Developers who have started using this terminal-based assistant have quadrupled their coding productivity. I spent a full month using this open-source tool in projects of all types and found that it deserves the buzz it's getting in the development community.

This AI assistant works with nine popular programming languages including Python, JavaScript, and Rust. Aider AI is budget-friendly too - it processes files for just $0.007 each. The aider chat interface handles automatic code testing and voice-commanded feature requests. It also blends naturally with Git to make version control simple.

I tested this AI coding assistant extensively with Claude 3.7 Sonnet and GPT-4o on complex projects and daily coding tasks. This hands-on review shares my four-week experience with Aider to help you decide if it belongs in your development toolkit.

Week 1: Getting Started With Aider AI

My experience setting up Aider on my development machine turned out to be easier than expected. The first week with this terminal-based code assistant taught me a lot about its capabilities through installation, configuration, and hands-on practice.

Installation via pip and virtual environment

A clean installation needs a dedicated virtual environment to prevent dependency conflicts. This step makes sense for any Python tool:

mkdir aider-projects
cd aider-projects
python -m venv aider-env
source aider-env/bin/activate  # On Windows: aider-env\Scripts\activate

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The next step was installing aider with pip after activating the environment:

python -m pip install -U --upgrade-strategy only-if-needed aider-chat

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Users with Python 3.8-3.13 already installed can take a simpler route:

python -m pip install aider-install
aider-install

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This method creates a separate Python environment just for aider and helps avoid package conflicts. Docker users have another option:

docker pull paulgauthier/aider
docker run -it --volume $(pwd):/app paulgauthier/aider --openai-api-key $OPENAI_API_KEY

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It's worth mentioning that system package managers often include aider versions with incorrect dependencies. The official installation methods are your best bet.

Initial setup with Git and API keys

Aider works naturally with Git repositories. The tool asks to create a repo if you launch it in a directory without Git. Git integration plays a central role in aider's workflow - every AI-suggested code change gets an automatic commit with clear messages. This makes tracking, reviewing, or undoing changes a breeze.

API configuration gives you plenty of options. My setup experiments included:

Command line arguments: --openai-api-key and --anthropic-api-key
Environment variables: OPENAI_API_KEY and ANTHROPIC_API_KEY
.env file in the project directory
.aider.conf.yml configuration file

The .env file approach worked best for my needs:

OPENAI_API_KEY=my_key_here
ANTHROPIC_API_KEY=my_other_key_here

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Other model providers work through similar settings. DeepSeek's models need a DEEPSEEK_API_KEY=key in your environment variables.

Configuration options follow this pattern - you can set them through command line, YAML config file, or environment variables. Dark mode activation is a good example:

# Command line
aider --dark-mode

# YAML config
dark-mode: true

# Environment variable
AIDER_DARK_MODE=true

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First impressions using aider chat in terminal

The first time I used aider felt natural and productive. Starting a session was as simple as running aider in my project directory. The user-friendly interface gives you a command prompt where you type instructions in plain language.

Creating a Python script was my first test. Aider generated the file, showed me what changed, and committed everything to Git. This quick feedback impressed me - no switching between tools or manual file handling needed.

Aider really shines when modifying existing code. Unlike other AI tools that just make suggestions, aider changes your files directly and tracks everything in Git. This practical approach cuts out copy-pasting and keeps your code organized.

The chat commands proved helpful:

/diff shows all changes since your last message
/undo reverts the last change instantly
/commit saves dirty changes with clear messages
/git runs raw git commands for complex operations

A simple Python game project showed me that aider not only writes working code but finds and fixes errors without being asked. The time savings jumped out right away - one user finished tasks in 30 minutes instead of their usual hour.

Git integration stands out as a thoughtful feature. Aider commits any pending changes with clear messages before making its own edits. This careful approach means you never lose work if you need to undo an AI change.

My first week showed me that aider is more than just another coding tool. It's a reliable pair programmer that respects development best practices while making coding faster and easier.

Week 2: Exploring Aider’s Core Features

My second week with aider focused on learning its core features. I wanted to understand what makes this terminal-based assistant different from other AI coding tools.

Code generation and file creation

After getting used to aider's interface, I found that there was an impressive way to generate complex code structures. Unlike other code assistants that just suggest snippets, aider creates entire files from scratch when you describe what you need in plain English.

I put this to the test by asking aider to "create a Flask API endpoint for user authentication." The response came in seconds - a complete Python file with all the right imports, function definitions, error handling, and docstrings. The code worked well and followed all the best practices with consistent style.

Aider really shines when it comes to handling multi-file projects. When I asked for a feature that needed changes in several files, it knew exactly which files to modify and made all the necessary updates. This came in handy when I had to implement design patterns that needed changes across different components.

The tool is just as good with existing codebases. When I needed to add logging to a medium-sized app, it picked the right spots across multiple files to make changes without breaking anything.

Auto-commits and Git integration

Git integration turned out to be one of my favorite parts of aider's workflow. Every meaningful code change gets its own commit with a clear message. This creates a clean history that's easy to review.

Here's what makes aider's Git handling special:

It stages any uncommitted changes before making new ones
Each commit message explains what changed and why
Your requested changes get their own commit
You can access the whole history through regular Git commands

This solves the common problem where AI changes end up as one big, messy commit. Instead, you get a clear record that shows how your code evolved.

Aider also plays nice with your Git workflow. I switched branches mid-session to check another feature, and it adapted to the new context right away.

Using aider chat modes: code, architect, ask

The second week let me try out aider's different chat modes. Each mode helps with specific development tasks:

The code mode (default) lets you write, edit, and refactor files directly. It works best when you need actual code changes.

The architect mode, activated with /mode architect, helps you plan things out. You can discuss structures, design patterns, and system architecture before making any changes. This helped me plan complex features before writing code.

The ask mode (/mode ask) turns aider into a coding consultant. It answers questions about your code without changing anything. This helped me understand older code and explore different ways to solve problems.

These modes created a flexible way to work. For bigger features, I started planning in architect mode, switched to code mode to build things, then used ask mode to check my understanding.

As days went by, I started giving aider more complex tasks. The time savings became clear - tasks that usually took hours of reading docs and testing syntax now happened through simple conversations with an assistant that understood my project and coding best practices.

Week 3: Real-World Coding Tasks With Aider

The third week arrived and I decided to expand aider's capabilities with some really challenging development tasks. Any coding assistant faces its real test when it deals with messy legacy code, test generation, and learning alternative interfaces.

Refactoring legacy codebases

Legacy code refactoring turned out to be aider's strongest suit. Most AI tools struggle with large codebases, but aider handled complex refactoring tasks across multiple files with impressive coordination.

My starting point was a bloated Python class full of oversized methods—perfect for cleanup. Aider's refactoring benchmark tests its ability to restructure 89 large methods from complex Python classes, making it a great fit for this challenge.

The system separated the code correctly and managed to keep all cross-references and dependencies when asked to "extract this method into smaller functions." Regression risks worried me at first, but aider's tight Git integration made each change easy to review and reverse.

Aider really showed its worth with deeply intertwined modules. The task involved extracting business logic from a web controller full of technical concerns. The assistant methodically spotted core functionality, moved external dependency references to the right spots, and pulled out pure logic functions. It basically applied the Repository pattern without being told to do so.

So refactoring tasks that normally take hours of careful analysis wrapped up in minutes. This quick turnaround proved invaluable with legacy systems that lacked proper documentation.

Generating test cases with LLMs

The system's ability to generate detailed test cases was maybe even more remarkable. My challenging test scenario involved creating tests without giving aider access to the tested source code.

Aider succeeded with just high-level metadata from the repository (function signatures and class structures). It:

Spotted the function signature needing tests
Figured out necessary class instantiations
Built appropriate test fixtures and mocks
Created assertions to check behavior

One standout example showed aider creating a "black box" test case using only a repository map from ctags. It worked out how to call methods and set up required classes without seeing any implementation details.

Running tests with aider's /run command helped it quickly spot and fix issues in the generated code based on error messages. This ability to learn from results and adjust its approach speeds up test development dramatically.

Voice commands and browser mode usage

My third week included learning aider's alternative interfaces, starting with voice commands. Voice mode activation was simple—typing /voice in chat let me speak coding instructions directly.

Technical terms posed no problem for the voice transcription. The system implemented a working solution right away when I asked it to "create a function that validates email addresses using regex." This hands-free option proved valuable during focused coding sessions when keyboard and mouse switching would break concentration.

The experimental browser UI, launched with the --browser switch, came next. This visual interface kept all terminal version features while making them more accessible. Browser mode excelled at handling multiple files, showing changes across the codebase clearly.

Multi-file editing became more intuitive with side-by-side comparisons highlighting exact changes. This visual approach helped implement design patterns affecting several components simultaneously.

The terminal version remained my go-to choice for its speed and simplicity. In spite of that, having these options meant I could adapt my workflow to specific tasks—voice for quick changes and browser interface for complex multi-file operations.

Week 4: Advanced Workflows and Customization

My last week with aider focused on making the tool fit my workflow better through advanced settings and editor connections. The flexibility I found boosted my work with this terminal-based assistant by a lot.

Using .env and YAML config files

I used aider with default settings for weeks until I found that there was a powerful system to adapt it to any development setup. You can customize how aider works through config files that load in a specific order:

Aider looks for the .aider.conf.yml file in these locations:

Your home directory
The root of your git repository
The current working directory

Files loaded later get priority, which lets you set global defaults and override them for specific projects. You can also set environment variables with a .env file in the same places, so you retain control over how aider behaves.

Instead of dealing with command-line settings, I made a project-specific config file that matched my workflow:

# My custom .aider.conf.yml
model: claude-3-5-sonnet
dark-mode: true
auto-commits: true
attribute-author: true
show-diffs: true

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The .env file helped me handle sensitive data like API keys. Unlike YAML settings, .env works with all API providers, not just OpenAI and Anthropic:

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=...
DEEPSEEK_API_KEY=...

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This setup keeps sensitive credentials away from general settings, which is a security practice I really like.

Prompt caching and model switching

While learning about aider's settings, I found that there was prompt caching - a feature that made responses faster and cut down API costs. Adding --cache-prompts to my setup turned on caching for models like Claude 3.5 Sonnet and DeepSeek Chat.

Aider smartly manages chat history to get the most from caching, especially when it comes to:

System prompts
Read-only files
Repository maps
Editable files added to the chat

This meant I got almost instant answers when asking more questions about the same code. Cache stats don't show up during streaming responses, so I sometimes used --no-stream to see how much it helped.

Cache usually expires after 5 minutes with Anthropic, so I added --cache-keepalive-pings 12 to my settings. This kept the cache alive for up to an hour when I wasn't active.

The in-chat /model command helped me switch between models. This came in handy when different tasks needed different strengths - Claude for complex thinking and GPT-4 for writing code.

I created a .aider.model.settings.yml file to fine-tune specific models:

- name: anthropic/claude-3-5-sonnet
  extra_params:
    reasoning_effort: high

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These settings made Claude give more detailed explanations when solving complex problems, though it used more tokens.

Integrating with VS Code and other editors

I spent time connecting aider to my favorite development environment. --watch-files mode turned out to be the best way to integrate, as aider could watch files for AI coding comments from any editor.

The "VSCode Aider Extension" from the marketplace gave me:

Files that sync automatically between aider and VS Code
Aider functions right in the command palette
Code refactoring through right-click menus
File management in the explorer menu

This extension creates a connection between VS Code and aider running in the background. It blends VS Code's great interface with aider's AI features.

I also tried "Aider Composer", another VS Code extension that puts chat modes directly in the editor. It's still experimental but looks promising for developers who want to stay in their IDE.

Making these customizations showed me how flexible aider really is. Each setting helps with specific workflow needs without making things complicated, and the tool stays user-friendly throughout.

Performance Review: Aider With Different LLMs

My tests of various language models with aider showed striking differences in how they performed, what they cost, and what they could do. I tracked how these LLMs handled similar coding challenges through aider's interface to get a full picture of which one worked best for professional development.

Claude Sonnet vs GPT-4o vs DeepSeek

Each language model brought its own strengths to aider's code editing capabilities. Claude Sonnet stood out with its well-laid-out code and detailed documentation. Aider's polyglot measure shows Claude 3.5 Sonnet hit a 51.6% success rate on complex coding exercises in multiple languages. The newer Claude 3.7 Sonnet pushed even further, nailing 60.4% of these benchmark tasks.

GPT-4o's results varied based on the version I tested. The latest GPT-4o benchmark landed at 45.3% correct completions, while older versions scored nowhere near that at 18.2%. One thing to note - GPT-4o wasn't as reliable with proper edit formatting compared to other models, hitting only 64.4% in its latest version.

DeepSeek turned out to be a strong contender, especially its newer versions. DeepSeek V3 nailed 55.1% on the polyglot benchmark, beating some GPT-4o versions at a much lower cost. DeepSeek R1 really shined in math, solving IMO problems with 83% accuracy, making it perfect for number-heavy coding tasks.

Claude models ended up delivering the most balanced results in my tests, particularly when handling complex refactoring across multiple files.

Speed, accuracy, and cost comparison

The price differences between these models really stood out during my month of testing. DeepSeek Chat V3 proved incredibly cost-effective at $1.27 per million tokens while still hitting 55.1% accuracy on coding benchmarks. Claude 3 Sonnet runs about $18.00 per million tokens, and GPT-4 tops the chart at $90.00 per million tokens.

Ground performance metrics beyond basic token costs revealed interesting patterns. The complete 225-coding exercise benchmark cost:

$36.83 with Claude 3.7 Sonnet (32k thinking tokens)
$19.74 with GPT-4o
$1.12 with DeepSeek V3

These cost differences add up during long development sessions, especially since DeepSeek V3 outperformed GPT-4o while costing just 5.7% as much.

Processing speeds varied quite a bit between models. Studies show GPT-4o processes images and code twice as fast as older GPT-4 versions. Claude models handled context better with 98% recall accuracy at 180K tokens, compared to GPT-4o's 89% at 128K tokens.

Complex coding tasks that needed multiple tries worked best with a combination of DeepSeek R1 and Claude 3.5 Sonnet in architect mode. This combo reached 64% accuracy and cost only $13.29 for the benchmark suite - striking a sweet spot between performance and cost.

The benchmark also looked at how reliably models produced properly formatted code edits - crucial for aider to work right. Claude 3.5 Sonnet hit 99.6% proper formatting, while GPT-4o's results bounced between 64.4% and 95.1% depending on the version.

My testing showed that while Claude Sonnet are the foundations of reliable performance for complex coding tasks, DeepSeek models are a great way to get similar results at a fraction of the cost. Your choice between them comes down to your development needs and budget.

Aider vs Cursor: Which AI Coding Assistant Wins?

My month of testing helped me compare aider and Cursor to figure out which AI coding assistant works better for different development needs. These tools take completely different paths to AI-assisted coding.

Interface and usability differences

Cursor gives you a full GUI environment built on VS Code that comes with integrated AI assistance. Developers who like graphical interfaces with traditional code editing features will find its visual environment appealing. On the flip side, aider takes a minimalist terminal-based approach that command-line fans will love.

The way these interfaces work affects your workflow substantially. Cursor let me hand-pick specific files for AI processing, which gave me exact control over what the model could see. Aider works differently - you add only the files that need changes, while its repository mapping system pulls context from related files automatically.

Context handling and model support

Each tool has its own way of handling context in large codebases. Aider's repository mapping system worked better for focused tasks but you need to think over how you manage files. Cursor keeps context through its indexing system but sometimes had trouble with bigger files and context windows.

Cursor would sometimes add features we already had because it couldn't keep everything in its context window. The biggest problem showed up when working with large files - Cursor would lose track of changed functions or remove features by mistake.

Looking at model support, aider showed it knows how to work with almost any LLM through its API integration. This meant I could switch between DeepSeek, Claude, GPT models, and even local options like Ollama. Cursor sticks mainly to specific commercial models.

Pricing and open-source flexibility

The way these tools handle pricing couldn't be more different:

Aider is open-source and free to use - you just pay for the API calls from your chosen LLM provider
Cursor needs a subscription that costs about $20 per month for full access

This price difference changes how you use them. With aider, you can try different models since you only pay for API calls. Cursor's subscription lets you generate as much AI-based code as you want within their platform.

These tools ended up serving different developer priorities. Aider shines when you value terminal workflows, Git integration, and model flexibility. Cursor makes more sense if you prefer visual interfaces with tightly integrated AI help.

What I Loved About Aider AI

My extensive experience with aider has revealed several outstanding features that make it my go-to AI coding assistant. Three core attributes have consistently amazed me and changed the way I handle my coding tasks.

Git-native workflow

Aider's Git integration has transformed my development process. Most coding assistants just suggest changes, but aider automatically commits each modification with descriptive, contextual messages. The integration works naturally - aider stages uncommitted changes before making its own modifications. This creates a clean, reviewable history of AI-assisted edits.

Knowing how to use standard Git commands to review, manage, and potentially undo AI changes gives you an unmatched safety net. This approach solves a common problem with AI coding tools where changes become one massive, hard-to-track modification.

Support for local models

Without doubt, aider's most valuable feature is its impressive flexibility with language models. The tool works great with commercial offerings like Claude 3.7 Sonnet and GPT-4o. It also connects naturally to local models through:

Ollama integration for privacy-conscious development
OpenAI-compatible API connections for custom deployments
LiteLLM package support for various model providers

This flexibility lets me balance performance against cost based on the task. More capable models produced better results - local options sometimes had trouble with complex edits. Having a choice between local and cloud models gave me welcome control over my development environment.

Community and documentation

The active ecosystem around aider ended up convincing me of its staying power. As an open-source tool (Apache 2.0 licensed), aider gets better through community contributions and customization. The project has an active GitHub repository and Discord community where users share their experiences and solutions.

The documentation quality stands out. The complete guides cover everything from simple installation to advanced configuration options and has sections for fixing common issues. These resources are a great way to get help when learning advanced features like repository mapping and multimodal support.

Aider bridges the gap between AI assistance and professional development practices. It's perfect for developers who want a powerful, flexible AI coding assistant that maintains control and integrates with existing workflows.

What Could Be Better in Aider AI

My month-long evaluation of aider revealed some areas that need work, despite its impressive capabilities. This terminal-based assistant has limitations you should think about before making it part of your workflow.

Occasional prompt misinterpretation

Advanced LLMs power aider, but prompt misinterpretation remains a challenge. The tool doesn't deal very well with local variable scope in single-file contexts and sometimes overwrites its changes during sequential modifications. These problems are systemic, especially when you have complex refactoring tasks that need careful context management.

I ran into a specific issue with Markdown files. Aider misreads text-based prompts with "AI!" as requests to remove comments instead of generating new content. This odd behavior meant I had to word my instructions carefully when working with documentation and code together.

Using GPT-4 through aider was problematic at times. It replaced Pester tests with "unusable garbage" and made poor assumptions about code structure. Breaking complex tasks into smaller, focused commands worked better than trying to handle multiple instructions at once.

Limited GUI polish in browser mode

The experimental browser interface works but lacks the polish of dedicated IDE extensions. The documentation calls it "a bit raw," though some users find it "surprisingly slick" to make quick edits.

Browser mode starts a local web server that gives you a chat interface like in the terminal experience. This interface helps with visual workflows and text manipulation, but you still need the same Git repository and API key setup as the terminal version.

The browser interface sometimes has trouble executing commands compared to its terminal counterpart. It shows search-replace blocks correctly, but users report inconsistent command processing. The interface's experimental nature shows up in how it handles Markdown too.

We have a long way to go, but we can build on this progress. These limitations show aider's focused approach that steers clear of "long delays, high token costs and the need for users to repeatedly code review incorrect solutions". This trade-off gives you an optimized experience but comes with occasional misinterpretations and rough edges in the interface.

Conclusion

My month-long experience with aider, a terminal-based coding assistant, showed it works amazingly well despite some limitations. I tested it with projects of all sizes and found its best feature: Git integration that automatically commits AI-suggested changes and keeps a clean, reviewable history.

Of course, aider makes financial sense, especially when you have DeepSeek models that give you 55% accuracy at just $1.27 per million tokens. Claude 3.7 Sonnet hits higher accuracy around 60%, but developers can pick what works best for their budget and performance needs thanks to aider's support for different models.

Numbers don't tell the whole story here. Daily tasks became substantially faster - from quick refactoring to test generation and complex codebase navigation. Some prompt confusion happened with Markdown files and complex refactoring requests. Breaking down tasks into smaller commands gave better results consistently.

Terminal-first design might look restrictive, but it fits perfectly into professional development processes. The browser interface needs work since it's still experimental. However, aider's core features, quality documentation, and strong community support make it an excellent choice for developers who want AI help while they retain control over their established practices.