What is it

Devin is an autonomous AI software engineer developed by Cognition Labs, an applied AI lab focused on reasoning. Unlike coding assistants that suggest completions within an editor, Devin operates as an independent engineer with its own sandboxed shell, browser, and code editor. It can plan and execute complex engineering tasks requiring thousands of decisions, recall relevant context at every step, learn over time, and fix its own mistakes.

What it can do

Devin writes, runs, and tests code; reproduces and fixes bugs; implements entire features from scratch; performs code migrations and refactors; reviews pull requests; and answers questions about codebases. It works inside a dedicated Linux sandbox with internet access, allowing it to install dependencies, browse documentation, run servers, and iterate on failures autonomously. Teams can assign a fleet of Devin agents to work in parallel on large-scale migrations across multiple repositories.

Who is it for

It is built for ambitious engineering teams, tech startups, and individual developers who want to scale their capacity by delegating end-to-end engineering tasks—from backlog tickets and bug fixes to multi-million-line codebase migrations—to an autonomous agent that works asynchronously and delivers reviewed pull requests.

Key Features

Autonomous AI Engineer – End-to-End Task Execution

Devin operates as a fully autonomous software engineer that receives a task, forms a plan, writes code, runs tests, handles failures, and opens a pull request for human review. It manages its own browser, shell, and editor within a sandboxed compute environment, executing the complete development workflow that a human engineer would follow—without requiring step-by-step guidance.

Sandboxed Development Environment – Isolated Workspace

Each Devin session runs in a dedicated Linux sandbox with a full file system, terminal, web browser, and embedded IDE. The agent can install packages via pip, npm, or apt; browse documentation and Stack Overflow; run database migrations; and execute test suites. This isolation ensures Devin never runs code directly on your local machine.

Code Migration & Refactoring – Fleet of Parallel Agents

Delegate large-scale migration and modernization tasks to Devin, including language migrations (JavaScript to TypeScript), framework upgrades (Angular 16 to 18), monorepo-to-submodule conversions, and legacy ETL refactoring. For massive projects, assign a fleet of Devin agents to migrate all repositories in parallel, achieving efficiency gains of 8-12x compared to manual engineering effort.

Bug Detection & Resolution – Automated Debugging

Devin autonomously investigates bug reports, reproduces issues, and implements fixes. It can monitor Datadog incidents, route Slack bug reports, automatically fix CI failures, and resolve Linear or Jira tickets. The agent reads error messages, searches for solutions, and iterates until the problem is resolved—keeping a human in the loop for final approval.

Use Cases

Tackling Linear and Jira Ticket Backlogs

Engineering teams assign Devin to handle incoming tickets for bug fixes, small features, and edge cases directly from their project management tools. Devin works asynchronously on multiple tickets in parallel, delivering draft pull requests that engineers review and merge—preventing backlog accumulation and keeping sprints on track.

Migrating Legacy Codebases at Scale

Organizations facing multi-year refactoring efforts—such as converting monolithic ETL systems with millions of lines of code into modular subsystems—deploy fleets of Devin agents to handle sub-tasks in parallel. After initial coaching on migration patterns, Devin completes the work autonomously with humans managing the project and approving changes, compressing months of effort into weeks.

Building New Features from Scratch

Product teams describe new features in natural language and delegate implementation to Devin. The agent handles frontend, backend, and testing logic, iterating on its own work until CI passes and opening a pull request for team review. This accelerates prototyping and reduces the time from specification to production.

Automating Pull Request Reviews

Teams configure Devin to review incoming pull requests for common issues—logic errors, missing tests, style violations, and security concerns—before human reviewers spend time on them. This raises baseline code quality and allows senior engineers to focus on architectural decisions rather than routine checks.

Pricing plans

Frequently Asked Questions

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