Installing a Terminal AI Coding Assistant from Scratch and Connecting It to an LLM

1. What Is a Terminal AI Coding Assistant?

A terminal AI coding assistant is an AI development tool that runs in a local command line. A web-based Q&A tool can only see what you paste into the chat box. A terminal assistant can read files in the current project directory, execute commands after you authorize it, analyze project structure, and write changes directly into the project.

You can think of it as a collaborator working inside your development environment. Typical use cases include:

• Analyzing the current project directory structure and explaining each module’s responsibilities;
• Adding a user login API to an existing backend project;
• Tracing the call chain of an exception and proposing a fix;
• Adding unit tests for a specified module;
• Checking configuration files for potential security risks;
• Modifying multiple related files at the same time based on a requirement.

The core value of this type of tool is shortening the loop of “understand the project, modify code, run verification, and locate problems.” It usually needs to call a remote large language model service for inference, so before use you need to prepare a local runtime environment and configure an available model API.

At the same time, note that the assistant can read files, modify files, and even execute commands. It is not a fully automated black-box program. Users should define authorization boundaries in advance and personally confirm important operations before they are executed.

2. Environment Preparation Before Installation

1. Open a Terminal

Every major operating system includes a terminal program:

• Windows: Open “Terminal” (Windows Terminal) or “Command Prompt”; PowerShell is recommended;
• macOS: Open the “Terminal” application;
• Linux: Open the terminal emulator provided by your distribution.

Beginners only need a few basic commands to complete most operations in this tutorial:

# Show the current directory (also works in Windows PowerShell)
pwd
# List files and subdirectories in the current directory (use dir on Windows if needed)
ls
# Enter the project directory (replace your-project with your project folder name)
cd your-project

Make sure your current account has read and write permissions for the project directory, and that the computer can access the network normally.

2. Install Node.js

Many terminal AI coding tools run on Node.js, so you need to install it first. Choose the LTS (Long Term Support) version rather than an experimental or beta release. For the exact minimum version requirement, refer to the tool’s official documentation. A common current requirement is Node.js 18 or later.

The official download page is https://nodejs.org. Windows users can also install it with winget in PowerShell:

winget install OpenJS.NodeJS.LTS

After installation, reopen the terminal and run the following check commands:

node --version
npm --version

Both commands should return version numbers. If you see “command not found” or “not recognized as a command,” troubleshoot in this order:

1. Confirm that Node.js is installed and that the installation completed without errors;
2. Close all terminal windows and open a new one so the new environment variables take effect;
3. Check whether PATH includes the Node.js installation directory. In Windows PowerShell, run $env:Path -split ';'; on macOS or Linux, run echo $PATH.

3. Install Git

The version control tool Git helps you save a backup point before the assistant makes bulk code changes. If something goes wrong, you can use it to quickly revert changes.

• Windows: Run winget install Git.Git in PowerShell;
• macOS: Run xcode-select --install, or download the installer from https://git-scm.com;
• Linux (Debian or Ubuntu): Run sudo apt install git.

Check the version after installation:

git --version

Before first use, you also need to set committer information. It is stored only locally and does not affect how the assistant runs:

git config --global user.name "Your Name"
git config --global user.email "your@email.com"

3. Install the Terminal AI Coding Assistant

1. Choose an Installation Method

Method	Suitable for	Main characteristics
Official install script	Beginners who want the fewest steps	The official script handles download and configuration, but you must verify the source first
Package manager installation	Users already familiar with npm	Version control is easier, and upgrades and uninstalls are convenient
Tool’s default service login	Users of officially hosted models	Simplest configuration, but depends on account permissions and regional availability

Below, assistant represents the assistant’s command name. In actual use, replace it with the command name in the official documentation. If the command name is different, you only need to replace that one word; the other steps remain the same.

2. Option 1: Use the Official Install Script

First open the tool’s official documentation and confirm the installation command and script URL. Do not copy commands from search results or unfamiliar forums. Before executing, it is recommended to download the script locally and read it first. For example:

# Replace SCRIPT_URL with the URL given in the official documentation
curl -fsSL SCRIPT_URL -o install.sh
less install.sh
bash install.sh

In Windows PowerShell, you can replace curl with Invoke-WebRequest and use the PowerShell installation command given in the official documentation. Do not use administrator privileges during installation unless the official documentation explicitly requires it.

After installation, close and reopen the terminal, then check the version:

assistant --version

If it returns a version number, the command is ready to use.

3. Option 2: Install Globally via npm

If Node.js and npm are already installed, you can install with the package manager. Replace OFFICIAL_PACKAGE_NAME with the package name given in the official documentation:

npm install -g OFFICIAL_PACKAGE_NAME

Before installing, you can check the global installation directory:

npm config get prefix
npm ls -g --depth=0

Common issues and handling methods:

• Insufficient permissions (macOS or Linux): Do not habitually add sudo. First run npm config get prefix to check the installation directory. If the directory does not belong to the current user, set a user-level directory instead of using administrator privileges long term;
• Network timeout: Run npm config get registry to check the current registry, and confirm that it is the official registry or a mirror approved by your organization;
• Command not found: Check whether the bin subdirectory under the directory returned by npm config get prefix is in PATH;
• Version conflict: Run npm uninstall -g OFFICIAL_PACKAGE_NAME to uninstall the old version, reinstall, and confirm the current version with assistant --version.

4. Recommendations Before the First Run

After a successful installation, do not start the assistant directly in the disk root or your home directory. First create a test project directory, for example:

mkdir assistant-test
cd assistant-test
git init
assistant

Familiarizing yourself with its behavior in a test directory is safer than experimenting directly in an important project.

4. First Launch and Project Authorization

On first launch, the assistant usually requests access to project files, command execution, or a model service connection. Do not allow everything at once; authorize item by item based on the current task.

1. Only Expose Necessary Directories

If the task involves only one project, start the assistant in that project’s root directory. Do not use a directory that contains password files, personal documents, or other projects as the workspace. For example, your home directory may contain .ssh, browser data, or financial spreadsheets; none of these should be exposed to the assistant.

2. Require Confirmation for High-Risk Operations

Deleting files, making broad code changes, performing database operations, installing dependencies, and accessing external networks are all high-risk operations. It is recommended to enable manual confirmation in the tool settings. You can also state boundaries directly to the assistant, for example:

For this task, you may only modify the src/auth directory. Do not delete any files or install new dependencies. For any database or network request, list the command first and wait for my confirmation before executing it.

If the assistant proposes an operation beyond the authorized scope, you can reply:

This step is outside the scope of this authorization. Stop execution and provide only your suggested changes; I will decide whether to run them.

3. Review Diffs Before Changes

Review the assistant’s generated changes before running tests. When using Git, you can run:

git status
git diff

Focus on these five checks:

• Whether configuration files were modified by mistake;
• Whether existing business logic was overwritten;
• Whether unnecessary dependencies were introduced;
• Whether secrets were written into code;
• Whether files unrelated to the current task were modified.

5. How to Connect to an LLM Service

The terminal assistant uses a model API to perform code analysis and generation. There are roughly two types of integration.

1. Use the Tool’s Default Supported Service

Some tools require login on first launch or ask you to enter access credentials. After authentication, the tool automatically uses the default model. This configuration is simple. However, you need to confirm that the service is available in your current network environment and understand the account’s usage limits, access regions, and permission scope.

2. Connect to a Compatible LLM API Service

If the tool supports a generic API protocol, you can connect it to a model service that conforms to that protocol. Such a service can be an internally deployed model gateway or an LLM service provided by a cloud platform such as Baidu AI Cloud. For specific parameters, refer to the latest documentation of the corresponding platform.

A generic configuration usually includes three pieces of information: the API endpoint, the access key, and the model name. The variable names below are only for explaining the configuration approach. The actual variable names should follow the documentation of the tool you use.

Temporary setup on macOS or Linux:

export MODEL_API_ENDPOINT="Replace with the API root endpoint provided by the platform"
export MODEL_API_KEY="Replace with your access key"
export MODEL_NAME="Replace with the model name available on the platform"

Temporary setup in Windows PowerShell:

$env:MODEL_API_ENDPOINT = "Replace with the API root endpoint provided by the platform"
$env:MODEL_API_KEY = "Replace with your access key"
$env:MODEL_NAME = "Replace with the model name available on the platform"

If you want to save them persistently, Windows users can run the following command in PowerShell. It writes the variable to the current user’s environment variables, and you need to reopen the terminal afterward:

[Environment]::SetEnvironmentVariable("MODEL_API_KEY", "Replace with your access key", "User")

In Windows Command Prompt (cmd), the syntax is setx MODEL_API_KEY "Replace with your access key".

3. Check Four Items During Integration

• API endpoint: Confirm whether it is the API root endpoint required by the compatible protocol. Some services require the root endpoint, while others require the full call path; the two cannot be mixed;
• Authentication method: Check whether the key is valid and whether additional authentication headers are required;
• Model name: The name must exactly match the model actually available on the server, including case and version number;
• Context capability: Check whether the model can handle the amount of code in the current project.

For context capability, you can first make a simple estimate. Record the number of files to be read and the total number of lines, then compare that with the context length described in the model documentation. If it exceeds the limit, split the task into several smaller tasks and read one module at a time instead of reading the entire repository at once.

4. Make Sure Keys Do Not Enter Version Control

Keys must have restricted access and must not be committed to a remote repository. Run the following commands to check for risks:

# Check whether the repository already tracks configuration files containing keys (replace .env with the file name you actually use)
git ls-files | grep -E "\.env$|secrets"
# Check whether key variables are written directly into code (if a real key appears in the output, remove it immediately)
git grep -n "MODEL_API_KEY"
# Confirm that the key file is ignored
git check-ignore -v .env

If the third command produces output, the file is ignored and can be safely stored locally. If there is no output, add the file to .gitignore. If the key has already been committed, revoke it on the platform immediately and generate a new one; simply deleting the file is not enough.

On macOS or Linux, you can also restrict read permissions for the configuration file:

chmod 600 .env

6. Verify Whether the Model Is Connected Successfully

After configuration, do not immediately ask the assistant to modify a complex project. First run a low-risk test:

Read the current directory structure and output only the analysis. Do not modify any files.

If the assistant returns the directory structure and the general purpose of each directory, it already has file access capability. You can compare the result with the output of ls to confirm whether it is complete.

Then test model invocation:

Explain the startup flow of the current project and identify possible configuration entry points.

During verification, focus on the following:

• Whether the assistant returns content normally;
• Whether the returned content matches the actual project, for example, whether the mentioned files really exist;
• Whether authentication failure messages appear;
• Whether an API endpoint error is reported;
• Whether timeouts occur frequently, or a context limit is reported.

Only after basic conversation is stable should you proceed to code modification tasks.

7. Common Troubleshooting

1. Command Not Found

Run node --version, git --version, and assistant --version in order to find which one fails. Then check PATH and reopen the terminal.

2. Authentication Failure

First confirm that the key has no extra spaces or line breaks. You can check the variable length with the following commands instead of printing the key in plaintext:

# macOS or Linux
echo ${#MODEL_API_KEY}
# Windows PowerShell
$env:MODEL_API_KEY.Length

If the length is 0, the variable has not taken effect. If the length is a few characters longer than the key length stated in the official documentation, spaces were usually included when copying. Also confirm that the key has not expired and that the current account has model invocation permission. Do not paste the full key into public logs or issue reports.

3. API Endpoint Error or Connection Timeout

Use the following commands to check whether the network can reach the API endpoint. Replace MODEL_API_ENDPOINT with the address you actually use:

# macOS or Linux
curl -I "$MODEL_API_ENDPOINT"
# Windows PowerShell (use curl.exe, not the PowerShell alias)
curl.exe -I $env:MODEL_API_ENDPOINT

If an HTTP status code is returned, the network is basically reachable. If there is no response for a long time, check the local proxy settings and try switching networks. If proxy settings exist in environment variables, check them with echo $HTTPS_PROXY or $env:HTTPS_PROXY.

4. Slow Response or Context Limit Reached

This is usually related to network quality, model load, oversized request content, or proxy configuration. You can first narrow the task scope, for example, change “analyze the entire repository” to “analyze the login flow in the src/auth directory,” and reduce the amount of code read at one time.