super-agent-tmp-20260420213045
─────────────────────────────────────────────

The agent persists after you exit, so you can continue the conversation later with ar sa chat <name> — the CLI remembers the last conversation id locally.

Step 3 — Declarative deployment

Save this to superagent.yaml:

apiVersion: agentrun/v1
kind: SuperAgent
metadata:
  name: my-helper
  description: "My personal assistant"
spec:
  prompt: "You are my helpful assistant"
  tools:
    - mcp-time-sa
  skills: []
  sandboxes: []
  workspaces: []
  subAgents: []

Then deploy it:

ar super-agent apply -f superagent.yaml
# → action: "created"    (first run)
# → action: "updated"    (subsequent runs)

# Chat with it
ar sa chat my-helper

# Single-shot invocation for scripts
ar sa invoke my-helper -m "Plan my day" --text-only

Multi-document YAMLs (--- separated) let you deploy many agents in one call.

Command groups

Group	Alias	Purpose	Docs
config		Credentials and named profiles	en · zh
model		Register external LLM providers as ModelServices	en · zh
sandbox	sb	Sandboxes + files, processes, contexts, templates, browser	en · zh
tool		MCP and FunctionCall tools	en · zh
skill		Platform skill packages + local execution	en · zh
super-agent	sa	Quickstart / CRUD / declarative / conversation	en · zh

Documentation

• English reference: docs/en/index.md
• 中文手册: docs/zh/index.md

Each page walks through installation, authentication, global options, output formats, exit codes and every command option with runnable examples.

Community

Questions, bug reports and feature requests are welcome on GitHub Issues.

For real-time discussion, join the 函数计算 AgentRun 客户群 on DingTalk — group number 134570017218.

License

Apache-2.0 — see LICENSE.