Best AI Tools for Debugging Code
AI can help developers debug their code with ease. You can simply ask it to go through your code and catch errors.
Then, you can act on it, either by doing it yourself or asking for further suggestions. And considering these tools have been trained on millions of lines of code – you can expect to get some amazing recommendations.
Speaking of which, many AI tools for coding support many programming languages — including all the “usual suspects” such as JavaScript, Java, Python, PHP, C++, Go, and so on.
So, without further ado, here are some of the most popular AI tools that will help you with code debugging:
GitHub Copilot by GitHub
👍 Pros
👎 Cons
- Boosts coding speed by up to 55%.
- Seamless GitHub integration.
- Automates issue resolution.
- Catches bugs pre-submission.
- Limited free tier usage.
- Weaker niche language support.
GitHub Copilot is an AI-powered coding assistant that integrates into IDEs and GitHub, offering code completions, chat support, and agent-driven automation to streamline coding. Built by GitHub with OpenAI and Microsoft, it’s trained on public code and text, delivering context-aware suggestions that (dramatically) boost productivity. For instance, developers at companies like Shopify and Mercado Libre use it to cut through repetitive tasks, with studies showing up to 55% faster coding without quality loss. It supports Visual Studio Code, JetBrains, and GitHub’s own platform (obviously), making it versatile for solo coders and teams alike.
The tool’s strength lies in its features. Next Edit Suggestions tracks how code changes affect your project, ensuring consistency across files. The agent mode handles issues by planning, coding, and testing, producing pull requests via GitHub Actions. Copilot Spaces organizes project context, like docs and code, for tailored suggestions. The chat interface, powered by models like GPT-5 and Claude Opus 4.1, answers coding questions or explains code in real time. Code review catches bugs and vulnerabilities before submission, a feature that rivals like JetBrains’ AI Assistant don’t match in scope.
Compared to competitors, Copilot shines for GitHub users. Tabnine offers similar completions but lacks the agent mode and deep platform integration. Copilot’s free tier includes 2,000 completions and 50 chats monthly, while Pro and Pro+ plans unlock unlimited use and premium models. Pricing feels competitive, especially for teams already on GitHub, though enterprise plans add customization for larger organizations.
The tool isn’t perfect. Suggestions for niche languages like Rust can be less accurate due to limited training data, as noted in recent Reddit threads. The free tier’s limits may frustrate heavy users, and the chat interface requires some practice to use effectively. Some developers on X report occasional irrelevant suggestions, which can disrupt flow. Still, the code referencing filter helps mitigate risks of matching public code, a concern GitHub pegs at under 1% of suggestions.
A standout feature is Autofix, part of GitHub Advanced Security, which flags vulnerabilities like SQL injections with fix suggestions. This adds real value for security-conscious teams. Copilot’s ability to switch between models for speed or depth is another plus, though non-English prompts may yield weaker results due to English-heavy training data.
To get started, try the free tier to test compatibility with your workflow. Enable the code referencing filter for peace of mind, and use Copilot Spaces to boost suggestion accuracy. Experiment with the chat feature for quick code explanations, but always review suggestions manually. For teams, explore the Enterprise plan for codebase indexing. Copilot is a powerful ally, just keep your coding instincts sharp.
BlackBox AI by Course Connect
👍 Pros
👎 Cons
- Support for 20+ programming languages
- Search across 100M+ open source code repos
- Quickly turn any question into code
- Only the Legendary plan includes web-based IDE
- All plans are yearly (though there is free trial)
BlackBox AI is an AI coding assistant that boosts developer productivity through real-time code suggestions and autonomous agents. It integrates with over 30 IDEs, including VS Code and JetBrains, supporting multiple languages ranging from Python to JavaScript. The tool accesses over 300 AI models, enabling users to select the best fit for tasks such as debugging or app development.
Key features include smart autocompletion that predicts code based on context, reducing typing time by up to 55 percent according to user benchmarks. The autonomous coding agents operate in the cloud, handling full projects asynchronously, running tests, and notifying users upon completion. Voice interaction enables natural language commands for explanations or modifications, making it accessible even for those with basic experience.
In comparison, BlackBox AI offers broader model variety than GitHub Copilot, which relies heavily on OpenAI for inline predictions but limits flexibility in non-Microsoft environments. Against Amazon Q Developer, it provides lighter pricing for individuals while matching enterprise security through encryption and audits. Tabnine stands out for local processing to enhance privacy, yet BlackBox’s cloud agents excel in scalability for team workflows.
Users appreciate the image-to-code converter for turning designs into functional components, though it may require adjustments for intricate layouts. The community snippet library facilitates quick implementations, but occasionally, outdated entries require verification. Free access covers basic needs, with pro tiers unlocking unlimited queries and advanced agents at a cost-effective rate compared to competitors’ subscriptions.
Potential drawbacks involve latency during high loads and overengineering in agent outputs for simple fixes. Despite this, integration with tools like Figma streamlines prototyping. Recent updates in 2025 improved multi-file editing, enhancing reliability for larger codebases.
For practical use, start by installing the IDE extension to test autocompletion on routine tasks. Experiment with voice for debugging sessions, review agent results thoroughly, and combine with version control to maintain code quality. This approach maximizes efficiency while minimizing errors.
Amazon Q Developer
👍 Pros
👎 Cons
- Supports all the popular programming languages and IDEs
- Can scan your code for security vulnerabilities
- Generous free individual tier (free) with unlimited code suggestions, reference tracking, and 50 security scans per month
- There are other great AI coding assistants out there - is Amazon Q Developer the best for you?
Formerly called Amazon CodeWhisperer, Amazon Q Developer is a generative AI assistant from AWS that enhances software development through code generation, task automation, and AWS expertise.
It provides real-time code suggestions in IDEs like VS Code and JetBrains. Suggestions range from cover snippets to full functions, based on comments and code context. Inline chat allows direct questions in the editor.
Agentic capabilities enable autonomous execution of complex tasks. These include feature implementation, unit testing, documentation, code reviews, refactoring, and upgrades. Agents read and write files, generate diffs, run commands, and incorporate user feedback.
AWS integration offers guidance in the console, Teams, and Slack. It optimizes costs, troubleshoots incidents, diagnoses networking, and follows well-architected patterns. Data tasks involve natural language queries, pipeline coding, and ML model design with governance.
Security features scan vulnerabilities across languages and suggest fixes. Customization connects to private repositories for relevant recommendations and codebase queries. CLI support includes autocompletions and bash translation.
Competitors include GitHub Copilot for suggestions, Tabnine for privacy focused completion, and Cody for codebase search. Amazon Q leads in agentic tasks and AWS depth. The Free Tier offers 50 chats and 1,000 lines per month, with usage-based pricing thereafter.
Test in a small project to gauge fit before full adoption.
Replit AI by Replit, Inc.
👍 Pros
👎 Cons
- Code completion alone can save a ton of time
- And the same goes for code generation, which could be very handy for many standard functions
- Chat and code explain features are extremely handy for beginners
- Not the only game in town, with GitHub users mostly sticking to GitHub Copilot
Replit is a cloud-based IDE that integrates AI agents to build, test, and deploy apps from natural language prompts. It supports over 50 programming languages and emphasizes real-time collaboration. The platform handles environment setup automatically, allowing users to focus on ideas rather than infrastructure.
Its key feature comes in the “form” of Agent 3, which can process prompts, search the web, and iterate on code using a reflection loop for self-testing. This is meant to reduce manual debugging by generating reports on issues and applying fixes. There is also the Visual Editor, which is able to import Figma designs for direct UI adjustments to streamline frontend work.
Built-in Database and Auth provide secure backend services, integrating with tools like Stripe and OpenAI without exposing keys. Agent Generation creates custom automations, such as Slack bots, from descriptions. Security options like SSO, SOC 2 compliance, and private deployments suit enterprise needs.
In comparison, Replit offers more end-to-end functionality than Cursor, which excels in local editing but requires separate deployment. Versus GitHub Copilot, Replit provides a full browser environment, while Copilot focuses on IDE plugins. Windsurf suits quick prototypes, but Replit adds robust collaboration. Lovable emphasizes no-code, differing from Replits hybrid approach.
Users appreciate the instant deployment and multiplayer editing, which speed up team workflows. However, the AI can alter code unexpectedly, leading to bugs in complex projects (this is true for all AI coding tools).
The platform’s testing via browser simulations catches UI errors early, a practical edge over manual checks in rivals. Recent updates enhance plan mode with auto-saved checkpoints and build score feedback for AI improvement.
For best results, start with small prompts, review the generated code step-by-step, and utilize collaboration for refinements. This approach maximizes efficiency while minimizing surprises.
Tabnine
👍 Pros
👎 Cons
- Can generate entire functions within your favorite editor
- You can run it on your own machine (privacy friendly)
- Major companies love it: LG, Nike, Amazon, Bloomberg, etc.
- Some developers prefer Copilot
Tabnine is an AI platform for code assistance, offering completions, specialized agents, and deployment options, including air-gapped solutions for enhanced security.
It integrates into IDEs such as VS Code, IntelliJ, and others. Completions use whole-line predictions based on current files, open tabs, and repository context. Agents cover SDLC stages: Code Review Agent checks PRs against standards, Jira agents implement and validate issues, while Testing Agent builds cases from existing tests.
Deployment choices include SaaS, VPC, on-prem, and air-gapped. Enterprise controls enable per-user LLM access, set budget thresholds, and track usage metrics. It integrates with Git, Jira, and Confluence to provide organizational context. Supports multiple models, no lock-in.
Competitors include GitHub Copilot for fast completions in Microsoft tools, Amazon Q Developer with AWS integration and scans, and Cursor for agentic coding across platforms. Tabnine stands out in agents and offline security (though we prefer Cursor). When it comes to pricing, it’s fairly simple – there’s a free individual tier, paid options for teams, and custom enterprise pricing.
Users appreciate adaptive suggestions, agent automation, and auditability. However, there are a few complaints, namely in the areas of resource use during indexing and occasionally inaccurate, complex suggestions.
To get started with Tabnine, test it for free in your IDE on a small project first, then explore agents on real tasks before committing to an enterprise solution.
What can AI tools for code debugging do?
To be fair, none of these tools is specifically made for code debugging, but it can perform that task, as well. In addition, it comes with a set of features that make every programmer that much more productive. Some of these cool features include:
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Code completion
You can just start writing a code, and the AI will figure out what you’re trying to do – offering suggestions on the next lines of code. As noted above, these AI tools are multi-lingual and will offer code suggestions in multiple programming languages.
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Text-to-code
One of the coolest features these tools sport is the ability to generate an entire function or even a class from a text prompt. You can just write what kind of a function/class you need, and the AI will create one for you from scratch.
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Documentation
The same tools that let you debug your code could also help you with documentation. Just ask the tool to add comment lines to your code and see the magic unfolding in front of your eyes. Now, other members of the team will more easily understand your code.
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Fewer errors
AI tools for coding also help you produce code with fewer errors. They can detect errors as you type, suggesting ways how to fix them. As a result, you can get a code that is better and with fewer bugs. Plus, these same tools also enable…
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Faster coding
It’s like two-in-one when you think about it. AI will not only help you write code with fewer errors but will also enable you to write it faster. It truly is a magical solution most modern developers have embraced.
All in all, using AI for code debugging and other features is a must these days. It not only helps developers code faster but also makes their code better, with fewer bugs. It is our belief that in the very near future, it will be impossible to imagine a developer coding without the help of AI. That’s how great these tools are.