CLI vs IDE Coding Agents: Choose the Right One for 10x Productivity!

With my ongoing research on coding agents, I am looking for tools that boost developers productivity. Lately, I came across multiple AI coding assistants such as agents that run inside your IDE and help with your daily coding tasks. Now, what if there is similar AI buddy in the terminal? Tools like ForgeCode, Aider, and Google’s Gemini CLI promise just that.

GitHub Copilot, famously helped developers code ~55% faster and made 85% of them more confident in their code. AWS reported that using CodeWhisperer in an IDE let developers finish tasks 57% faster. Those stats jumped out at me – half again as fast or more! But which approach truly pays off in real-world work? In this article I’ll share what I’ve learned by using both IDE-based agents (like Copilot and CodeWhisperer) and CLI-based agents (like ForgeCode and Aider) in my daily workflow.

CLI Coding Agents: Power in Your Terminal

Recently, I shifted gears and tried AI agents that live in the terminal. Instead of a sidebar in my editor, these tools run as shell commands. ForgeCode was my first stop. It is a open source “AI pair programmer in your terminal”. Installing ForgeCode was easy – just `npx forgecode@latest'. Immediately I liked that it didn’t yank me into a new interface.

As one user put it, “ForgeCode gave me high-quality code suggestions extremely quickly without forcing me into a new UI”. I simply run commands like "what does this project do?" or "help me add a new feature" it gives me the output I wanted. It shows the exact same logs and output I’d see if I ran tools manually, so it feels like a natural extension of my workflow.

Beyond ForgeCode, I tried a few others. Google’s Gemini CLI (open-sourced by Google) was surprisingly polished. After installing (npm i -g @google/gemini-cli), I asked it to scaffold a FastAPI app. It instantly created project files and functions with few errors, thanks to its huge context window (1 million tokens). The CLI output was clean and well-structured, highlighting steps clearly. Gemini CLI felt fast and reliable, rarely hallucinating on common tasks.

Anthropic’s Claude Code CLI took a different approach. It needed a bit more setup (Node 18+ and an API key), but once running it was like having a very patient junior dev on call. I had Claude explain a legacy module and fix a bug; it traced through multi-file context impressively and auto-committed fixes with nice messages. It’s not instantaneous (it thinks deeply), but the output quality is high. Importantly for enterprises, Claude Code has built-in memory and security controls, which gave me confidence about using it on sensitive code.

I also tried Aider, an open-source Python CLI agent. It installed via pip install aider-install and gave me an aider command to use anywhere. Aider stands out for flexibility: it supports 100+ languages and multiple LLMs, and it even shows token usage after each session. In practice, Aider automatically committed code changes and ran linters/tests after edits, which was handy for catching mistakes. It wasn’t as “smart” at reasoning about huge multi-file context as Claude, but it was very reliable for everyday tasks and easy to integrate.

Finally, there’s OpenAI Codex CLI, which runs a local agent. With npm i -g @openai/codex, it became just another CLI tool. I asked it to generate a TODO-app scaffold; surprisingly, it created HTML, JS, and even ran tests in a sandbox before finalizing the code. Codex CLI emphasizes safety: it executes code snippets to verify them, and it asks for approval before making changes. This made its output very accurate, at the cost of a bit more waiting for those check cycles. It was comforting to know it was “thinking” and verifying.

✅ Pros of Coding CLI Agent

❌ Cons of Coding CLI Agent

IDE AI Coding Agents: Your Editor’s Sidekick

Before this CLI coding agent, IDE-integrated agents were there, after all, they’re the most familiar. GitHub Copilot (in VS Code, IntelliJ, etc.) offers inline suggestions and autocompletion. In practice, Copilot really feels like a super-smart autocomplete: I type a comment or a function signature, and it completes the body. It often “knows” my codebase and libraries, and seeing Copilot suggestions pop up right in my editor is seamless. In trials at Accenture, 90% of developers felt more fulfilled and 96% enjoyed coding more with Copilot. It’s no surprise: Copilot learns my style and stays in the IDE where I already work.

AWS CodeWhisperer is another IDE agent (now part of Amazon Q Developer that plugs into many editors (VS Code, IntelliJ, JetBrains IDEs, etc.). When I enable CodeWhisperer, I get real-time code hints and can even invoke it via comments to generate code snippets. AWS’s own testing showed devs with CodeWhisperer “were 27% more likely to complete tasks successfully and did so 57% faster” compared to those without it. In other words, these tools can really speed you up.

There are also newer IDE platforms. For example, Codeium (Windsurf) is a free AI assistant that emphasizes privacy and supports 70+ languages. It offers a plugin for VS Code and JetBrains, and even its own AI-powered IDE called Windsurf. Being free (for individuals) and available for on-premise deployment makes it appealing for enterprises. Similarly, Continue.dev is an open-source IDE framework for custom agents. It has 20K+ GitHub stars (as of 2025) and lets teams build custom assistants that live in VS Code or JetBrains, using local or cloud models. Siemens and Morningstar are early adopters of Continue’s platform, showing enterprises are indeed experimenting with IDE-centric AI that they can control.

Still, for everyday coding tasks and new feature work, IDE agents like Copilot or CodeWhisperer just work. They shave off keystrokes and give instant help, and they have broad language and framework support built-in. In my experience, enabling Copilot or CodeWhisperer in the IDE often felt like having a super-competent coding buddy on standby.

Head-to-Head: IDE vs CLI

After trying both sides, I’ve noticed some clear contrasts:

🧩 Interface & Workflow

IDE agents (Copilot/CodeWhisperer) work inside your code editor. You type in an editor window and suggestions appear; accepting them often requires clicking or keyboard shortcuts within the GUI. CLI agents (ForgeCode, Aider, etc.) run entirely in the terminal. You type an AI-specific command at your project root, and the agent “asks” follow-up questions in the shell. There’s no pop-up – changes are applied (or shown) right in the diff view, just as if you ran git tools manually. This minimal interface means no bulky UIs. As one analysis put it, CLI tools have “no chunky interface for confirming changes”, which can make the process faster for power users. In practice, using IDE agents for quick one-off suggestions (e.g. autocompleting a function) helps alot. But when I’m deep in a refactor or multi-step task, a CLI agent’s single-command workflow can feel smoother.

🔧 Setup & Integration

IDE agents require minimal setup, just install a plugin or log in (e.g. Copilot in VS Code). CLI agents often need an initial install (e.g. npm install -g) and API configuration. ForgeCode stands out for its near-zero friction: install with npx forgecode@latest and you're ready. Once installed, ForgeCode runs entirely from the terminal and works in any editor such as VS Code, IntelliJ, or Vim via shell integration, so it's IDE agnostic.

🧠 Flexibility & Choice of Models

CLI tools give users model flexibility, allowing you to choose OpenAI, Anthropic, local models, and more. For instance, tools like Aider and Codex CLI support various provider choices; you can host and run models behind your own firewall for privacy and cost control. ForgeCode supports multiple providers, lets you bring your own key, and runs locally, ensuring your code never leaves your system. In contrast, most IDE agents lock you into a specific vendor-backed system (e.g. Copilot, CodeWhisperer).

⚡ Performance & Cost Model

IDE agents are generally fast for inline suggestions because they rely on optimized, cloud-hosted models. Some CLI agents like ForgeCode or Gemini CLI also feel snappy, while others such as Claude Code CLI can lag depending on model verification and latency. ForgeCode reportedly performs nearly as fast as GPT-4 in-browser workflows, with robust context continuity and live follow-up capability. Cost-wise, IDE agents are often based on subscription or per-seat licensing (Copilot, CodeWhisperer Pro), while CLI tools can be free or pay-per-use. ForgeCode offers a free tier and paid plans for higher-volume use. Local models avoid recurring fees entirely.

🛡️ Enterprise Security & Governance

CLI agents like ForgeCode are better suited to enterprise governance, offering local execution, auditability, and integration with Git without external data transfer. ForgeCode keeps code and indexes local, optionally runs in restricted shell mode, and supports audit logs via Git commits, meaning data stays on-premises if required. IDE agents, even those with enterprise editions, still depend on vendor infrastructure and do not offer the same level of self-hosted control.

In practice, I use both. For routine coding in VS Code, I keep Copilot on; it’s like a helpful autocomplete that I barely notice until I need it. But when I’m orchestrating complex tasks (like migrating code, bulk edits, or generating entire modules), I often switch to the terminal and use a CLI agent like ForgeCode or Aider. The terminal keeps me focused on the bigger picture, and the AI can run tests or git commands under the hood.

Conclusion

AI coding assistants are no longer science fiction – they’re real tools in my toolbox now. IDE agents (Copilot, CodeWhisperer, Codeium, etc.) are great for everyday coding: they live in the editor, give instant suggestions, and take almost no setup. CLI agents (ForgeCode, Gemini, Aider, Claude Code, Rovo Dev, etc.) offer a different vibe: they sit in your terminal, giving you low-level control and often stronger customization.

Which is better? It depends on your team’s needs. If your developers love their GUI editor and want something familiar, an IDE agent will feel natural and can boost coding speed dramatically (remember that 55% faster stat?). But if your team values flexibility, privacy, or likes working in shells, CLI agents are compelling – especially since tools like ForgeCode work with any IDE and preserve your normal workflow.

If you’re a dev or tech lead, give one of these AI assistants a try. Maybe enable Copilot or CodeWhisperer in your next sprint and see how much faster your team completes tasks. Then, try a CLI agent like ForgeCode or Rovo Dev CLI on a backlogged issue. Measure the difference: many teams see 10× productivity gains on repetitive tasks with these tools. Experiment and share the results with your colleagues. The future of development is collaborative, and AI agents are here to make coding smarter and faster.

Let me know your thoughts in the comment section below!!