Aider: Terminal-Based AI Pair Programming Guide
Artificial intelligence is reshaping how developers approach their daily work. Yet not all AI coding tools are built with the same philosophy. Many focus on speed and convenience, generating inline suggestions that accelerate small tasks. Aider takes a fundamentally different approach. It is designed not as a typing assistant, but as a structured collaborator that operates at the scale of an entire repository.
Running directly inside the terminal, Aider works where many developers are already most productive. Its strength lies in understanding the full context of a project and executing coordinated, multi-file changes based on high-level instructions. Instead of offering fragmented suggestions, it reads the codebase, builds a map of its structure, and applies edits that reflect architectural intent. This makes it particularly effective for feature-sized implementations and complex refactors.
Control is central to its design. Through deep Git integration, every change is atomic, reviewable, and reversible. This ensures that AI-assisted development remains transparent and manageable. Developers retain full oversight, with the ability to inspect diffs, accept modifications, or roll back instantly. The result is a workflow that combines automation with discipline rather than replacing one with the other.
This article examines Aider from a practical perspective. It covers installation, configuration, and connecting to advanced Large Language Models such as DeepSeek and Claude. More importantly, it demonstrates real-world usage through two substantial examples: implementing a complete authentication system in a Python Flask application and performing a coordinated multi-file refactor in a React and TypeScript project to introduce dark mode and API synchronization. By the end, you will see how Aider fits into a professional development workflow and how it can be used to execute meaningful, production-ready changes with confidence.
Understanding Aider: a new paradigm for AI coding
Before diving into the command line, it's crucial to understand what makes Aider different from other AI coding tools. Its unique architecture and design philosophy are what enable it to handle tasks that are far beyond the scope of simple autocompletion.
Beyond autocomplete: the power of repo-wide edits
The fundamental distinction of Aider is its scope. Most AI coding assistants operate on a single file at a time. They analyze the immediate context of your cursor and suggest snippets of code. This is incredibly useful for boilerplate, simple functions, or completing familiar patterns. However, their awareness is limited. They often don't understand how a change in one file will impact another, which makes them unsuitable for tasks like large-scale refactoring, adding new features that span multiple components, or updating dependencies across an entire project.
Aider tackles this challenge head-on. When you initiate a session, Aider doesn't just look at one file; it builds a "repository map." Using a sophisticated parsing technology called tree-sitter, it analyzes the structure of all the files you've added to the chat context. It identifies class definitions, function signatures, import statements, and other key structural elements. This map provides the LLM with a bird's-eye view of your project, allowing it to reason about the codebase holistically. When you ask it to "add a user profile page," it knows it needs to create a new route, a new template, a database model, and update the navigation bar in a separate layout file—and it can execute all those changes in a single, coordinated operation.
Git-native for safety and control
One of the biggest anxieties developers have with AI tools is the fear of losing control. An AI making uncontrolled, sweeping changes to a codebase can feel chaotic and dangerous. Aider's creators understood this and built the entire workflow around Git, the version control system that developers already know and trust.
This "Git-native" approach manifests in several key ways:
Atomic Commits: Every change or set of changes that Aider successfully applies is automatically committed to your Git repository with a descriptive commit message. This creates a clean, chronological history of the AI's work, completely separate from your own manual commits.
Effortless Reversibility: If Aider makes a change you don't like or if an edit introduces a bug, you don't have to manually hunt down every modified line. You can simply type the /undo command in the Aider chat, and it will instantly revert the last commit, returning your codebase to its previous state.
Clean Workflows: Aider is designed to work with your existing Git workflow. If you have uncommitted changes (a "dirty" working directory), Aider will first prompt you to commit them with a descriptive message before it starts its own work. This ensures that your work and the AI's work are never confusingly mixed in the same commit.
This tight integration transforms Aider from a black box into a transparent and reliable partner. You always have a safety net, making it possible to confidently delegate even the most complex tasks to the AI.
LLM flexibility and remarkable performance
Aider is not tied to a single AI model. It acts as a sophisticated orchestration layer that can connect to a variety of the industry's leading LLMs. This includes models from OpenAI (like GPT-4), Anthropic (Claude 3), and DeepSeek. This flexibility allows you to choose the model that best fits your needs, whether you prioritize raw reasoning power, speed, or cost-effectiveness. Furthermore, through integrations like Ollama, you can even run powerful open-source models locally for complete privacy and zero cost.
The project's own benchmarks are a testament to its effectiveness. Aider's documentation features a leaderboard that tests various LLMs on a challenging set of 225 coding exercises across multiple languages. The top models consistently achieve success rates approaching 90%. Even more impressively, the Aider project itself is largely self-developed; its release history shows that for many recent versions, over 80% of the new code was written by Aider itself, a practice known as "dogfooding" that proves the tool's real-world viability.
Installing and configuring Aider
Understanding the principles behind Aider sets the foundation for practical use. The installation process is remarkably simple, as it's just a single command-line interface (CLI) tool.
Installing the Aider CLI
Aider provides a convenient installation script that handles everything. Open your terminal and run:
This command downloads and executes a shell script that installs the aider binary on your system, making it available in your path. Once it's finished, you can verify the installation by checking its version:
This should output the installed version number, confirming that the tool is ready to use.
Connecting to a Large Language Model
Aider is the "brain" that directs the work, but it needs an LLM to provide the coding intelligence. You'll need an API key from a supported provider. While you can use OpenAI or Anthropic, this example uses the DeepSeek Coder model, which offers a powerful balance of performance and affordability.
Navigate to the DeepSeek platform and sign up to get your API key. Once you have your key, you can launch Aider for the first time while configuring it to use the DeepSeek model:
This command specifies which LLM to use (--model deepseek/deepseek-coder) and provides your credentials (--api-key). On this first run, Aider will save your API key to a local configuration file (~/.aider.conf.yml), so you won't need to enter it again for future sessions.
Initializing Aider in your project
The first time you run Aider within a project directory, it performs a quick, one-time setup. It will ask if you want to add Aider-related files (like .aider.env and .aider.chat.history.md) to your project's .gitignore file. It's highly recommended to accept this. It prevents your chat history and configuration from being accidentally committed to your repository.
If your directory is not already a Git repository, Aider will offer to initialize one (git init). Since Aider's entire workflow depends on Git, you should allow this.
Once these steps are complete, you will be greeted by the Aider chat prompt, ready to accept your commands. The terminal will display information about the current session, including the model in use, the Git repository status, and the repo-map details.
Building a Flask user authentication system
Putting Aider to the test with a common and non-trivial task demonstrates its capabilities. The challenge: adding a secure user registration and login system to a web application. The starting point is a basic Python Flask app with just a homepage and a contact form.
Adding project files to the chat context
Before asking Aider to make changes, you need to tell it which parts of the codebase are relevant. This is done with the /add command.
First, add the main application file:
Next, add the directories containing your HTML templates and CSS files. Aider can ingest entire directories at once:
After each /add command, Aider will confirm that the files have been added to the chat and are now part of its context map.
Crafting a detailed, high-level prompt
The quality of Aider's output is directly proportional to the quality of your input. Vague requests lead to vague results. A good prompt is specific, provides context, and clearly defines the desired outcome.
Here is an excellent prompt to request the authentication feature:
Add secure user authentication to my Flask app using Flask-Login and bcrypt for password hashing. The current setup is a basic Flask app without any auth. Assume a simple structure with the app.py, possibly a database (use SQLite if none exists), and basic routes. (Add tests for these too)
This prompt is effective because it specifies technologies (explicitly names Flask-Login for session management and bcrypt for secure password hashing), defines the data store (suggests using SQLite if a database isn't already present), describes the current state (mentions that the app is currently "basic" and "without any auth"), and includes a critical requirement (explicitly asks for the addition of tests).
Reviewing and approving Aider's plan
After submitting the prompt, Aider will communicate with the LLM to formulate a plan. It will then present this plan to you as a series of proposed file edits and creations. Aider will propose to create requirements.txt (to add new dependencies like Flask-Login and bcrypt), create models.py (to define the User database model), create new templates (login.html, register.html, and profile.html), create test_auth.py (a new file for the requested tests), modify app.py (to add the new routes, configure Flask-Login, and handle form submissions), and modify templates/base.html (to add conditional navigation links).
For each new file, Aider will ask for your confirmation. For each modification, it will show you a clear diff format, highlighting the exact lines to be added or removed.
Applying changes and finalizing
Once you have approved all the proposed changes, Aider will apply them to your files. But it doesn't stop there. It also recognizes that code changes often require subsequent actions. In this case, it suggests the necessary shell commands to get the new system running:
After running these commands, the simple website is transformed. It now has a fully functional and secure user authentication system, complete with registration, login, logout, and profile pages—all generated from a single prompt in under a minute.
Advanced refactoring of a React app
For a different stack (React and TypeScript) and a more complex task, the goal is to take a simple to-do list application and add two major features: a dark mode toggle with persistence using localStorage, and API synchronization to fetch and update to-dos from a remote server.
This kind of task is a perfect showcase for Aider, as it requires coordinated changes across CSS, component logic, state management, and utility functions.*
Planning with Architect Mode
For a complex refactor, blindly asking the AI to "just do it" can be risky. A better approach is to first have the AI generate a high-level plan. Aider facilitates this with its Architect Mode.
Add the relevant project files to the context (/add src/). Invoke Architect Mode by starting your prompt with /architect:
/architect Plan a multi-file refactor to add dark mode (Tailwind dark variant, theme toggle button with localStorage persistence) and API sync (fetch/save data like todos from JSONPlaceholder API using fetch or axios). Use TS React with Tailwind.
Aider will not write any code. Instead, it will produce a detailed, step-by-step plan outlining all the necessary changes, such as adding dependencies (axios, tailwindcss), updating tailwind.config.js, creating new files for a theme hook (hooks/useTheme.ts), a to-dos hook (hooks/useTodos.ts), an API client (api/client.ts), and new UI components, plus detailing the changes needed in App.tsx to integrate the new hooks and logic.
This allows you to review and validate the AI's proposed approach before a single line of code is touched.
Implementation with Code Mode
Once you're satisfied with the plan, you can switch to Code Mode to execute it:
/code Now, please implement the plan we just discussed.
Aider will now proceed with the implementation, presenting each file creation and modification for your approval, just as it did in the Flask example. It will intelligently create custom hooks for managing theme state and API data, encapsulate API logic in a dedicated client file, and refactor the main component to be cleaner and more maintainable.
Generating tests for the new logic
With the features implemented, the final step is to ensure they are robust:
Generate unit/integration tests for the new API sync logic (e.g., fetch Todos, saveTodo in src/api/client.ts) and dark mode toggle. Use Vitest or Jest with React Testing Library. Cover success/failure cases.
Aider will create the necessary test files (.test.ts), mock API calls using libraries like vi.mock, and write assertions to verify that components render correctly, state updates as expected, and dark mode classes are applied to the DOM. If the tests fail initially, Aider's iterative nature shines. You can provide it with the error message and ask it to fix the failing test, demonstrating a powerful test-driven development (TDD) loop.
After approving the final test-related changes, the refactor is complete. The application now has a polished dark mode and robust data persistence, all achieved through a structured, reviewable, and test-covered process.
Final thoughts
In a rapidly expanding ecosystem of AI development tools, Aider stands out as a focused and purpose-built solution. It is not designed to predict every keystroke, and it is not a one-click application generator. Instead, its real value lies in serving as a structured AI collaborator for clearly scoped features and multi-file refactoring efforts.
What makes this positioning effective is the balance it strikes between power and control. Aider’s repository-wide context awareness, Git-centered safety model, and support for multiple LLMs work together to create a workflow that is both capable and disciplined. Rather than generating isolated snippets, it operates with a clear understanding of the broader codebase while ensuring every change is versioned and reviewable.
This becomes especially evident in practical scenarios. For example, when adding a complete authentication system to a Flask application or performing a large-scale refactor in a React project entirely through conversational prompts in the terminal, the benefits shift from theoretical to tangible. The process remains iterative, transparent, and aligned with established development best practices.
Importantly, Aider does not attempt to replace the developer. Instead, it amplifies developer effectiveness. By automating repetitive edits across multiple files, generating boilerplate, and assisting with test creation, it removes much of the mechanical effort that slows complex implementations.
As a result, developers can redirect their attention toward higher-level concerns such as architecture, system design, and solving nuanced technical challenges. For those who are comfortable in the command line and value a structured, review-driven workflow, Aider represents a meaningful productivity multiplier. If your goal is to enhance your capabilities while maintaining full control over your codebase, bringing Aider into your terminal is a logical and forward-looking step.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.