Within three days, Feishu, DingTalk, and WeCom each rolled out their own CLI (command-line interface) tools, and all of them have broken a thousand GitHub stars. This is no coincidence -- the CLI is becoming the standard interface for AI Agents to control software. This article helps you understand what these tools are, why they matter, and how to use them to make your AI Agent more efficient.

Why Agents Need CLIs

Karpathy puts it bluntly: the CLI's "retro" quality is exactly why it is enjoying a new lease on life in the AI era. The core logic comes down to three points:

Plain text is the native language of LLMs. Asking an Agent to recognize the "Export" button on a screen is slow and error-prone. But having an Agent run one line of wecom-cli contact get_userlist is precise and reliable.

CLIs bridge the API integration gap. Without a CLI, an Agent would first have to digest hundreds of pages of API documentation, then handle authentication, token refresh, and network requests. The official CLI does all that dirty work, so the Agent only needs to call ready-made commands.

CLIs naturally support pipe composition. Individual CLI tools can be chained together seamlessly like building blocks to construct complex workflows.

Three Major Enterprise CLI Tools

Feishu CLI

• Repository: https://github.com/larksuite/cli
• Core capabilities: Operating enterprise-grade Feishu features such as docs, spreadsheets, contacts, and messaging
• Use cases: Letting an Agent manage Feishu workspaces automatically

DingTalk CLI

• Repository: https://github.com/DingTalk-Real-AI/dingtalk-workspace-cli
• Core capabilities: DingTalk workspace management, approval workflows, and contact directory queries
• Use cases: Letting an Agent automate DingTalk office workflows

WeCom CLI

• Repository: https://github.com/WecomTeam/wecom-cli
• Core capabilities: Contact directory management, message sending, and department operations
• Use cases: Letting an Agent operate WeCom back-end data

CLI vs GUI operation: a graphical interface takes multiple clicks, while a single CLI command goes straight to the result.

How to Design a CLI That Works Well for Agents

If you are developing your own CLI tool for Agents to use, developer Eric Zakariasson has put together a set of battle-tested design principles. Unlike a CLI built for humans, a CLI built for Agents needs to follow a completely different design logic.

Principle 1: Must Be Fully Non-Interactive

An Agent cannot press the arrow keys or type "y" mid-run. Every input should be passable as a flag.

# This will make the Agent hang
$ mycli deploy
? Which environment? (use arrow keys)

# This is how the Agent works properly
$ mycli deploy --env staging

Principle 2: Make --help Actually Work

Every subcommand needs a --help, and it must include usage examples. Agents match example patterns far faster than they read descriptions.

$ mycli deploy --help
Options:
  --env     Target environment (staging, production)
  --tag     Image tag (default: latest)
  --force   Skip confirmation

Examples:
  mycli deploy --env staging
  mycli deploy --env production --tag v1.2.3
  mycli deploy --env staging --force

Principle 3: Fail Fast with Actionable Error Messages

Error out immediately when a required flag is missing instead of hanging and waiting. Show the correct way to invoke the command.

Error: No image tag specified.
  mycli deploy --env staging --tag <tag>
  Available tags: mycli build list --output tags

Principle 4: Commands Must Be Idempotent

Agents retry constantly. Repeating the same operation should return "already done" rather than create duplicates.

Principle 5: Provide a --dry-run Flag

Agents should be able to preview the effect before committing a destructive operation.

$ mycli deploy --env production --tag v1.2.3 --dry-run
Would deploy v1.2.3 to production
  - Stop 3 running instances
  - Pull image registry.io/app:v1.2.3
  - Start 3 new instances
No changes made.

Principle 6: Return Structured Data on Success

Return key information such as the deploy ID and URL so the Agent can carry out follow-up actions.

deployed v1.2.3 to staging
url: https://staging.myapp.com
deploy_id: dep_abc123
duration: 34s

How CLIs Relate to MCP

CLIs and MCP (Model Context Protocol) are not competitors but complements: MCP lets Agents read all kinds of data sources through one unified interface, while CLIs execute standard actions. Combining the two forms a complete working loop.

Other mainstream Agent CLI tools currently include:

• Claude Code: Anthropic's Agent coding tool, with strong reasoning capabilities
• Gemini CLI: From Google, with multimodal processing and a usable free tier
• Codex CLI: From OpenAI, natively integrated with GPT models

Practical Advice

1. Install and try them directly: all three enterprise CLIs are open source; just git clone and follow the README to install
2. Start with simple commands: for example, use the WeCom CLI to query the contact directory and verify that the Agent can call it correctly
3. Combine them: write CLI commands into the Agent's tool definitions so it can automatically pick the right command for each task
4. Mind permissions: first-time use requires configuring an API key and enterprise application permissions; refer to each repository's documentation

The CLI revival is not nostalgia; it is the inevitable choice of the AI Agent era. If your tool cannot be used by Agents natively and easily, it risks being phased out of tomorrow's AI workflows.