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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

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"
Loading model services...
? Select model service: svc-tongyi
? Select model:         qwen-max
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"
  model:
    service: svc-tongyi
    name: qwen-max
  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.

License

Apache-2.0 — see LICENSE.