Aider Terminal AI Coding Assistant: Installation and Configuration with Chinese LLMs

1. Overview and Tool Positioning

Aider is an open-source terminal AI coding assistant that lets developers work with intelligent coding capabilities directly inside a shell. Its core strengths include repository-wide code understanding, natural-language-driven code generation, native terminal execution, and Git integration. Typical use cases span rapid prototyping, legacy code refactoring, onboarding on unfamiliar projects, and automated script writing. This article uses Aider as a concrete example to walk through installation, deployment, and the practice of connecting domestic Chinese language models.

2. Environment Preparation and Installation

2.1 Prerequisites: Runtime Environment

Aider runs on Python, so Python 3.9 or higher is required. The recommended version is 3.10 LTS or above.

1. Download the stable release for your operating system from python.org.
2. After installation, verify the version in your terminal:

python --version
pip --version

1. (Optional) If network access to PyPI is limited in your region, switch the pip mirror to a reachable source:

pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

2.2 Installing Aider

Run the following command in your terminal:

pip install aider-chat

Dependencies are pulled automatically. If the network is unstable, use a mirror source or a proxy to speed things up. Verify the installation:

aider --version

A correct version output means Aider is successfully deployed.

2.3 Cross-Platform Notes

The examples below use Windows 11. macOS and Linux users can follow the same steps with a few adjustments:

• Node.js requirement: Although Aider’s main runtime is Python, some plugins need Node.js ≥ 18 LTS. Install it in advance.
• Path and syntax differences: Linux/macOS use / as a path separator and export for environment variables rather than set.
• Permissions: A global install on Linux/macOS may need sudo or the --user flag. If you hit a permission error, run:

sudo chown -R $(whoami) ~/.local
pip install aider-chat --user

3. Connecting to Chinese Large Language Models

3.1 Background and Approach

By default, Aider connects to overseas API endpoints such as OpenAI, which can be difficult for developers in China due to network restrictions. There are two common approaches: using a compliant API proxy to forward requests, or replacing the underlying model in the configuration with a Chinese LLM service. The key is to set Aider’s environment variables or config file so it points to a domestically reachable API endpoint.

Note: When using a proxy, be aware of data cross-border compliance risks. Prefer direct domestic connections where possible.

3.2 Configuration File Location and Structure

Aider manages model parameters through environment variables and config files. A common pattern is to create a .env file in the project root or set system-level environment variables. Below are reference configurations for Qwen (DashScope), Zhipu GLM, and DeepSeek.

Example 1: Qwen (DashScope)

export AIDER_MODEL=qwen-max
export DASHSCOPE_API_KEY=your-dashscope-key-here

Example 2: Zhipu GLM

export AIDER_MODEL=glm-4
export ZHIPUAI_API_KEY=your-zhipu-key-here
export OPENAI_API_BASE=https://open.bigmodel.cn/api/paas/v4

Example 3: DeepSeek

export AIDER_MODEL=deepseek-chat
export DEEPSEEK_API_KEY=your-deepseek-key-here
export OPENAI_API_BASE=https://api.deepseek.com

⚠️ Critical reminder: Never hard-code API keys in config files. Use environment variables or a .env file, and make sure .gitignore excludes .env and any sensitive config files. You can load .env via the python-dotenv library or export variables before launching Aider.

Note that field names and endpoint URLs may change per platform—always cross-check the latest platform documentation.

3.3 Chinese LLM Compatibility Considerations

Platform	Context Window	Compatible Protocol	Billing	Notes
Qwen (DashScope)	128K tokens	OpenAI-compatible	Per-token	Requires a DashScope key
Zhipu GLM-4	128K tokens	OpenAI-compatible	Per-token	Endpoint: open.bigmodel.cn
DeepSeek-V3	64K tokens	OpenAI-compatible	Per-token	Cost-effective for high-frequency calls
Baidu ERNIE	8K–128K tokens	Proprietary + proxy	Per-call/token	Requires Baidu AI Cloud access

When choosing a Chinese model, keep the following in mind:

1. API compatibility: Most platforms offer an OpenAI-compatible interface, but parameter names may differ slightly—verify whether extra adaptation is needed.
2. Context window limits: For large codebases, check whether the model’s maximum token count meets your needs; process in segments if necessary.
3. Authentication: Key formats vary across platforms—configure according to each provider’s official docs.
4. Cost and rate limits: Chinese models typically have call-frequency caps. Monitor usage during development to avoid overages.

3.4 Verifying the Connection and Troubleshooting

After configuration, start Aider in your project directory and try a simple prompt:

cd your-project-folder
aider --model qwen-max

If Aider returns valid generated code, the connection is working. If not, consult the error reference:

Error Code	Meaning	What to Check
401	Unauthorized	Verify the API key is correct and not expired
403	Forbidden	Confirm the account has access to the requested model
429	Too Many Requests	Reduce call frequency or upgrade the plan
504	Gateway Timeout	Check network connectivity and proxy settings

You can quickly test API reachability with curl:

curl -s -o /dev/null -w "%{http_code}" https://dashscope.aliyuncs.com/compatible-mode/v1/models \
  -H "Authorization: Bearer your-dashscope-key-here"

A 200 response means the endpoint is reachable.

4. Usage Workflow and Hands-On Example

4.1 Initial Launch

Navigate to your project root and run:

cd your-project-folder
aider

On the first run, Aider will confirm the directory is inside a Git repository, let you pick a Dark or Light theme, and verify directory access. After that, you can start a conversation.

4.2 Hands-On Example: Building a Web Mini-Game

User prompt:

Please help me write a browser-based matching game using pure front-end technologies, with a timer and scoring.

What Aider does:

• Analyzes the request and plans the HTML/CSS/JS file structure.
• Generates complete source files (index.html, style.css, game.js) including board rendering, matching logic, timer, and score tracking.
• You can open index.html directly in a browser to preview the result.

Generated file layout:

your-project-folder/
├── index.html
├── style.css
└── game.js

4.3 Advanced Tips

• Precise prompts: Specify the tech stack (e.g., “pure HTML5 Canvas”), feature scope, and performance constraints to improve output quality.
• Iterative refinement: Break complex tasks into multiple conversation turns—build the skeleton first, then add details.
• Code review: Prompt Aider with “Please review this code for security and performance” to leverage its understanding capabilities.
• Git integration: When satisfied with generated code, type /commit and Aider will auto-generate a commit message and complete the local commit.

5. Security and Best Practices

1. API key management: Always store keys in environment variables or a .env file. Never hard-code or commit them to version control.
2. Code review: AI-generated code must be manually reviewed before merging into production branches to prevent security issues.
3. Network security: If using a proxy, ensure the channel is encrypted and the source is trustworthy; be mindful of data cross-border compliance.
4. Privacy: Do not paste sensitive business data, passwords, or personal information into prompts.

6. Summary

This guide uses Aider as a concrete example to provide a complete, verifiable path from Python environment setup and tool installation to configuring Chinese LLMs (Qwen, Zhipu GLM, DeepSeek) and hands-on usage. Developers should adapt the approach to their own network conditions, manage keys strictly via environment variables, and use the troubleshooting reference to resolve issues quickly. For deeper learning, visit the official Aider repository on GitHub.