Aider: AI Pair Programming in Terminal

Aider is a lightweight AI pair‑programmer that lives right inside your terminal. It watches the files you edit, suggests code, runs tests, and even writes documentation—all without leaving the command line you already love. By turning the terminal into an interactive coding assistant, Aider bridges the gap between traditional REPL workflows and modern LLM‑driven development. In this article we’ll explore how Aider works, set it up, and walk through real‑world examples that showcase its power.

What makes Aider different?

Most AI coding tools require a web UI or a heavyweight IDE plugin, which can disrupt your flow. Aider, on the other hand, is a pure‑terminal experience that integrates with Git, your favourite editors (vim, nano, VS Code – via the terminal), and any language you throw at it. It keeps a persistent conversation history in a hidden .aider.chat file, so you can pick up where you left off days later. Moreover, Aider respects your project’s context: it reads the repository, the current branch, and even the open diff before generating suggestions.

Because Aider runs locally, you stay in control of your data. You can point it at a private LLM endpoint, an OpenAI API key, or even a self‑hosted model like Llama 3. This flexibility makes it suitable for enterprise environments where data compliance is a must.

Key design principles

• Terminal‑first: No GUI, no browser, just the shell you already use.
• Context‑aware: Reads the entire repo, recent commits, and open files.
• Iterative: You can ask follow‑up questions, request refinements, or roll back changes.
• Language‑agnostic: Works with Python, JavaScript, Rust, Bash, and more.

Getting started with Aider

Installation is a single pip install aider-chat command for most platforms. After installing, you need to configure your API key (or endpoint) so Aider can talk to the LLM. The simplest setup looks like this:

pip install aider-chat
export OPENAI_API_KEY=sk-*************
aider --help

Running aider without arguments opens an interactive session that automatically detects the current Git repository. If you’re not inside a repo, Aider will still work, but you’ll miss out on diff‑aware suggestions.

To make Aider launch from your favourite editor, add the following alias to your shell configuration:

alias aid='aider --edit-mode vim'

Now, typing aid in any project folder opens a vim session where you can edit files, and Aider will respond inline with suggestions.

Core features you’ll use daily

1. In‑place code generation – Ask Aider to write a function, and it will insert the code directly into the file you’re editing. You can also request a specific style, such as type hints or docstrings.

2. Test‑driven assistance – Prompt Aider with “write unit tests for this module,” and it will generate pytest files that align with your existing codebase.

3. Refactoring on demand – Tell Aider to “extract this block into a helper function,” and it will rewrite the code while preserving imports and formatting.

4. Documentation generation – Aider can produce README sections, function docstrings, or even API reference markdown based on your source files.

How Aider communicates

Under the hood, Aider builds a prompt that includes a snapshot of the repository, the current diff, and the conversation history. It then sends this prompt to the LLM and parses the response, looking for fenced code blocks or inline suggestions. The result is applied to your files, and you get immediate feedback via the terminal UI.

Pro tip: Use the --dry-run flag the first few times you experiment. It shows you what changes Aider would make without actually writing to disk.

Practical example: Building a CLI todo app

Let’s walk through a hands‑on scenario where Aider helps you scaffold a small Python CLI todo manager. The goal is to end up with a todo.py script that supports add, list, and remove commands, persisting data in a JSON file.

First, create an empty project folder and initialise Git:

mkdir cli-todo && cd cli-todo
git init
touch todo.py
aid

Inside the Aider session, type the following prompt:

Please create a Python CLI script named todo.py that uses argparse. It should store tasks in tasks.json and support add, list, and remove commands.

Aider responds with a complete todo.py implementation, inserts it into the file, and even adds a minimal tasks.json placeholder. Review the generated code, run it, and you’ll see a working CLI.

Now, let’s ask Aider to add type hints and improve error handling:

Add type annotations to all functions and make the script exit gracefully if tasks.json is missing or corrupted.

Aider updates the file in place, adding from typing import List, Dict and wrapping JSON loading in a try/except block. The changes are immediately testable.

Running the app

python todo.py add "Buy milk"
python todo.py list
python todo.py remove 1

Each command works as expected, demonstrating how Aider can accelerate the entire development cycle—from scaffolding to polishing.

Advanced use case: Refactoring a legacy codebase

Imagine you inherited a monolithic Flask application with mixed concerns and no type hints. You want to extract the authentication logic into a dedicated module and add Pydantic models for request validation.

Start an Aider session at the root of the repo and ask:

Extract the login route into a new file auth.py, add type hints, and replace the current request parsing with a Pydantic model called LoginRequest.

Aider creates auth.py, moves the route function, adds the Pydantic model, and updates imports in app.py. It also modifies the unit test file to import the new model.

Because Aider works with the Git diff, you can review the changes with git diff before committing. If something looks off, simply ask for a revision:

The error handling in auth.py should return a 400 JSON response instead of raising an exception. Fix it.

Aider adjusts the code, adds a Flask abort call with a JSON payload, and updates the test expectations. This iterative loop feels like having a senior developer sitting beside you, ready to refactor on demand.

Generating tests automatically

After the refactor, you can ask Aider to generate a test suite for the new authentication module:

Write pytest tests for auth.login that cover successful login, invalid credentials, and missing fields.

Aider produces a tests/test_auth.py file with three parametrised test cases, complete with fixtures for a test client. Running pytest -q shows all tests passing, confirming that the refactor didn’t break existing behaviour.

Integrating Aider with existing toolchains

Many teams already use CI pipelines, pre‑commit hooks, and code formatters like Black. Aider can slot into these workflows without friction. For example, you can create a pre‑commit hook that runs Aider in --dry-run mode to suggest missing docstrings before each commit.

# .git/hooks/pre-commit
#!/usr/bin/env bash
aider --dry-run --check-docstrings || {
  echo "Aider suggests adding docstrings. Fix them before committing."
  exit 1
}

In CI, you might use Aider to auto‑generate changelog entries based on merged PRs. A simple script can invoke Aider with the list of changed files and append the generated markdown to CHANGELOG.md.

changed=$(git diff --name-only HEAD~1 HEAD)
aider --dry-run --changelog "$changed" >> CHANGELOG.md

Because Aider’s output is deterministic given the same prompt and model, you can version‑control the generated snippets and review them in pull requests.

Pro tips for power users

Tip 1 – Use custom prompts. Store frequently used instructions in a .aider_prompt.txt file and pass it with --prompt-file. This saves typing and ensures consistency across the team.

Tip 2 – Leverage the --no-branch flag. When working on a detached HEAD (e.g., in a CI job), disable branch‑aware suggestions to avoid confusing diffs.

Tip 3 – Combine with tmux. Run Aider in a split pane while your editor occupies another pane. The side‑by‑side view mimics a modern IDE chat window but stays fully terminal‑based.

Conclusion

Aider transforms the terminal from a passive command executor into an active coding partner. By keeping the conversation context‑aware, language‑agnostic, and fully controllable, it fits naturally into both solo projects and large‑scale enterprise pipelines. Whether you’re scaffolding a quick script, refactoring legacy code, or automating documentation, Aider’s iterative workflow accelerates development while preserving the simplicity of the command line. Give it a spin in your next repository—you’ll quickly wonder how you ever coded without an AI pair‑programmer right at your fingertips.