
Agent TARS CLI
Agent TARS CLI is the terminal interface for the Agent TARS stack — a multimodal agent that can browse, run commands, use MCP tools, and coordinate GUI/browser actions. It supports multiple model providers (Volcano Engine Seed-1.5-VL, Anthropic Claude 3.7 Sonnet, OpenAI GPT-4o), can pop open a local Web UI to inspect runs, and lets you manage a typed workspace config in TypeScript.
Installation
- Prerequisites
- Node.js ≥ 22 (LTS recommended)
- Google Chrome (the CLI controls your local browser)
- Install
# latest npm install -g @agent-tars/cli@latest # or install the current beta npm install -g @agent-tars/cli@next
Usage
- Pick a model provider & run
# Volcano Engine (Seed 1.5 VL / Doubao)
agent-tars \
--provider volcengine \
--model doubao-1-5-thinking-vision-pro-250428 \
--apiKey "$VOLC_API_KEY"
# Anthropic
agent-tars \
--provider anthropic \
--model claude-3-7-sonnet-latest \
--apiKey "$ANTHROPIC_API_KEY"
# OpenAI
agent-tars \
--provider openai \
--model gpt-4o \
--apiKey "$OPENAI_API_KEY"
When the CLI starts it prints a local link (e.g. http://localhost:8888
) — open it to view the Web UI for the current run.
- Create a global workspace (recommended)
agent-tars workspace --init # guided setup
agent-tars workspace --open # open the folder
Then manage config in TypeScript with types:
// agent-tars.config.ts
import { defineConfig } from '@agent-tars/interface';
export default defineConfig({
model: { provider: 'volcengine' },
// ...other settings
});
- Start a task
agent-tars --provider openai --model gpt-4o --apiKey "$OPENAI_API_KEY"
# Then in the Web UI, enter a prompt like:
# "Tell me the top 10 for Humanity's Last Exam"
Notes
- Designed for headless CLI use; you can summon the Web UI on demand to inspect/steer runs.
- Integrates with browser, shell commands, file system, and MCP tools; includes built-in tools like Search/Browser/File/Command.
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Developer
ByteDance is the developer of the TARS framework and UI-TARS Desktop, exploring multi-agent orchestration systems for building applicat…read more
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System Requirements
Operating System
WINDOWS, MACOS, LINUX
Memory (RAM)
8GB (recommended)
Processor
Modern multi-core CPU
Disk Space
1–2GB for tooling/cache
AI Capabilities
Multimodal perception (vision + text)
Browser and GUI interaction
Tool calling and MCP integration
Planning and web/navigation actions
Typed config and reproducible runs