aider

Start a new project or work with an existing code base

$ aider --model [model_name] --api-key [your_api_key]

$ aider [path/to/file1 path/to/file2 ...]

$ aider [path/to/file] --describe "[bug_description]"

$ aider [path/to/file] --refactor

$ aider [path/to/file] --update-docs

$ aider --help

aider [options]

--help
    Show help message and exit.

--model
    Specify the LLM to use (e.g., gpt-4, gpt-3.5-turbo).

--config-path
    Specify the path to the aider config file.

--no-pretty-print
    Disable pretty printing of output.

--input-file
    Load initial instructions from a file.

--git-diff-context
    Set the number of context lines to show in git diffs.


    Path to the git repository. If not specified, uses the current directory.

Aider is a command-line tool designed to be your AI pair programmer. It allows you to work with large language models (LLMs) like GPT-4 to collaboratively edit code in your local git repository.

Aider integrates directly with your terminal. You can describe the changes you want to make, and aider will automatically modify the code, commit, test, and discuss the results with you. It's essentially a chatbot interface for code modification, enabling a more natural and conversational workflow for software development. Aider can also explain code, suggest improvements, and help troubleshoot problems.

It bridges the gap between natural language descriptions of tasks and concrete code changes, offering a powerful way to enhance developer productivity and accessibility.

Aider's effectiveness heavily relies on the capabilities of the underlying LLM. The quality of suggestions and code edits can vary depending on the complexity of the task and the model being used. Always review and test changes made by aider before committing them.

A typical workflow involves initializing a git repository, running `aider`, specifying the desired changes in natural language, and iteratively refining the code based on aider's suggestions and feedback. Aider manages code edits, git commits and tests using the LLM