Master Devin AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Request access at devin.ai and complete the enterprise onboarding process with Cognition's sales team Connect your GitHub or GitLab repositories and configure repository access permissions for Devin Set up the Slack or Microsoft Teams integration so your team can assign tasks by tagging @Devin in conversation threads Upload your team's coding standards, documentation, and architectural guidelines to Devin's Knowledge base Start with a well
scoped test task — such as writing unit tests for an existing module or migrating a small component to TypeScript — and review Devin's pull request
💡 Quick Start: Follow these 2 steps in order to get up and running with Devin AI quickly.
Explore the key features that make Devin AI powerful for ai agent builders workflows.
Devin doesn't just suggest code — it independently plans, implements, tests, and deploys complete solutions. Given a task like 'migrate this module from JavaScript to TypeScript,' Devin analyzes the existing code, plans the migration strategy, converts files while maintaining type safety, updates imports, runs the test suite, fixes any failures, and opens a pull request. This end-to-end autonomy eliminates the constant context-switching developers face with traditional AI code assistants.
Each Devin session runs in an isolated workspace with its own shell, VS Code-style editor, and web browser. Devin uses these tools exactly like a human developer — running commands in the terminal, editing files in the IDE, and browsing documentation or Stack Overflow when needed. The entire environment is observable in real-time, and developers can take over at any point through the embedded IDE.
Enterprise customers can fine-tune Devin on their specific codebase patterns using historical examples of completed tasks. Nubank's fine-tuning doubled Devin's task completion scores and improved speed by 4x. The Knowledge feature supplements this by letting teams upload documentation, coding standards, and architectural guidelines that Devin references during development.
Teams can run multiple Devin sessions concurrently, each working on different tickets, bugs, or features. This parallel execution model means a team of five engineers can effectively operate like a team of twenty by delegating routine tasks to Devin sessions while focusing their own time on high-judgment work.
Devin integrates directly with Slack and Microsoft Teams for conversational task assignment — tag Devin in a thread about a bug and it starts investigating. GitHub and GitLab integration enables direct repository access and PR creation. Linear and Jira connections allow Devin to pick up tickets directly from your project management tools. The Devin API enables custom automation pipelines.
Devin is an autonomous AI software engineer rather than an autocomplete copilot. Copilot and Cursor sit inside your IDE and accelerate the code you are actively writing. Devin works in its own cloud sandbox with a shell, editor, and browser, so you can hand it a ticket and it will plan the work, write the code, run tests, debug, and open a pull request without a human at the keyboard for each step.
Devin uses a usage-based model built around ACUs (Agent Compute Units). The Core plan starts around $20 to get started with pay-as-you-go ACUs, the Team plan is roughly $500/month and includes a bundle of ACUs plus collaboration features, and Enterprise pricing is custom with volume discounts, SSO, and dedicated support. Pricing has changed several times since launch, so check devin.ai for the current rates.
Devin performs best on well-scoped, verifiable work: fixing bugs with a clear repro, large-scale migrations (framework upgrades, language version bumps, codemods), backfilling test coverage, small feature work, and triaging issues from Sentry, Linear, or Jira. It struggles more on ambiguous architectural design or in poorly documented legacy code without good tests.
Cognition offers SOC 2 Type II compliance, role-based access controls, and a custom knowledge layer so Devin can learn an organisation's internal conventions. Code runs in isolated sandboxes, and enterprise customers including Goldman Sachs, Citi, MongoDB, Nubank, and Ramp have publicly discussed using it. As with any AI agent, teams typically restrict the repositories and credentials Devin can access and require human PR review.
No. In practice teams use Devin as an autonomous junior-to-mid engineer that absorbs repetitive, low-leverage work — migrations, dependency bumps, test writing, small bug fixes — while senior engineers focus on design and review. PRs from Devin still require human code review, and ambiguous or high-stakes work is not handed over fully autonomously.
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Tutorial updated March 2026