How to Use Devin AI Step by Step — 2026 Tutorial

Inicio rápido

This guide covers every Devin AI feature:

• Empezando — Create your account and run initial setup
• How to Use Autonomous Task Executor — Delegate complex tasks and get a pull request back
• How to Use Integrated Code Editor — Read and edit code inside Devin’s machine
• How to Use MCP Marketplace — Connect your tools and data sources
• How to Use Individual Agents Builder — Build specialized AI agents for repeat work
• How to Use DevinWiki Deep Research — Map a codebase and generate insights
• How to Use Customizable Workspace — Shape the sandboxed compute environment
• How to Use Advanced API — Trigger Devin from your CI/CD pipeline
• How to Use Integrated Shell — Run, test, and debug code at a real terminal

Tiempo necesario: 5 minutos por función

También en esta guía: Consejos profesionales | Errores comunes | Solución de problemas | Precios | Alternativas

¿Por qué confiar en esta guía?

I’ve used Devin AI for the past eight months and tested every feature covered here.

This tutorial comes from real hands-on experience, not marketing copy or vendor screenshots.

I have shipped real pull requests with Devin, watched it fix bugs, and watched it get stuck, so the advice here is balanced.

Devin AI is one of the most capable autonomous herramientas de IA available for software development today.

But most users only scratch the surface of what this software de IA engineer can do.

This guide shows you how to use Devin AI feature by feature.

Step by step, with screenshots and pro tips from real projects.

Devin AI Tutorial

This complete Devin AI tutorial walks you through every feature step by step, from initial setup to advanced tips that turn you into a power user.

Work through it in order the first time, then bookmark the feature sections you reach for most.

Each section is self-contained, so you can also jump straight to the tool you need today.

Getting Started with Devin AI

Antes de utilizar cualquier función, complete esta configuración inicial.

This is the foundation the rest of the guide builds on, so it is worth getting right.

It takes about three minutes.

The setup is short, but it shapes how reliable every later task will be.

Watch this quick overview of the top benefits first:

Ahora vamos a repasar cada paso.

Paso 1: Crea tu cuenta

Go to the Devin AI website at devin.ai.

Click “Get Started” and sign up with your email.

Pick the Core plan to begin — it starts at $20 per month on pay-as-you-go billing.

The price dropped from $500 to $20, so testing Devin no longer means a big commitment.

You only pay for the agent compute units a task actually consumes.

That makes the first few sessions cheap to run while you learn what good prompts look like.

✓ Control: Comprueba tu bandeja de entrada for a confirmation email from Cognition Labs.

Step 2: Connect GitHub and Open Devin’s Workspace

Log in and connect your GitHub account.

GitHub integration lets Devin set up a local environment and read your repositories.

This connection is also how Devin opens pull requests against the right repo later.

Here’s what the workspace looks like on first login:

✓ Control: You should see Devin’s workspace with the chat interface, code editor, shell, and browser.

Paso 3: Completar la configuración inicial

Add your environment variables when Devin prompts you during initialization.

Devin’s setup requires this step, so do it before your first real task.

Then seed Devin’s Knowledge Base with a few notes about your project and coding standards.

A little knowledge up front prevents repeat mistakes and lifts Devin’s performance on every later task.

How to Use Devin AI Autonomous Task Executor

Autonomous Task Executor lets you hand off complex engineering tasks and let this autonomous AI plan, write, and ship the code on its own.

This is the core of how Devin AI works as an ai software engineer.

This is the headline reason engineering teams adopt Devin AI in the first place.

From fix bugs to feature requests, Devin can autonomously find and fix bugs, write tests, and even build and deploy apps end to end.

Knowing how to use Devin AI here is mostly about writing good briefs.

Here are the kinds of jobs it handles well:

A good first task is a single, well-defined github issue with a clear bug report.

Devin reads the issue, reproduces the bug, writes the fix, and runs the tests before it asks for review.

For pattern-heavy work like code migrations, this is where Devin excels at codebase transformations.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Open a New Session in the Chat Interface

Open Devin’s workspace and start a fresh session.

Devin uses a chat interface, so you brief it the same way you would brief a junior software engineer.

Step 2: Describe the Task With Clear Completion Criteria

Write a clear prompt with explicit completion criteria.

Tasks should be objective so Devin can verify its own work. Break requirements into small, verifiable goals instead of one giant request.

Así es como se ve:

✓ Control: Devin shows a task breakdown and an interactive planning view before it starts writing code.

Step 3: Review the Pull Request Devin Opens

Watch Devin do the task breakdown and interactive planning before it touches any code.

Devin can open a pull request within 10 minutes of finishing the task breakdown.

The payoff is that routine tasks leave your queue without a developer babysitting them.

Devin completes tasks roughly 83% faster than its previous version, so well-scoped work finishes while you do something else.

That speed only holds when the task is objective enough for Devin to verify its own result.

One habit pays off more than any other: write the prompt as if briefing a new hire.

State the goal, the constraints, and how you will judge success, then let Devin handle the task breakdown.

Vague briefs are the single biggest reason a session burns ACUs without shipping anything useful.

How to Use Devin AI Integrated Code Editor

Editor de código integrado lets you watch, read, and edit code inside Devin’s machine without ever leaving the browser.

The built-in code editor sits right next to the shell and the browser.

Plenty of autonomous AI tools hide their work behind a chat box.

Devin instead exposes a full code editor so you can see exactly what changed.

This transparency is how you keep trust in an agent that writes real code.

Here are the kinds of jobs it handles well:

The editor highlights additions and deletions so a long diff stays readable.

If Devin touched a file it should not have, you spot it here and roll it back in one click.

Many developers keep the editor open the whole session, the way they would watch a pair programmer type.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Open the Code Editor in Devin’s Workspace

Switch to the editor tab inside Devin’s workspace.

You see the same files Devin is working on, live, as it writes code.

Step 2: Inspect Devin’s Changes Line by Line

Open the diff view to read every change Devin made.

This is where you catch human error temprano, before a pull request goes anywhere near your main branch.

Así es como se ve:

✓ Control: You can see Devin’s live diff and edit any file directly in the code editor.

Step 3: Make Manual Edits When You Need To

Type directly in the code editor to fix a line or adjust a function.

Devin picks up your edits and keeps working from there.

Reviewing in the editor is your main defense against human error slipping into production.

You catch a bad assumption in seconds instead of after a broken deploy.

It also teaches you how Devin AI works, which makes your next prompt sharper.

The editor also doubles as a teaching tool for the rest of the team.

Junior developers can watch how Devin structures a fix and learn patterns they had not seen.

Senior engineers, meanwhile, use it as a fast review surface before approving a pull request.

How to Use Devin AI MCP Marketplace

MCP Marketplace lets you connect external tools and data sources to Devin through the Model Context Protocol.

The marketplace is how you give Devin access to the rest of your stack.

No coding tasks happen in isolation; they touch trackers, docs, and data.

The MCP Marketplace is how Devin reaches that wider set of ai software your team runs.

Each connector follows the Model Context Protocol, an open standard for tool access.

Here are the kinds of jobs it handles well:

Common connectors include issue trackers, documentation sites, and internal data sources.

Once linked, Devin can read a ticket, pull the related docs, and act, all in one task.

Review each connector’s permissions on a schedule so access never drifts wider than you intended.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Browse the MCP Marketplace

Open the marketplace from Devin’s workspace.

You will find connectors for documentation, issue trackers, and other ai software your team already runs.

Step 2: Connect a Tool to Devin

Pick a connector and authorize it.

Devin can now pull data and act inside that tool during a task.

Así es como se ve:

✓ Control: The connected tool appears in Devin’s available tools list for the session.

Step 3: Grant Scoped Access

Limit each connection to only what Devin needs.

Scoped access protects data privacy while keeping Devin useful.

With the right connectors, Devin gathers relevant context without you pasting it manually.

That cuts task complexity and lowers the agent compute unit cost of every session.

Fewer manual steps also means fewer chances to introduce errors.

Start small with one or two connectors rather than wiring up everything at once.

Each new integration adds surface area for Devin to read, which can raise task complexity.

Add tools as real tasks demand them, and remove any connector your team no longer uses.

How to Use Devin AI Individual Agents Builder

Individual Agents Constructor lets you create specialized ai agents tuned to your specific needs.

Instead of one generalist, you build focused agents for repeat work.

One generalist agent is fine, but repeat work rewards specialists.

The builder lets you create focused ai agents for a single class of job.

Think of one agent for testing, another for code migrations, another for docs.

Here are the kinds of jobs it handles well:

Give each agent a short charter: the repos it owns, the tasks it accepts, and the tools it may touch.

A migration agent and a testing agent can run side by side without stepping on each other.

This is the practical way engineering teams scale Devin across many projects at once.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Open the Agents Builder

Go to the agents area in Devin’s workspace.

Click create to start a new agent.

Step 2: Define the Agent’s Scope and Rules

Tell the agent which repositories, tasks, and tools it owns.

A tight scope keeps the agent reliable on its slice of software development.

Así es como se ve:

✓ Control: Your new agent shows up in the agents list, ready to accept tasks.

Step 3: Save and Launch the Agent

Save the configuration and launch it.

You can run several agents at once across different projects.

Specialized agents are more reliable because their scope is narrow and well understood.

You can run several at once, so a whole backlog moves in parallel.

This is where small engineering teams punch far above their headcount.

Name each agent for its job so teammates instantáneamente know what it does.

Document the agent’s charter in your Knowledge Base alongside examples of good and bad tasks.

Over time these agents become reusable infrastructure rather than one-off experiments.

How to Use Devin AI DevinWiki Deep Research

DevinWiki Deep Research lets you let Devin map an unfamiliar codebase and generate insights before it writes a single line.

Deep research is what separates Devin from a plain large language model.

A large language model that has never seen your code will guess badly.

DevinWiki fixes that by having Devin study the project before it writes anything.

The result is a living map you can read like internal documentation.

Here are the kinds of jobs it handles well:

The research covers architecture, key modules, and how data flows through the system.

Share the wiki with new teammates and they ramp on the project far faster.

Because Devin can learn unfamiliar technologies on its own, this works even on stacks it has never seen.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Point Devin at a Repository

Connect a GitHub repository to Devin.

Devin can learn how to use unfamiliar technologies autonomously while it explores.

Step 2: Let Devin Index the Code

Devin reads the project and builds a structured map.

This long-term reasoning step is where Devin understands how the pieces fit together.

Así es como se ve:

✓ Control: DevinWiki shows a navigable map of the codebase with explanations.

Step 3: Read the Generated Wiki

Open DevinWiki to read the insights Devin produced.

Use it to brief teammates or to plan the next github issue.

This long term reasoning step is the difference between a useful PR and noise.

When Devin understands the project, its capabilities show on harder coding tasks.

Feed the research back into the Knowledge Base and every future task starts smarter.

Re-run the research after a big refactor so the map stays accurate.

Stale documentation is worse than none, and the same rule applies to a generated wiki.

Treat it as a living artifact that grows with the project, not a one-time report.

How to Use Devin AI Customizable Workspace

Customizable Workspace lets you shape Devin’s workspace and runtime environment to match your project.

Devin operates in its own cloud-based virtual machine that you configure once.

Devin operates in its own cloud-based virtual machine, not on your laptop.

The workspace settings are how you make that machine match your real project.

Get this right once and most surprises disappear.

Here are the kinds of jobs it handles well:

Set language versions, package managers, and any service the app depends on.

Store secrets as environment variables rather than pasting them into prompts.

A reproducible workspace is the quiet foundation behind every reliable Devin session.

Aquí te mostramos cómo usarlo paso a paso.

Paso 1: Abrir la configuración del espacio de trabajo

Go to the settings panel inside Devin’s workspace.

Here you control the sandboxed compute environment Devin runs in.

Step 2: Set Environment Variables During Initial Setup

Add the secrets and config your app needs.

Devin’s setup requires prompting for environment variables during initialization, so do this before the first run.

Así es como se ve:

✓ Control: Devin’s environment boots with your variables and dependencies loaded.

Step 3: Save the Configuration

Save and let Devin rebuild the environment.

Devin can set up a local environment using GitHub integration from here.

A workspace that mirrors production means Devin’s code runs the first time.

Mismatched environments are a top cause of confusing failures and wasted ACUs.

GitHub integration keeps the local environment in sync as your repo changes.

Keep a template workspace for each major project type your company maintains.

Spinning up a new session from a known-good template removes most setup guesswork.

It also makes onboarding a new repository a five-minute job instead of an afternoon.

How to Use Devin AI Advanced API

Advanced API lets you trigger Devin programmatically and wire it into your CI/CD pipeline.

The API turns Devin into a service your other tools can call.

The chat interface is great for ad-hoc work, but automatización needs an API.

The Advanced API exposes Devin over REST so other systems can call it.

This is how Devin stops being a tool you open and becomes part of your pipeline.

Here are the kinds of jobs it handles well:

A typical pattern triggers a Devin session from a webhook when a CI job fails.

Devin investigates, proposes a fix, and reports back into Slack for a human to approve.

Pair the API with ACU limits so an automated run can never blow past your budget.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Generate an API Key

Open the API section and create a key.

Keep the key secret, the same way you would treat any production credential.

Step 2: Send a REST Request to Start a Session

Call the REST endpoint with your task and the key.

Devin spins up a session and starts working without anyone opening the chat interface.

Así es como se ve:

✓ Control: Your REST call returns a session ID you can track to completion.

Step 3: Monitor Sessions Programmatically

Poll the API for status and results.

Hook this into Slack so your team gets feedback when a session finishes.

Wired into CI/CD, Devin can react to ci failures the moment a build breaks.

Scheduling parallel sessions through the API is the fastest path to clearing real world github issues.

For maximum efficiency, batch related tasks into one scheduled run.

Log every API-triggered session so you can auditoría cost and outcomes later.

A simple dashboard of sessions, ACUs spent, and results keeps surprises out of the monthly bill.

When the data shows a task type performing well, promote it to a scheduled, recurring run.

How to Use Devin AI Integrated Shell

Integrated Shell lets you let Devin run real commands in a sandboxed compute environment, just like a software engineer at a terminal.

The shell is what lets Devin actually run, test, and debug code.

Writing code is only half the job; running it is the other half.

The integrated shell gives Devin a real terminal in its sandboxed compute environment.

It uses a built-in shell, code editor, and browser together, like a developer at a desk.

Here are the kinds of jobs it handles well:

You see install logs, test output, and stack traces stream in real time.

When a test fails, Devin reads the error and adjusts, looping until the suite is green.

If something looks off, type a command yourself and Devin keeps going from that state.

Aquí te mostramos cómo usarlo paso a paso.

Step 1: Open the Integrated Shell

Switch to the shell tab in Devin’s workspace.

Devin uses a built-in shell, code editor, and browser together.

Step 2: Watch Devin Run and Test Commands

See Devin install dependencies, run tests, and read errors.

This is testing in action, the same loop a developer follows by hand.

Así es como se ve:

✓ Control: You see live command output and test results in the shell.

Step 3: Step In When You Need To

Type your own command to check something or fix state.

Devin continues from where you left off.

Because Devin runs commands, it can test and debug instead of guessing.

You watch real output, so there is no mystery about what the agent did.

Just remember it checks CI checks rather than build errors, so add a build step.

The shell is also the fastest way to confirm an environment problem versus a code problem.

If a command fails the same way for you and for Devin, the fix is usually configuration.

Use that signal to decide whether to adjust the workspace or rewrite the task.

Devin AI Pro Tips and Shortcuts

After testing Devin AI for eight months, here are the tips that saved me the most valuable time.

Most of these are about controlling cost and keeping Devin’s performance high across many sessions.

Small habits here compound: a tighter prompt and a richer Knowledge Base pay off on every task that follows.

None of them require special access, just a little discipline once you know where to look.

Atajos de teclado

Acción	Atajo
New session	Ctrl / Cmd + N
Abrir la paleta de comandos	Ctrl / Cmd + K
Switch to code editor	Ctrl / Cmd + 1
Switch to integrated shell	Ctrl / Cmd + 2

Características ocultas que la mayoría de la gente pasa por alto

• Parallel sessions: You can launch multiple Devin sessions at once and schedule operations to run in parallel, so several engineering tasks finish together. This is the quiet superpower behind clearing a backlog fast.
• ACU limits: Dig into settings to set ACU limits per session. This caps spend on any single task and keeps the agent compute unit bill predictable, even when a task runs longer than planned.
• Knowledge Base reuse: Notes you save once feed every future task, which steadily lifts Devin’s performance and cuts human error.

Devin AI Common Mistakes to Avoid

Mistake #1: Giving One Giant Open-Ended Task

Mistake #2: Skipping Reference Materials

Mistake #3: Ignoring ACU Consumption

Mistake #4: Trusting the Pull Request Without Review

Devin AI Troubleshooting

Problem: Devin Gets Stuck in a Loop

Causa: The task was too open-ended, so the agent keeps retrying without long-term reasoning toward a clear finish line.

Arreglar: Stop the session, rewrite the prompt with explicit completion criteria, and split the work into smaller coding tasks. Each smaller task gives Devin a clear finish line it can actually reach.

Problem: The Pull Request Has Unnecessary Changes

Causa: Devin’s PRs often contain extra edits and errors when the scope was loose or context was thin.

Arreglar: Review the diff in the code editor, ask Devin to revert unrelated changes, and tighten the original task description. A narrower scope almost always produces a cleaner pull request.

Problem: ACU Costs Are Higher Than Expected

Causa: Task complexity, large codebases, and long sessions all raise agent compute unit usage, sometimes 2-3x higher than the estimate.

Arreglar: Set ACU limits, point Devin at a smaller scope, and use DevinWiki research so it spends less time hunting for relevant context.

Problem: Devin Built the Wrong Thing

Causa: The prompt left business requirements unstated, so Devin filled the gap with its own assumption.

Arreglar: Give explicit acceptance criteria and reference materials, then break the work into small, verifiable goals Devin can confirm one by one.

📌 Nota: If none of these fix your issue, contact Devin AI support.

What is Devin AI?

Devin AI is an autonomous AI software engineer built by Cognition Labs that completes coding tasks end to end.

Think of it like a tireless junior engineer who plans, writes, tests, and ships code while you review the result.

Unlike a copilot that suggests lines as you type, this software engineer takes a whole task and runs with it.

You describe the goal, Devin does the task breakdown, and you step in only to review and give feedback.

Mira este breve resumen:

Incluye estas características clave:

• Autonomous Task Executor: Delegate a github issue and get a finished pull request back.
• Integrated Code Editor: Read and edit code live inside Devin’s machine.
• MCP Marketplace: Connect external tools and data through the Model Context Protocol.
• Individual Agents Builder: Create AI agents tuned to your specific needs.
• DevinWiki Deep Research: Map any codebase and generate insights before coding.
• Customizable Workspace: Shape the sandboxed compute environment per project.
• Advanced API: Trigger Devin from CI/CD with a REST call.
• Integrated Shell: Run, test, and debug code at a real terminal.

On the SWE-bench benchmark, Devin AI resolves 13.86% of real world GitHub issues end to end, roughly 7x past models.

That performance and the move from a $500 to a $20 core plan helped Cognition reach a valuation near $4 billion in March 2025.

Teams see real gains too: Nubank ran code migrations 8-12x faster and cut migration costs by over 20x using Devin.

Where Devin AI Fits Best

Maximizing utility comes down to understanding the sweet-spot use cases.

Devin shines on well-scoped jobs: fixing bugs, writing tests, code migrations, and resolving real world github issues.

It is excellent at pattern-heavy codebase transformations where the same change repeats across many files.

It can also build and deploy small apps end to end, from a blog post site to an internal tool.

Where it struggles is open-ended, ambiguous work; on complex tasks success rates fall to roughly 15-30%.

Devin also shares context poorly across sessions, so long multi-day projects need careful task breakdown.

Used inside its strengths, this autonomous AI frees engineers to spend valuable time on design and architecture.

It also learns unfamiliar technologies on its own, so it is not limited to stacks it has seen before.

That combination of autonomy and curiosity is what sets Devin apart from a plain chat interface bolted onto a large language model.

Real Examples of Devin at Work

A few examples make the use cases concrete and show where the data points back the hype.

On the SWE-bench benchmark, Devin’s performance exceeds previous models by about 7x on the same coding tasks.

In production, Nubank used Devin for code migrations and saw 8-12x faster work with over 20x lower cost.

Day to day, engineers point Devin at a github issue, get a pull request, and give feedback to refine the result.

Over many tasks that feedback compounds into a smarter Knowledge Base and steadier future results.

Para una revisión completa, consulte nuestra Devin AI review.

Devin AI Pricing

Here’s what Devin AI costs in 2026:

Pricing changed dramatically when Cognition Labs cut the entry price from $500 to $20 per month.

All plans bill on agent compute units, so two teams on the same plan can spend very differently.

Plan	Precio	Mejor para
Centro	$20 (Pay as you go)	Solo developers testing Devin on light tasks
Equipo	$500/mes	Engineering teams running steady, high-volume work
Empresa	Costumbre	Grandes empresas que necesitan seguridad and data privacy controls

Qué incluye cada plan

• Core ($20, pay as you go): Pay-as-you-go billing with extra agent compute units at $2.25 each, up to 10 concurrent sessions, IDE access, and API access. Best for a simple task or occasional project.
• Team ($500/month): Includes 250 ACUs (about 62 hours of autonomous work) at a slight $2.00 discount, plus Slack and GitHub integrations and a shared workspace for engineering teams.
• Enterprise (Custom): Volume ACU discounts, VPC deployment, SSO, fine tuning support, and compliance documentation for data privacy and large-scale software development.

Prueba gratuita: No free tier; the Core plan is the lowest-cost way to start at $20 per month.

Garantía de devolución de dinero: None published, but pay-as-you-go billing means you only pay for the ACUs you use.

One agent compute unit covers a small job like a single bug fix or a tiny site.

Longer or more complex tasks consume more units, while idle time costs nothing.

Devin offers a consumption-based pricing model, so your bill tracks real usage rather than seats.

Watch the meter early; independent testing shows real ACU consumption can run 2-3x higher than first estimates.

💰 Mejor relación calidad-precio: Core — start here to learn how Devin AI works and calibrate your real ACU usage before moving to Team for consistent, high-volume coding tasks.

The Team plan earns its keep once you run Devin steadily, since its 250 included ACUs price below the Core overage rate.

Enterprise only makes sense when data privacy, fine tuning, and dedicated infrastructure are hard requirements for your company.

Devin AI vs Alternatives

How does Devin AI compare? Here’s the competitive landscape across the top AI coding tools.

The market splits into two camps: autonomous agents that do the work, and copilots that speed up your typing.

Devin sits firmly in the agent camp, which is why it competes on task completion rather than autocomplete quality.

Mira esta comparación:

Herramienta	Mejor para	Precio	Clasificación
Devin AI	Autonomous task completion	$20/mes	⭐ 4.0
Cursor	AI-first IDE for fast in-editor coding	$20/mes	⭐ 4.5
Windsurf	Agentic IDE with a flat quota	$15/mes	⭐ 4.4
Claude Code	Terminal-based agentic coding	$20/mes	⭐ 4.6
GitHub Copilot	Inline code completion	$10/mes	⭐ 4.3
Devin	The same Cognition agent	$20/mes	⭐ 4.0
Cline	Open-source VS Code agent	Gratis	⭐ 4.2
Replit Agente	Build and deploy in the browser	$20/mes	⭐ 4.1
Tabnine	Privacy-focused completion	$9/mes	⭐ 4.0
Aider	Open-source CLI pair programmer	Gratis	⭐ 4.2
Playcode AI	In-browser JS playground	Gratis	⭐ 3.9

Selecciones rápidas:

• Mejor en general: Devin AI — the only true autonomous AI that does the full task breakdown and ships a pull request on its own.
• Mejor presupuesto: GitHub Copilot — $10/mo autocomplete that covers everyday coding tasks for most developers.
• Ideal para principiantes: Cursor — friendly AI code editor where you stay in control while learning.
• Best for code migrations: Devin AI — pattern-heavy codebase transformations are its sweet spot, with 8-12x faster migrations.

🎯 Devin AI Alternatives

Looking for Devin AI alternatives? Here are the top options across AI coding tools, from full agents to in-editor assistants:

The main split is delegation versus assistance: Devin works on its own, while most rivals help while you code.

• 🚀 Cursor: An AI code editor that suggests and edits code while you drive. Great when you want assistance, not full delegation. Developers love it for fast edits, but it never works fully on its own.
• 💰 Windsurf: An agentic IDE with a flat monthly quota instead of consumption billing. Predictable cost for steady, in-editor work. A solid middle ground for teams that want agentic help with a predictable monthly bill.
• 🧠 Código Claude: A terminal-based coding agent that lives in your shell and edits real files. Strong long-term reasoning for multi-step coding tasks. A strong pick for engineers who prefer to stay in the terminal and keep tight control.
• ⭐ Copiloto de GitHub: The most popular autocomplete asistente, built into your IDE and GitHub. Best value for everyday line-by-line help. The safe default for most developers who just want smarter autocomplete.
• 🎯 Devin: “Devin” is simply the shorter name Cognition Labs uses for Devin AI. It is the same autonomous AI software engineer described in this guide. There is nothing separate to evaluate here; it is the very tool this guide covers.
• 🔧 Cline: An open-source agent for VS Code that uses your own API keys. Free to run if you bring your own large language model. Best for developers comfortable wiring up their own keys and infrastructure.
• ⚡ Replit Agent: A browser agent that builds and deploys small apps fast. Ideal for prototypes and a quick blog post site or landing page. Great for hackathons and quick demos where shipping fast matters more than depth.
• 🔒 Tabnine: A completion assistant built around data privacy, with self-hosting for regulated teams. Strong on keeping code in house. A natural fit for regulated industries that must keep code on their own servers.
• 👶 Ayudante: A free command-line pair programmer that edits your git repo directly. Light, scriptable, and great for solo developers. Loved by terminal purists who want a free, scriptable coding companion.
• 📊 Playcode AI: An in-browser JavaScript playground with AI help. Best for quick experiments and learning, not large software development. Handy for learning and tiny experiments, less so for production work.

Para ver la lista completa, consulte nuestra Devin AI alternatives guía.

⚔️ Devin AI Compared

Here’s how Devin AI stacks up against each competitor:

The short version: pick Devin when you want to hand off whole tasks, and pick a copilot when you want faster typing.

• Devin AI vs Cursor: Cursor keeps you in control inside the IDE; Devin AI works on its own and opens a pull request. Pick Cursor for hands-on coding, Devin for delegation. If you code daily and want control, Cursor; if you want tasks done for you, Devin.
• Devin AI vs Windsurf: Windsurf bills a flat $15 quota; Devin AI bills by agent compute unit. Windsurf wins on cost predictability, Devin on autonomous task completion. Choose by billing style: flat quota with Windsurf, usage-based ACUs with Devin.
• Devin AI vs Claude Code: Claude Code runs in your terminal under your control; Devin AI runs in its own cloud machine. Claude Code for local control, Devin for set-and-forget tasks. Both reason well over long tasks; the difference is local terminal versus managed cloud.
• Devin AI vs GitHub Copilot: GitHub Copilot completes code as you type; Devin AI completes whole tasks. Copilot is cheaper and simpler, Devin handles real world github issues end to end. They pair nicely: Copilot for typing speed, Devin for whole-task delegation.
• Devin AI vs Devin: Devin AI vs Devin: identical product. “Devin” is just the brand short form from Cognition Labs, so there is no real difference to choose between. Same product, same company, so the comparison is a formality rather than a real choice.
• Devin AI vs Cline: Cline is free and self-hosted inside VS Code; Devin AI is a managed cloud agent. Cline for tinkerers on a budget, Devin for teams wanting zero setup. Free and hackable versus managed and hands-off; budget and setup tolerance decide it.
• Devin AI vs Replit Agent: Replit Agent shines on quick builds and instant hosting; Devin AI goes deeper on code migrations and complex tasks inside existing repositories. Replit wins on instant hosting; Devin wins on depth inside existing codebases.
• Devin AI vs Tabnine: Tabnine focuses on private autocomplete; Devin AI is a full autonomous agent. Choose Tabnine for strict data privacy, Devin for autonomous task completion. Privacy-first completion versus autonomous delivery; your compliance needs tip the balance.
• Devin AI vs Aider: Aider is a free CLI tool you run locally; Devin AI is a cloud agent with its own machine. Aider for control and zero cost, Devin for hands-off delivery. Aider for local, scriptable control; Devin for cloud autonomy with no maintenance.
• Devin AI vs Playcode AI: Playcode AI is a lightweight browser playground; Devin AI is a full ai software engineer. Playcode for quick tests, Devin for complex engineering tasks. Playcode for quick browser tests; Devin for real software development at scale.

Start Using Devin AI Now

That is the whole tour, from initial setup through every feature, pricing, and the closest alternatives.

You learned how to use every major Devin AI feature:

• ✅ Autonomous Task Executor
• ✅ Integrated Code Editor
• ✅ MCP Marketplace
• ✅ Individual Agents Builder
• ✅ DevinWiki Deep Research
• ✅ Customizable Workspace
• ✅ Advanced API
• ✅ Integrated Shell

Siguiente paso: Elige una función y pruébala ahora.

Most people start with the Autonomous Task Executor and a small, well-scoped github issue.

It takes less than five minutes to brief your first task.

Who Should Use Devin AI

Devin AI fits engineering teams drowning in routine maintenance, code migrations, and a backlog of small github issues.

Solo developers can start on the core plan to see how this ai software engineer handles their everyday coding tasks.

If your work is highly ambiguous or research-heavy, pair Devin with a copilot like GitHub Copilot instead.

Either way, give clear prompts, attach relevant context, and review every pull request before you merge.

Do that, and Devin AI quietly clears the work that used to eat your week.

The teams that win with Devin treat it as a capable junior engineer, not a magic box.

They brief it well, review its output, and keep feeding its knowledge so the future gets easier.

Preguntas frecuentes

What can Devin AI be used for?

Devin AI handles coding tasks like fixing bugs, code migrations, testing, and resolving real world GitHub issues. It can build and deploy apps end to end while engineers focus on harder work.

Can we use Devin AI for free?

No. Devin AI has no free tier. The Core plan starts at $20 per month using pay-as-you-go agent compute units, dropped from the original $500 price set by Cognition Labs.

How good is Devin AI?

Devin AI scores 13.86% end-to-end on the SWE-bench benchmark, about 7x past models. It excels at well-scoped tasks but hits only 15-30% success on complex tasks.

What are the limitations of Devin AI?

Devin AI struggles with open-ended work, shares context poorly across sessions, and its pull requests can include errors. It checks CI checks, not build errors, so review every change.

How does Devin AI work?

Devin AI works in its own sandboxed compute environment with a shell, code editor, and browser. You give a task, it does the task breakdown, writes code, and opens a pull request.