The best AI tools for developers

AI is now part of most development workflows, but the tools differ widely in where they sit and what they are good at. Some live inside the editor and complete code as you type. Others work as agents that read a repository, plan changes across many files and run commands. Others help after code ships, by explaining errors in production. This list picks the tools we think cover those stages well, with honest notes on their limits.

How we chose

We considered tools that a working developer or small team can adopt without a long procurement process, and judged them on:

• Quality of suggestions and changes on real codebases, not toy examples
• How well the tool understands context across files and projects
• Fit with existing workflows: editors, Git, terminals and CI
• Controls for privacy, data retention and team administration
• Transparency about what the tool is doing and why

The picks at a glance

Cursor

Cursor is a code editor built on the VS Code foundation with AI throughout: tab completion that predicts multi-line edits, a chat panel that understands the indexed codebase, and an agent mode that can make coordinated changes across files and run terminal commands with approval. Because it is a VS Code fork, most extensions, themes and keybindings carry over.

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<p class="tool-card__desc">AI-first code editor built on VS Code with agentic editing across your codebase.</p>
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  <span class="price-tag">Freemium</span>
  <a class="btn btn--secondary btn--sm" href="/go/cursor?p=embed-best-ai-tools-for-developers" rel="nofollow noopener" target="_blank" data-out="cursor">Visit website<svg class="i" width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M14 4h6v6M20 4l-9 9M18 14v5a1 1 0 0 1-1 1H5a1 1 0 0 1-1-1V7a1 1 0 0 1 1-1h5"/></svg></a>
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Why we picked it: it is one of the most complete AI-first editing environments, and the agent workflow is well suited to refactors and feature work that touch many files.

Consider: it requires switching editors, which is a real cost for teams standardised on JetBrains or other IDEs. Usage of premium models is metered on paid plans, so heavy agent use needs monitoring.

GitHub Copilot

GitHub Copilot is the most widely deployed AI coding assistant. It provides inline completions, chat, and agent capabilities inside popular editors, and integrates with GitHub itself for pull request summaries, code review suggestions and assigning issues to a coding agent.

Why we picked it: it meets developers in the editor they already use, and for organisations already on GitHub, administration and policy controls fit into existing accounts.

Consider: the strongest features tend to arrive first in VS Code and on GitHub.com, with other editors catching up later. Teams outside the GitHub ecosystem get less of the integrated value.

Claude

Claude, from Anthropic, is a general assistant with particular strength in coding and long-context reasoning. Developers use it in three ways: in the chat interface for design discussions and code review, through Claude Code in the terminal and IDE for agentic work on a repository, and via the API to build their own tools. It supports the Model Context Protocol, which lets it connect to external tools and data sources.

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<p class="tool-card__desc">AI assistant from Anthropic for writing, analysis, research and software development.</p>
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  <span class="price-tag">Freemium</span>
  <a class="btn btn--secondary btn--sm" href="/go/claude?p=embed-best-ai-tools-for-developers" rel="nofollow noopener" target="_blank" data-out="claude">Visit website<svg class="i" width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M14 4h6v6M20 4l-9 9M18 14v5a1 1 0 0 1-1 1H5a1 1 0 0 1-1-1V7a1 1 0 0 1 1-1h5"/></svg></a>
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Why we picked it: it handles large amounts of code and documentation in one context well, and its agentic tooling is designed to plan, edit, test and explain changes in a real repository.

Consider: agentic sessions on large codebases consume usage quickly, and as with any agent, changes should be reviewed before merging.

ChatGPT

ChatGPT, from OpenAI, remains a versatile tool for everyday programming questions: explaining unfamiliar code, drafting scripts, writing regular expressions and SQL, and prototyping. It can execute code for data analysis, accepts screenshots and files, and is available through an API and coding-focused agent products.

Why we picked it: broad capability across languages and adjacent tasks such as documentation, data work and diagrams, with a familiar interface.

Consider: in chat form it lacks direct repository context, so answers depend on what you paste in. Verify generated code, particularly calls to libraries whose APIs have changed recently.

Sentry

Sentry is an application monitoring platform focused on errors and performance. It groups exceptions into issues, attaches stack traces, release information and user context, and links problems to the commits that likely introduced them. Its AI features help explain the root cause of an issue and can suggest fixes based on the stack trace and your code.

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<p class="tool-card__desc">Error tracking and performance monitoring for application developers.</p>
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  <span class="price-tag">Freemium</span>
  <a class="btn btn--secondary btn--sm" href="/go/sentry?p=embed-best-ai-tools-for-developers" rel="nofollow noopener" target="_blank" data-out="sentry">Visit website<svg class="i" width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M14 4h6v6M20 4l-9 9M18 14v5a1 1 0 0 1-1 1H5a1 1 0 0 1-1-1V7a1 1 0 0 1 1-1h5"/></svg></a>
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Why we picked it: AI coding tools speed up writing code, but production is where problems surface. Sentry closes the loop by turning errors into actionable, explained issues.

Consider: event volume drives cost on paid plans, so set sampling and filters early. Self-hosting is possible but requires significant infrastructure work.

Building a practical setup

Most developers do not need every tool on this list. Common combinations:

• Editor plus monitoring: Cursor or GitHub Copilot for daily coding, Sentry for production. This covers the most ground for the least overhead.
• Agent plus assistant: Claude for agentic repository work and longer reasoning, with ChatGPT or Claude chat for quick questions and explanations.
• GitHub-centred teams: GitHub Copilot throughout, with Sentry linked to the same repositories for suspect commits and release tracking.

Responsible use

Whichever tools you choose, a few practices keep quality high:

1. Review AI-generated changes as carefully as a colleague's pull request.
2. Keep tests and CI as the source of truth; do not merge because the agent says it works.
3. Check each tool's data retention and training settings before using it on proprietary code.
4. Keep secrets out of prompts and out of files agents can read.

Verdict

Cursor and GitHub Copilot are the strongest choices for AI inside the editor, with Cursor ahead on AI-native workflows and Copilot ahead on editor coverage and GitHub integration. Claude is our pick for agentic work on real repositories, ChatGPT is a reliable general-purpose companion, and Sentry is the tool that makes sure what ships keeps working.

See full profiles for Cursor, GitHub Copilot, Claude, ChatGPT and Sentry.