Comparison · 5 min read
AI coding agents compared: how to choose, and the skills that make them better
AI coding tools have moved from autocomplete to agents that read your repository, edit files, run tests and open pull requests. There are now dozens of them, and most are genuinely capable. The most useful question isn't which one is smartest this month, since the models underneath change constantly. It's which one fits the way you work: where you want the AI to live, how much autonomy you'll give it, and whether you want to bring your own model. This guide sorts the field into groups and then covers the skills that make any of them more reliable.
The main types
| Type | Where it lives | Examples | Best for |
|---|---|---|---|
| Terminal agents | Your command line, working in your repo | Claude Code, Codex, Gemini CLI, Aider, OpenCode | Multi-step changes, scripting, developers who live in the terminal |
| Editor agents and AI IDEs | Inside your code editor | Cursor, GitHub Copilot, Cline, Kilo Code, Zed, Kiro | Seeing every change inline as you work |
| Cloud and background agents | A remote environment that opens pull requests | Codex, Devin, OpenHands | Delegating well-scoped tickets |
| App builders | The browser, from a chat prompt | Lovable, Bolt, v0, Replit | Prototypes and MVPs, including for non-developers |
| Code review bots | Your pull requests | CodeRabbit | A fast first-pass review on every PR |
Terminal agents
Terminal agents work directly in your project folder. Claude Code reads your codebase, edits files, runs commands and supports skills; our directory rates it capable at multi-step work in real repositories, with the caveat that you should configure permissions and review its diffs. OpenAI's Codex spans the cloud, a CLI and IDE extensions. Google's Gemini CLI is open source with generous free usage. Aider is a lean terminal pair programmer that commits as it goes and works with many models.
Open-source agents you can run with any model
If you want to choose your own model provider, or run a local model, the open-source agents in our catalogue are worth a look. OpenCode offers specialised agents for building, planning and exploring code, in a terminal or desktop app, with control over file edits and commands. Kilo Code works in VS Code, JetBrains and the CLI and lets you switch models mid-task. Codewhale is a terminal agent with sandboxing, permission modes and easy undo. Pi is a minimal, highly extensible harness, though it runs with your full user permissions unless you containerise it. OpenHands is a self-hosted control centre for running and managing several agents.
OpenCodeOpen sourceOpen-source terminal and desktop coding agent.★ 224k · MIT
Kilo CodeOpen sourceEditor and CLI agent with model switching.★ 27.8k · MIT
CodewhaleOpen sourceTerminal agent with sandboxing and undo.★ 41.2k · MIT
OpenHandsOpen sourceSelf-hosted control centre for coding agents.★ 92.5k · MITIn the editor
If you'd rather watch every change appear in your editor, an IDE-based tool is more comfortable. Cursor is an AI-first editor built on VS Code and is very productive for codebase-wide edits. GitHub Copilot is the easiest to roll out across a team already on GitHub. Cline is an open-source agent for VS Code that shows each step and asks for approval, with token costs on your own key to watch. Zed is a very fast editor with built-in AI, and Kiro pushes a spec-first approach that suits teams wanting more structure.
How to choose
- Where do you work? Terminal people tend to prefer terminal agents; editor people prefer IDE tools. Fighting your habits rarely pays off.
- How much autonomy will you allow? Start with an agent that asks before running commands, and loosen permissions as you build trust.
- Do you need model choice? Open-source agents and bring-your-own-key tools let you switch providers or go local; first-party agents are tuned for their own models.
- How do costs scale? Several directory verdicts warn that usage-based costs climb on long tasks. Set limits early.
- Does it support skills or project instructions? Tools that read skills or an AGENTS.md-style file let you teach them your conventions once.
Any agent that runs commands can delete files, leak secrets or install things you didn't expect. Use version control, keep secrets out of the repo, review diffs before merging, and be cautious with modes that skip permission prompts.
Skills that make any agent better
The difference between a frustrating agent and a dependable one is often process, not intelligence. Agents tend to start coding too early, fix symptoms rather than causes, and announce success before checking. Agent skills fix that by giving the agent a method. The Superpowers collection is a well-known example: a set of skills that make agents plan, test and verify like a disciplined engineer.
Writing PlansAgent skillTurns a spec into a step-by-step, test-driven plan before any code.Agent skill · MITTest-Driven DevelopmentAgent skillEnforces red, green, refactor.Agent skill · MITSystematic DebuggingAgent skillRoot cause first, then the fix.Agent skill · MITVerification Before CompletionAgent skillNo “done” without evidence.Agent skill · MITA few more are worth adding once those feel natural: brainstorming to clarify requirements before building, a code-review skill that sends a fresh subagent over your changes, and a skill for working in isolated Git worktrees so experiments don't touch your main branch.
Brainstorming Before BuildingAgent skillClarify what to build before building it.Agent skill · MITRequesting Code ReviewAgent skillA rigorous review of your changes by a subagent.Agent skill · MITGit Worktrees IsolationAgent skillIsolated workspaces for safe experiments.Agent skill · MITSuperpowersOpen sourceThe full methodology these skills come from.★ 311k · MITGive your agent better context
Agents do better work when they know your project's rules. A short instructions file in the repository, describing how to run tests, the code style and what not to touch, pays for itself quickly. AGENTS.md is a simple open format for this. Context7 supplies up-to-date library documentation to coding agents. When you hand over a task, a precise brief beats a one-line request, and our free brief builder walks you through writing one.
AGENTS.mdOpen sourceA simple open format for guiding coding agents.★ 25.3k · MIT
Context7Open sourceCurrent library docs for AI coding agents.★ 64.1k · MITAAI Coding Task Brief BuilderBrowser toolWrite a precise task brief for any coding agent.BBug reproduction templatePromptGive the agent a reproducible bug, not a vague complaint.Free promptPut it into practice
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Written by the AI Tool Garage editors. Tool details link to their catalogue pages, which are refreshed weekly; spotted something out of date? Tell us.