AI Coding CLI Tools: Installation, Login, and Project Initialization Guide

1. Pick a Form Factor First: Web UI, CLI, Editor Plug-in, or Desktop App

AI coding tools generally come in four form factors, each with a different positioning and audience:

Form factor	Learning curve	Key characteristics	Best for
Web interface	Low	No installation, ready to use in a browser	First-time users who want to understand the interaction model
Command line (CLI)	Medium	Can read and write local files, run commands, fits a wide range of development scenarios	Working developers and engineering teams
Editor plug-in	Low to medium	Chats inside the editor and is aware of the currently open file	Users who prefer to code inside an IDE
Desktop app	Low	Graphical interface, low configuration overhead	Users who are uncomfortable with terminals

1. Web interface: a good fit for a first encounter with AI coding. Within a few minutes you get a feel for the “describe the requirement → generate code → iterate” interaction. It cannot access your local repository, though, so it is not suitable for real development work.
2. Command line: treated by the vendors as the tool’s original design intent, and the focus of this guide. It runs in the terminal, can read project files, run builds and tests, and invoke system commands. It also composes with scripts, pipes, and version control workflows for automation, and it is the most stable option.
3. Editor plug-in: its advantage is that you never leave the editor, and context association is natural — a good fit for small, scoped changes. For complex work such as multi-file refactors or large-scale investigations, switching to the CLI is usually more efficient.
4. Desktop app: reduces the dependency on terminal operations. New users can start here and move to the CLI once they are comfortable.

Bottom line: just exploring? Use the web interface. Doing real development? Learn the CLI. Coding without switching windows? Install the plug-in.

2. Four Things to Prepare Before Installing

1. Confirm your account and access credentials. Register on the model provider’s website first. Before you start, be clear about which authentication method you will use: signing in with a subscription account, or an API key. Section 6 covers when each applies.
2. Set up a command-line environment.
   • macOS / Linux: just open the built-in terminal.
   • Windows: you can use PowerShell, but running the tool inside the Windows Subsystem for Linux (WSL) is preferable, because these tools and most of their dependencies are designed for a Unix-like environment. In WSL, path formats, file permissions, and shell behavior match Linux, so you hit fewer compatibility issues.
3. Evaluate network access and your model service. Two things matter here:
   • Confirm you can reach your chosen model provider reliably from your network environment; a proxy may be required.
   • If you intend to use a provider other than the tool’s default, confirm that the provider offers an endpoint compatible with the tool. Several providers publish compatible interfaces; for example, Baidu AI Cloud and other vendors describe their compatible APIs in their official documentation.
4. Run basic environment checks.
   • Reserve at least 1–2 GB of disk space.
   • Confirm a version control system is installed. AI tools modify files directly, and version control is the most basic safety net: if you dislike a change, you can always roll it back.

3. Installation Option 1: The Official Install Script (Recommended)

The official script detects your operating system and CPU architecture, downloads the matching build, and completes the configuration for you. It does not require a pre-installed runtime or package manager, which makes it suitable for most users.

1. Open a terminal.
   • macOS / Linux: open the system terminal.
   • Windows: work in PowerShell, or enter your WSL environment first.
2. Go to the tool’s official website or official documentation, copy the latest install command from the installation section, and run it. These tools are updated frequently, so always rely on the command currently published by the vendor.
3. Verify the installation: open a new terminal window and run the version command, for example xxx --version (xxx stands for the tool’s actual command name). A version number means the installation succeeded.
4. If you get command not found, the install directory is usually missing from your PATH. Add it as described in the official documentation, or reopen the terminal and try again.

Security note: these commands typically download a remote script and execute it directly, so use only the commands published through official channels. Do not run “one-click install scripts” forwarded in forums or chat groups. In enterprise environments, validate on a test machine first.

4. Installation Option 2: Installing Through a Package Manager

If a common JavaScript runtime is already installed (version 18 or newer is recommended), you can install the tool globally through a package manager.

1. Check the runtime. Run node -v in the terminal. A version number such as v18.17.0 means it is installed; command not found means you should download it from the runtime’s official website. Prefer an LTS (Long Term Support) release for better stability.
2. Install the tool. Run npm install -g @scope/package-name. The -g flag installs globally, so the command works from any directory; @scope/package-name is the package name given in the official documentation.
3. Verify the installation. Run xxx --version as before, and a version number means it worked. You can also run xxx --help to list the available commands and options and get a quick overview of the features.
4. Common problems.
   • EACCES (permission denied): do not simply escalate with sudo, which tends to create messy file ownership. The safer approach is to configure a user-level global directory as described in the package manager’s official documentation, or to use a Node version manager.
   • Multiple runtime versions on the machine: switch with a version manager, and make sure the globally installed package lives under the version you are actually using.

Which Method Should You Choose?

• Install script: effortless, no other environment required, suitable for most people.
• Package manager: easier to manage versions centrally, suitable for developers who already use that runtime.

Either way, record the version number in your team documentation to avoid behavior differences caused by version drift.

5. Editor Plug-ins: Stay in Your Editor

1. Open the editor’s “Extensions” panel.
2. Search for the tool’s name and pick the plug-in published by the vendor. Check that the publisher really is the official account, to avoid imitation or third-party wrapper plug-ins.
3. Click install, then complete the sign-in or authorization as prompted.
4. Open the tool panel in the editor and start a conversation. Some plug-ins reuse the CLI login state, so you may not need to sign in again.

Keep in mind that plug-in capabilities depend on the underlying CLI program. Even if you mainly use the plug-in, it is still worth completing the CLI installation described in Section 3 or Section 4 first.

6. Authentication and Model Configuration

1. Two Authentication Methods

• Account sign-in: run the login command in the terminal, and a browser opens the authorization page automatically. Confirm it and you are ready to go. This suits individual developers.
• API key: create a key in the model provider’s console, then hand it to the tool through environment variables or a configuration file. This suits team collaboration and CI/CD pipelines where no browser can pop up, and it makes usage tracking and management easier.

2. How to Choose a Model

Most tools support several models, and the following rules of thumb work well:

• Default model: balanced overall capability, suitable for most coding tasks.
• Lightweight model: fast responses and low consumption, suitable for explaining code, generating comments, and simple refactors.
• Reasoning-enhanced model: suitable for complex algorithm design and hard-to-diagnose defects.

With an API key, billing is normally based on input and output token usage, and per-model prices differ, so you can estimate cost against the official pricing.

3. Switching to a Third-Party Model

Some tools let you override the model endpoint (base URL) and credentials through environment variables, which makes it possible to connect a third-party model service. Before doing so, confirm that the target service offers a compatible interface; the exact parameters are defined in the tool’s official documentation.

Example (format only — replace with real values):

# Effective for the current terminal session
export ANTHROPIC_BASE_URL="https://your-model-endpoint.example/v1"
export ANTHROPIC_AUTH_TOKEN="your-token"
# Write to the shell profile for permanent effect (append to ~/.zshrc for zsh, ~/.bashrc for bash)
echo 'export ANTHROPIC_BASE_URL="https://your-model-endpoint.example/v1"' >> ~/.zshrc
source ~/.zshrc

If you want to use a service such as Baidu AI Cloud’s Qianfan platform, follow the compatible-interface setup instructions in its official documentation.

7. Project Initialization: Help the Tool Understand Your Project

1. Why Initialization Matters

Without initialization, a tool only sees scattered individual files and knows nothing about the project context: not the tech stack, not the directory conventions, not the coding standards. Code generated from that state tends to look right but behave wrong. The goal of project initialization is to create a project description that is machine-readable and human-understandable, so the AI’s answers match the reality of the project.

2. Three Ways to Generate It

• Automatic: some tools provide an initialization command (such as xxx init) that scans the project and summarizes the tech stack, common commands, and related information into a project-level description file. This is the fastest route.
• Manual: create a description file in the project root (such as AGENTS.md) and fill in a template. This fits cases where you are unhappy with the generated result or the project structure is unusual.
• Reuse a template: the team maintains one standard template, and new projects copy it and adjust, keeping the style consistent.

If the project has several subdirectories (for example frontend, backend, and infrastructure), you can place a description file in each of them to document the conventions for that module in more detail.

3. Project Description File Template

# Project Description
## Overview
E-commerce order service providing core capabilities such as order placement, payment, and refunds.
## Tech Stack
- Language: Python 3.11
- Framework: FastAPI 0.110
- Database: PostgreSQL 15
- Test framework: pytest
## Common Commands
- Install dependencies: pip install -r requirements.txt
- Run locally: uvicorn app.main:app --reload
- Run tests: pytest -q
- Lint: ruff check .
## Directory Structure
- app/api: interface layer
- app/services: business logic layer
- app/models: data models
- tests: unit and integration tests
## Coding Conventions
- Follow PEP 8 and use type annotations
- Public functions must have docstrings
- No print debugging; use the logging module
## Things to Avoid
- Do not modify committed migration files under migrations
- Do not commit configuration files containing keys or tokens
- Every new endpoint must come with unit tests

The template should cover six categories of information:

• What the project does.
• Which technologies it uses.
• How to build and test it.
• Where the code lives.
• Which conventions apply.
• What counts as a no-go zone.

The “no-go zone” section is the easiest to overlook, yet it often has the biggest impact on whether generated code is usable.

4. Scope of Configuration Files

• User-level configuration (in the home directory): personal preferences that apply to every project, such as a preferred model or UI language.
• Project-level configuration (in the project root): project-specific conventions that can be committed and shared with the team.

Follow a single-source-of-truth principle when writing these files: keep each piece of information in exactly one file, so that contradictory files do not confuse the model.

8. First Run and Troubleshooting Common Errors

Running xxx in the project root starts an interactive session (check the official documentation for the exact command name). On first use, verify step by step:

1. Ask the tool to read the project description file and summarize the project structure, and check whether it understood it correctly.
2. Request a small change, such as adding unit tests for one function, and observe whether its file reads and writes and command execution behave as expected.
3. Run diagnostic commands such as xxx doctor, if available, to check installation state and configuration completeness.

Troubleshooting guidance for common issues:

Symptom	Likely cause	Fix
command not found	Not installed, or PATH not updated	Confirm the installation succeeded, add the install directory to PATH, reopen the terminal
Permission denied	Global install directory needs administrator rights	Configure a user-level directory as described in the package manager documentation; do not escalate privileges carelessly
Network connection timeout	Restricted network environment	Configure a proxy or switch to a reliably reachable model service
Authentication failed	Wrong, expired, or exhausted key	Verify the key and check usage and balance in the console
Version too old after install	Package cache not refreshed	Run the package manager’s cache-clean command and reinstall

9. Safe-Usage Practices

1. Understand the permission model. When a tool writes files or runs commands, it normally asks for confirmation first. Common permission policies include “ask every time,” “auto-approve per directory,” and “plan mode” (read-only, no file changes). Start with a stricter policy, and relax it only after the behavior matches your expectations.
2. Protect your credentials. Provide API keys only through environment variables or a local configuration file. Do not hard-code them in source files and do not commit them to the repository. If a key leaks, revoke it in the provider console and generate a new one immediately.
3. Review every change. AI-generated code needs human review, backed by automated tests and static analysis. Never merge it into the main branch directly.
4. Rely on version control. Start from a clean working tree, keep commits small and focused, so problems can be rolled back quickly.
5. Control context and usage. The longer a session’s history grows, the more tokens it consumes. Clear the context when switching tasks; when working with large files, point the tool at the relevant code sections first instead of loading the whole file at once.

10. Final Thoughts

Installation is only the first step. What really determines productivity is three things: using the CLI to connect the full development chain, using a project description file so the AI understands your codebase, and using disciplined processes to hold the line on quality and security. Work through installation, login, and initialization in the order above, then start practicing with one minimal task, and a stable AI-assisted development workflow will follow.