I tested Kiro, Opencode

Building autonomous coding agents is the current frontier, but most of them feel like glorified chat interfaces that just hallucinate file structures. I wanted to see which of the big three—Kiro, Opencode, and the new Claude Code—actually handles a real-world development workflow without needing constant hand-holding.

If you are looking to move beyond simple copilot completions and into actual agentic workflows where the AI can run terminal commands, read entire directories, and fix bugs independently, the landscape is changing fast.

The Contenders

• Claude Code: Anthropic's official CLI agent. It’s incredibly tight with the model's reasoning capabilities and feels like it "understands" the intent behind a refactor rather than just blindly applying patches.
• Opencode: A heavy hitter in the open-source space that focuses on deep integration with local environments. It’s designed to be a more expansive ecosystem for developers who want granular control.
• Kiro: The dark horse here. It focuses on speed and a very specific type of streamlined interaction that aims to reduce the "latency of thought" between a prompt and a code execution.

Performance Breakdown

When I put these through a stress test—specifically asking them to migrate a React component from JavaScript to TypeScript while updating the accompanying Vitest suite—the differences became obvious.

• Reasoning Depth: Claude Code wins here. It didn't just change types; it recognized that the test assertions needed to be updated to match the new interfaces. It thinks in terms of "impact analysis."
• Environment Autonomy: Opencode felt the most "at home" in my terminal. It’s aggressive about running npm test or ls -R to orient itself, which makes it great for a hands-on guide approach where you want the AI to explore a legacy codebase from scratch.
• Execution Speed: Kiro is noticeably snappier. If you are working on small, iterative changes or microservices, the overhead of Claude Code can sometimes feel heavy. Kiro gets in, does the job, and gets out.

How to get started with a local deployment

If you want to try the Claude Code approach, it is currently one of the most seamless ways to experience a true LLM agent. It’s a CLI-first tool, so you don't have to wrestle with a web UI.

1. Ensure you have Node.js installed on your machine.
2. Install the package via npm:

npm install -g @anthropic-ai/claude-code

3. Run the initialization command in your project root:

claude

4. Once authenticated, you can start giving high-level commands like:

"Find all unused exports in the /src folder and remove them, then run the build to ensure nothing broke."

The Verdict

Is there a single "best" agent? Not really. It depends on your specific AI workflow.

If you are working on a massive, complex repository where you need the AI to act like a Senior Engineer who understands architectural implications, Claude Code is currently the gold standard. Its ability to maintain context through complex refactors is hard to beat.

However, if you are an open-source enthusiast who wants to tweak the underlying logic and have more "raw" access to how the agent interacts with your shell, Opencode provides a much more flexible foundation.

Kiro is the tool for the developer who is tired of waiting. It's perfect for those quick-fire tasks where you don't want to wait for a massive reasoning loop to complete just to fix a typo in a config file.