Augment

Augment is an AI coding assistant built to understand large codebases, giving developers completions, answers, and refactors grounded in their real repository.

Overview

Most coding assistants work well on a single file and fall apart on a monorepo. Augment’s differentiator is deep codebase context: it indexes the repository, learns its patterns and dependencies, and uses that understanding when generating code or answering questions about how the system works. It plugs into popular IDEs, offers next-edit suggestions that handle multi-file changes, and includes a chat interface for architecture questions. Teams use it to onboard faster and reduce the context switching that slows large projects.

The difference from general-purpose assistants shows up in large repositories: suggestions respect local conventions because they are grounded in the codebase itself. Teams report the biggest gains in onboarding and in refactors that touch many files, where context is exactly what generic tools lack.

Key Features

• Repository-wide context engine tuned to your codebase
• Multi-line and multi-file completion suggestions
• Chat that answers architecture and debugging questions
• IDE integrations for mainstream editors
• Team-oriented deployment with privacy controls
• Next-edit suggestions handling cross-file changes
• Chat grounded in repository structure and history
• Privacy controls for enterprise codebases

The difference from generic assistants shows up in large repositories: suggestions respect local conventions because they are grounded in the codebase itself. Teams report the biggest gains in onboarding and multi-file refactors, where context is exactly what general tools lack.

Team plans centralize context configuration so the assistant understands shared conventions, and privacy controls keep code inside approved boundaries. Usage analytics help engineering leads see where the assistant earns its seat across repositories and roles.

Pricing

A free trial lets developers test it on real work; paid individual and business plans follow, priced per seat.

Who Is It For

Professional developers working in large, mature codebases where generic suggestions underperform. It fits professional developers working in large, mature codebases.