Devin: AI Coding Assistant

What is Devin?

Devin is an AI coding assistant built to plan and execute complex software engineering tasks autonomously. It writes code, runs tests, submits pull requests, resolves bugs, reviews PRs, and analyzes codebases — operating inside a full Linux development environment the way a human engineer would. Devin handles long-horizon tasks that span multiple files, repositories, and decision points in sequence. It is built for engineering teams working on production codebases, not for isolated one-off scripts.

Features & Benefits

• Autonomous Code Execution — write and run code inside a sandboxed Linux environment with terminal access, browser use, and GUI interaction to complete tasks end to end
• Computer Use — control a full Linux desktop by moving the mouse, clicking UI elements, typing, and taking screenshots to interact with any application the way a human tester would
• End-to-End Test Recording — run the application locally after creating a PR, execute a focused test plan in the desktop environment, and send a screen recording as a message attachment so reviewers can verify changes without pulling the branch
• Pull Request Generation — open PRs, respond to reviewer feedback, and pick up CI results to get changes merged; leave comments, approve PRs, and request changes synced to GitHub
• Devin Review — turn large, complex GitHub PRs into logically organized diffs with smart grouping of related edits, copy and move detection, confidence-labeled bug flags, and codebase-aware chat that answers questions about the PR with context pulled from the full repo; free for public and private GitHub repos
• Ask Devin — index any connected repository automatically and answer natural language questions about architecture, dependencies, and key functions using advanced code search; plan and scope implementation tasks before writing code, and track session status directly in the conversation
• DeepWiki — automatically generate repo wikis with architecture diagrams, source-linked summaries, and codebase overviews; used by Ask Devin to improve code search accuracy and answer quality
• Code Migration & Refactoring — execute large-scale, multi-repo migrations including .NET and Java version upgrades and legacy ETL refactors across thousands of subtasks
• Bug Fixing & Issue Triage — investigate incidents in Datadog, route Slack bug reports, fix CI failures, and proactively handle tickets suited to Devin’s capabilities
• Data Analyst Agent (DANA) — query databases with SQL, analyze data for patterns and anomalies, generate seaborn visualizations, and answer data questions in a fast, analytics-optimized workflow
• ETL Development & Data Engineering — build ETL pipelines, run data warehouse migrations, and perform data lookup and investigation
• Codebase Learning — absorb tribal knowledge and codebase-specific context on an ongoing basis; improve task accuracy over time by reading past session trajectories
• Playbook Creation & Improvement — turn successful sessions into reusable playbooks or refine existing ones based on feedback for consistent execution across recurring task types
• Session Outcome Analysis — review why a session succeeded or failed, identify patterns, and extract learnings to improve future task execution
• Knowledge Base Management — deduplicate, consolidate, or create new knowledge entries drawn from the codebase
• Managed Devins (MultiDevin) — break large tasks into parallel workstreams and delegate each to a managed Devin session running in its own isolated VM; the coordinator session scopes work, monitors ACU consumption, sends follow-up instructions, resolves conflicts, and compiles results
• Schedule Management — configure recurring or one-time automated Devin sessions for tasks like daily QA checks, release notes generation, and documentation updates
• Event-Driven Automation (Enterprise) — spin up new Devin sessions automatically in real time as on-call tickets, CI failures, and other events occur
• MCP Marketplace — connect Devin to hundreds of external tools and data sources via Model Context Protocol using stdio, SSE, or HTTP transport, including Sentry, Datadog, Vercel, SonarQube, CircleCI, Linear, Notion, Airtable, Stripe, and more
• Devin API & Automations — integrate the AI coding assistant into existing pipelines via API or configure rule-based automations triggered by external events
• Slash Commands — insert predefined prompt templates into the chat interface to start common workflows quickly
• Unit & End-to-End Testing — generate and execute test suites as part of the development cycle
• Web Research & Scraping — gather information from the web as part of a larger engineering or data task
• Workspace Collaboration — share sessions, manage team roles, and control access within a shared environment
• VPC Deployment (Enterprise) — deploy Devin’s development boxes inside your virtual private cloud across major cloud providers so data stays within your controlled environment
• Custom Identity Provider Integration — connect to your organization’s IdP for authentication and access management

Real-World Applications

Engineering teams running a large legacy migration can assign the repetitive, rule-bound portions of that work to Devin. After defining the approach and feeding Devin examples of past migrations, teams can instruct it to analyze the codebase, group files into independent work packages, and launch a parallel Devin session for each package — all running simultaneously in isolated VMs. Human engineers review outputs and handle edge cases. What would take 18 months distributed across hundreds of engineers can compress into weeks.

On-call workflows can route directly through Devin. When a Datadog alert fires or a CI pipeline fails, event-driven automation can spin up a session automatically to investigate the root cause and open a fix for human review before an engineer has looked at the ticket. Teams dealing with high ticket volume or unpredictable incident frequency can configure this AI coding assistant to proactively handle tickets that match its capabilities, reducing the manual triage load significantly.

Code review on large PRs is where Devin Review has a concrete impact. When a PR touches dozens of files across multiple modules, Devin Review reorganizes the diff so related changes appear together, flags suspicious code by confidence level, and detects moved or copied blocks rather than showing them as full deletes and inserts. Reviewers can ask questions about any part of the diff and get answers grounded in the full codebase — all without leaving the review interface.

Data and analytics teams can use the Data Analyst Agent to query connected databases, explore patterns, and generate visualizations without writing analysis code manually. Combining DANA with MCP-connected data sources like Airtable or Stripe means Devin can pull live data, run SQL queries, and return charts in a single workflow. Teams onboarding engineers to unfamiliar repositories can use DeepWiki for auto-generated architecture diagrams and source-linked summaries — and use Ask Devin as an AI coding assistant for scoping new features before writing a line of code.