The best AI tools for software engineers in 2025

AI is now regularly showing up in the day-to-day for software engineers: refactors, test coverage, architecture diagrams, sprint planning, and more. Whether or not you love the current tools, they’re changing how teams build and what’s expected from the people writing the code.

That shift goes beyond productivity. SWEs have to maintain fluency. 

A recent O’Reilly survey showed that 93% of tech leaders expect engineers to use AI regularly in their workflows this year. GitHub says Copilot speeds up coding tasks by 55%. Microsoft estimates suggest AI is already generating 30% of their code. This stuff is no longer optional.

But knowing which tools are out there isn’t the same as knowing how to use them. And using them doesn’t mean blindly trusting every output. AI literacy — being able to work with these tools, test their limits, and integrate them into your workflow — is the skill to build.

⚠️ One thing before we dive in

AI tools for engineers are changing fast. Tools launch, pivot, and get sunset in the span of weeks. New features drop quietly. Entire companies get acquired and refocused. Some of what’s hot today might be irrelevant six months from now—and some breakout tools might not even exist yet.

That’s why this guide isn’t meant to be definitive or timeless. Instead, it’s a snapshot of what’s working right now (May 15, 2025) — what we’re seeing engineers use in real workflows today. It’s organized into three categories to reflect how people actually use these tools:

• Day-to-day tools – the ones engineers turn to regularly
• Popular tools – widely used across teams and orgs
• Emerging tools – rising fast or solving specific problems in new ways

Treat this as a map, not a rulebook. You’ll likely use some tools daily and others only when needed. The real value comes from staying curious and building fluency with how these systems work, because the landscape will keep evolving.

Day-to-day tools

Tools that have become core to many engineers’ regular flow, whether for coding, planning, writing, or thinking through systems.

• ChatGPT Plus or Pro: Used for everything from debugging stack traces to drafting design docs. Helps engineers move faster across writing, learning, and planning tasks. ChatGPT o3 has quickly leveled up coding assistant capabilities.
• Claude: Strong at summarizing long threads and reviewing large files. Great for reviewing PRs, API docs, or multi-file projects with a lot of nuance.Also available as a command line tool, Claude Codes, for developers who want to streamline reviews and summaries directly in their terminal.
• GitHub Copilot: Still the leader in AI pair programming, Copilot now offers contextual coding suggestions, auto-generates documentation, and integrates pull request support directly inside your IDE.
• Cursor: A newer, fast-rising player — a code editor built fully around AI collaboration, offering deeper control over AI interactions than traditional IDE plugins.
• Windsurf: Lightweight, fast code completion and review tool that integrates with VS Code, JetBrains, and other major IDEs.
• Replit: A cloud-based IDE that integrates AI tools for real-time code generation and debugging. Popular for rapid prototyping and collaborative development.

Popular tools

Tools that are widely used across teams or in specific workflows — often not always in your editor, but essential for supporting good engineering work.

• Perplexity: A fast, citation-backed research tool. Especially helpful when comparing frameworks, exploring best practices, or tracking down unusual bugs.
• Deep Research (OpenAI): A go-to tool for staying current and making informed technical decisions—used by engineers to evaluate new tools, compare libraries, and quickly understand emerging technologies.
• DeepCode (now Snyk Code): Real-time semantic analysis of your codebase, identifying bugs and vulnerabilities that traditional linters often miss.
• Qodo (formerly CodiumAI): Generates and verifies test cases automatically, helping spot gaps in logic that might not be obvious during code review.
• Figma Make: An AI-powered coding assistant, Figma enables designers and developers to transform prompts or visual designs into functional prototypes and applications. 
• StackBlitz: Lets teams spin up secure, full-stack dev environments in seconds and provides for rapid feedback and prototyping.
• Continue: An open-source AI coding assistant that integrates directly into VS Code and JetBrains IDEs. It offers features like inline code completion, natural language editing, and a customizable agent for more substantial codebase modifications.

Emerging tools

Tools that are gaining traction for solving newer or more specialized problems, or offering a different take on familiar ones.

• Whyline: Enables natural-language debugging, letting you ask questions like "Why didn’t this user get created?" and tracing the execution flow to find the answer.
• Metabob: Identifies complex code errors and refactor suggestions before runtime errors occur.
• Mintlify: Creates clean, synced documentation from your codebase, complete with real-world usage examples.
• Swimm: Ties walkthroughs and onboarding docs directly to code changes so teams stay up to date as systems evolve.
• Warp Terminal: A modern terminal that supports context-aware commands and shareable sessions—designed to make CLI workflows more collaborative.
• CodeSee: Visualizes system dependencies and automatically maps out the ripple effects of code changes across large codebases.
• Augment Code: Acts like a copilot for pull requests, helping engineers craft better PR descriptions, summarize changes, and flag edge cases.
• Cognition: Building “Devin,” a self-directed AI software engineer that can take tickets and work independently—still early, but generating buzz.
• Graphite: Automates and improves the code review process, integrating AI suggestions into Git workflows and making PR management easier.
• Lovable: A lightweight AI teammate that comments on PRs, nudges best practices, and automates small fixes, helping shape team behavior.All Hands: An emerging tool for engineering team coordination, summarizing updates, surfacing blockers, and assisting with standups using AI.

AI tools are changing the way SWEs build

There’s no perfect stack of AI tools. What matters more is building the habit of using them regularly, critically, and with an eye toward what’s actually useful in your context.

In the past, leveling up as an engineer meant reading docs, learning frameworks, and stacking years of experience solving problems manually. That still matters. But now, a growing part of engineering skill is about learning in collaboration with a system that’s constantly changing.

AI tools are new surfaces for judgment. Knowing when to trust them, when to double-check them, and how to turn a vague output into something useful isn’t a checklist skill. It’s a sense you build. The same way you learned to name an anti-pattern when you saw one, or to refactor before adding a new feature, this is just another kind of technical instinct.

The question isn’t whether AI is coming for your workflow. It’s whether you’re close enough to the tools to shape how they get used. Because in a few years, the teams that built that muscle will be the ones moving with speed and confidence, not just reacting to change, but helping define how modern engineering is done.

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