Aider is an open-source AI pair programming tool created by Paul Gauthier that runs entirely in the terminal. It connects to LLMs like Claude, GPT-4, and Gemini to edit code directly in a local git repository, automatically committing changes with descriptive messages. With over 42,000 GitHub stars and millions of installs, Aider has established itself as a leading terminal-based AI coding assistant through its deep git integration, repository-wide context awareness, and flexible multi-model support.

Aider's architecture is built around three key components:

Repository Map (Tree-sitter)

Aider generates a structured map of the entire codebase using Tree-sitter, a language-agnostic parser generator. This repo map indexes files, functions, classes, methods, and their relationships across 100+ programming languages. Rather than naively concatenating file contents, the map provides the LLM with a compact representation of project structure, enabling:

• Understanding of cross-file dependencies
• Identification of relevant context without reading every file
• Efficient handling of large repositories that exceed context windows

The repo map is generated by parsing all source files into syntax trees, extracting declarations, building a dependency graph, and ranking symbols by relevance using PageRank-like scoring.

Edit Formats

Aider supports multiple formats for applying LLM-generated changes:

• Whole-file: Replaces entire files – suitable for major rewrites or new files
• Diff: Standard unified diff format for precise line-based modifications
• Udiff: Aider's custom micro-diff format that minimizes token usage and reduces errors by making minimal, targeted edits

The edit format is selected based on the model's capabilities – more capable models use diff/udiff, while others default to whole-file replacement.

Architect/Editor Pattern

The Architect mode introduces a two-model system that separates reasoning from editing:

• Architect LLM (e.g., Claude Sonnet, o1-preview): Analyzes the problem, designs the solution, and produces a high-level plan without writing code
• Editor LLM (e.g., GPT-4o, DeepSeek): Interprets the architect's plan and generates exact file edits in diff/udiff format

This separation achieves the highest benchmark scores (85% on Aider's code editing benchmark) by optimizing each model for its strength.

Git is fundamental to Aider's workflow, not an afterthought:

• Every edit is automatically committed with an LLM-generated descriptive message
• Changes are applied as atomic commits – multi-file edits are grouped together
• Users can review changes via /diff or standard git diff
• Reverting is trivial: git reset HEAD~1 undoes the last AI change
• Aider expects to run inside a git repository and will initialize one if needed

This makes AI-assisted changes fully traceable, reviewable, and reversible through familiar git workflows.

Aider handles coordinated edits across multiple files through:

• /add <files>: Add files as editable context
• /read-only <files>: Add files as reference context (prevents accidental modification)
• The Tree-sitter repo map identifies cross-file dependencies automatically
• Multi-file diffs are generated in a single LLM response and applied atomically

# Aider workflow examples (terminal commands)
 
# Start aider with Claude Sonnet in architect mode
# $ aider --sonnet --architect
 
# Start with GPT-4o
# $ aider --4o
 
# In-chat commands:
# /add src/auth.py src/models.py    - Add files for editing
# /read-only src/config.py          - Add as read-only context
# /ask "How does the auth flow work?" - Ask without making edits
# /architect                         - Toggle architect mode
# /diff                              - Review pending changes
# /run pytest tests/                 - Run tests after edits
# /undo                              - Undo last change (git reset)
# /tokens                            - Show token usage
# /model claude-3-5-sonnet           - Switch models mid-session
 
# Example session:
# $ cd /path/to/project
# $ aider --sonnet --architect
# > /add src/api.py src/models.py
# > Add pagination to the /users endpoint with limit and offset params
# (Aider edits both files and auto-commits)
# > /run pytest tests/test_api.py
# (Aider sees test results and can fix failures)

Aider works with virtually any LLM provider:

Models are configured via API keys in environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.) or command-line flags.

On Aider's code editing benchmark (measuring accurate multi-file changes):

• Architect/Editor mode: 85% accuracy (highest recorded)
• Top combinations: o1-preview (Architect) + DeepSeek (Editor), o1-preview + Claude 3.5 Sonnet (Editor)
• Single-model baselines trail due to weaker edit precision
• The two-step process adds latency but significantly improves correctness

Linting and Testing Integration

• /run executes any shell command and feeds output back to the LLM
• If tests fail after an edit, Aider can automatically attempt to fix the failures
• Linter output is parsed and used to guide corrections

Voice and Web Input

• Voice input via /voice for hands-free coding
• URL scraping via /web to add documentation or issue context
• Image input for visual context (UI mockups, diagrams)

# Install via pipx (recommended)
# $ pipx install aider-chat
 
# Or via pip
# $ pip install aider-chat
 
# Or via Homebrew (macOS)
# $ brew install aider
 
# Quick start
# $ export ANTHROPIC_API_KEY=sk-ant-...
# $ cd /path/to/git/repo
# $ aider --sonnet

• Aider Official Website
• Aider GitHub Repository
• Aider Code Editing Benchmarks
• Repository Map Documentation

• TaskWeaver - Code-first agent framework with a different approach to code generation
• LLM-as-a-Judge - Evaluation methodology relevant to coding benchmarks
• AgentBench - Benchmark that includes coding-related agent tasks