Top 5 IDEs for Building AI, GenAI and Agentic Applications and agentic software

Visual Studio Code (with AI Toolkit + Copilot & friends)

Visual Studio Code (VS Code) is a lightweight, cross platform code editor that has effectively become a full IDE for AI, GenAI and agentic AI development when combined with extensions like AI Toolkit, GitHub Copilot, and various LLM plugins.

AI Toolkit helps you discover, run, evaluate, and deploy AI models from providers (OpenAI, Azure, local models, etc.) directly inside VS Code. You can spin up models, test prompts, manage connections, and integrate outputs into your app without leaving the editor. Copilot (and similar AI assistants) adds context-aware code completion, inline explanations, test generation, and chat over your entire workspace, which is especially powerful in complex LLM/agentic architectures.

Advantages

• Ecosystem: Huge extension marketplace (LangChain tools, Docker, Python, Jupyter, Git, cloud CLIs) makes it a “hub” for AI workflows.
• Cross-stack: Great for Python, TypeScript/Node, Java, C#, etc., matching typical GenAI microservice architectures.

  • Integrated AI tooling: AI Toolkit + Copilot streamline prompt engineering, rapid prototyping, and refactoring.
  • Customizable: Devcontainers and remote SSH let you mirror production environments locally.

Disadvantages

• Can become heavy: With many extensions, VS Code can feel slow or memory-hungry, especially on modest laptops.

  • Setup complexity: Newcomers may find configuring Python envs, GPUs, and multiple AI extensions confusing.

    • AI dependence risk: Over-reliance on Copilot can mask gaps in understanding and occasionally introduce subtle bugs or insecure patterns.

      • Not opinionated for ML: Unlike some data-science-first tools, you still need to assemble your own stack for experiment tracking, data versioning, and MLOps.

Overall, VS Code + AI Toolkit + Copilot is one of the most versatile environments for building modern AI and agentic applications, especially in polyglot, cloud-native teams.

Best for everyone from researchers and data scientists to ML engineers and software developers building agentic systems. It's the perfect balance of a lightweight editor and a powerful IDE.

Cursor (AI-first IDE)

Cursor is an AI-first IDE built on top of a Visual Studio Code like experience but redesigned around large language models. Instead of just “adding an AI plugin,” Cursor treats the LLM as a core collaborator that understands your entire codebase and helps you write, refactor, and debug in a more conversational way.

Cursor continuously indexes your project, so when you ask it to “add logging to all API handlers” or “convert this service to use LangChain instead of custom prompts,” it can modify multiple files coherently. You can chat with the IDE about design choices, generate new modules, or quickly explore unfamiliar parts of the repo. Features like context-aware autocomplete, codebase search + natural language Q&A, and “edit this selection in-place” make it feel like an intelligent pair programmer that’s aware of global structure, not just the current file.

Advantages

• Deep AI integration: Designed around LLM workflows (multi-file edits, refactors, explanations) rather than bolted-on AI.

  • Codebase-level context: Better at understanding larger projects, so agentic systems with many services and prompts are easier to navigate.

    • Fast iteration: Great for prototyping, migrating legacy code, and experimenting with new AI/GenAI patterns.

      • VS Code familiarity: Keybindings and UI feel comfortable if you come from VS Code.

Disadvantages

• You’re tied to AI workflows: If you prefer a minimal, non-AI editor, Cursor can feel “over-assistant.”

  • Model & cost dependence: Strong value comes from high-quality LLMs, which may require paid API keys or subscriptions.

    • Ecosystem smaller than VS Code: While compatible with many extensions, the overall ecosystem and docs are still maturing.

      • Potential over-reliance: As with any AI-heavy IDE, it’s easy to accept changes without fully understanding them, risking subtle bugs.

Cursor is best for early adopters and developers who want to leverage AI as a pair-programmer to its fullest extent. It's particularly powerful for quickly building prototypes and boilerplate code for agentic systems.

JetBrains PyCharm (+ JetBrains AI)

PyCharm is JetBrain's Python-focused IDE, built for serious development of data, ML, and production AI/GenAI backends. Unlike lightweight editors, PyCharm is a fully powered environment including powerful refactoring, deep code analysis, debugger, profiler, test runner, database tools, and excellent virtualenv/conda support.

For AI and GenAI work, PyCharm shines when you’re building large, long-lived Python codebases, model-serving APIs, orchestration logic for agents, ETL pipelines, or backend services around LLMs. It integrates smoothly with Jupyter notebooks, frameworks like Django/FastAPI, and common data-science libraries. With JetBrains AI Assistant, you also get AI-powered completion, explanations, and refactor suggestions, integrated into PyCharm’s existing navigation and analysis engine.

Advantages

• Deep Python intelligence: Best-in-class code inspections, refactorings, and navigation (go to symbol, type inference, etc.) for complex Python projects.

  • Great for large projects: Scales well to big monorepos and microservice backends; tight integration with Git, tests, and CI.

    • Data & web stack ready: Good support for Jupyter, scientific mode, SQL, and web frameworks, which is ideal for GenAI APIs and tooling around agents.

      • JetBrains ecosystem: Easy to combine with DataSpell, IntelliJ, or other JetBrains IDEs in polyglot environments.

Disadvantages

• Heavier than editors: More resource-intensive than VS Code or simple editors, especially on lower-end laptops.

  • Learning curve: The rich feature set (run configs, inspections, debugger, profilers) can feel overwhelming at first.

    • Commercial licensing: Full-featured editions require a paid license (though there is a free Community Edition with fewer features).

      • Less “AI-native” than Cursor: AI Assistant is powerful, but PyCharm isn’t designed primarily around LLM workflows.

It is best for Developers and teams working on large, complex Python projects where robust code navigation, debugging, and project management are priorities. Overall, PyCharm is ideal if your AI/GenAI/agentic work is Python-centric and production-oriented, not just quick experiments.

Jupyter Notebook / JupyterLab

Jupyter Notebook / JupyterLab is an interactive computing environment built around the idea of mixing code, outputs, text, and visuals in a single “notebook.” For AI, GenAI, and agentic AI, it’s the go-to space for experimentation to try models, testing prompts, visualizing data, and iterating quickly.

Jupyter Notebook is the classic, single document interface. JupyterLab is the modern “IDE-like” version with tabs, file browser, terminals, text editors, and extensions. You can run Python (most common for AI), but also R, Julia, and others via kernels. Most GenAI libraries (OpenAI, LangChain, LlamaIndex, transformers, etc.) ship examples as notebooks, so you can copy, tweak, and run complete workflows very fast.

Advantages

• Perfect for exploration: Great for data analysis, model prototyping, prompt engineering, and quick agent behavior tests.

  • Literate programming style: Combine code, markdown, math, and charts in one document — ideal for explaining AI experiments to stakeholders.

    • Ecosystem & portability: Notebooks can run locally, in VS Code, in PyCharm, on Colab, or on cloud platforms with minimal changes.

      • Visual feedback: Inline charts, tables, and outputs make debugging model behavior and prompt responses much easier.

Disadvantages

• Not ideal for large codebases: Managing big, production-grade projects across many notebooks becomes messy and hard to maintain.

  • Hidden state issues: Executing cells out of order can create confusing, non-reproducible states.
    Weak refactoring tools: Compared to full IDEs (VS Code, PyCharm), refactoring, navigation, and testing support are limited.

    • Production gap: You still need a “real” IDE or framework to turn notebook experiments into robust, deployed services or agentic systems.

      • Overall, Jupyter Notebook / JupyterLab is best seen as your AI/GenAI lab bench, not your entire factory.

It is best for data scientists, researchers, and anyone in the experimental and prototyping phase of an AI project. It's often used in tandem with a more traditional IDE like VS Code.

AWS Cloud9 / Gitpod / Codespaces

AWS Cloud9, Gitpod and GitHub Codespaces are all cloud IDEs that let you develop AI/GenAI apps in the browser, but each serves a slightly different sweet spot.

AWS Cloud9 runs inside your AWS account and gives you a preconfigured Linux environment tightly integrated with AWS services like Lambda, ECS, SageMaker and Bedrock, so you can build and deploy directly in AWS with no local setup, plus support for real-time collaborative editing; its downsides are that it’s very AWS-centric, feels less “AI-first” than newer tools, and the UI can feel dated.

Gitpod focuses on ephemeral, reproducible dev environments spun up from your repo (GitHub, GitLab, Bitbucket), using devcontainers or similar config, every branch or PR gets a fresh workspace, which is fantastic for teams and open source, and you can use VS Code or JetBrains frontends with AI extensions like Copilot or Codeium, but it requires good upfront configuration, is dependent on a solid network connection, and long-running cloud workspaces can get expensive.

GitHub Codespaces is essentially cloud VS Code deeply integrated with GitHub repos and Actions, you launch a fully configured environment from any branch/PR in one click, sync your VS Code settings and extensions (including Copilot), and work in the browser or desktop with the same experience, making it ideal for GitHub centric AI teams building and reviewing GenAI features, the trade-offs are GitHub lock-in, usage quotas and cost for heavy workloads, and reliance on being online.

In practice, choose Cloud9 if you’re all-in on AWS, Gitpod for polyglot team/OSS workflows, and Codespaces if your world already revolves around GitHub and VS Code.

Conclusion

There here is no single best IDE for AI and GenAI development, each of the tools covered in this article solves a different part of the workflow. Many developers are using multiple IDEs for developing and maintaining same applications and services. VS Code and Cursor shine as flexible, AI-augmented environments for day-to-day coding and rapid iteration. PyCharm brings enterprise-grade structure and reliability to larger Python-centric backends. Jupyter Notebook / JupyterLab remains the ideal lab bench for experimentation, data exploration and prompt testing. Cloud IDEs such as AWS Cloud9, Gitpod and GitHub Codespaces add scalable, browser based workspaces that remove local setup and make collaboration easier. The right choice is rarely one tool alone, but a small stack that fits your team’s language preferences, cloud strategy and maturity on the AI journey. Start with the IDE that best matches your core workflow, then layer others in as your projects and ambitions grow.