Directory

AI Coding Agents Compared

A coding agent is distinct from autocomplete. Autocomplete predicts the next few lines in the file you are looking at. An agent reads across the repository, edits multiple files, runs the test suite, reads the failure, and tries again.

That difference changes what matters. Suggestion quality stops being the bottleneck and the harness becomes it: how the agent decides what to read, whether it can run your tests, what it does when a command fails, and how much it asks before changing something.

The main axis separating these tools is where they run and how much autonomy you grant. A terminal agent working in your checkout with your credentials is a different risk profile from a sandboxed cloud agent that opens a pull request.

5 tools · 3 open source · 2 self-hostable

At a glance

Coding agents compared
ToolBest forPricingRuns
Claude CodeSubstantial multi-file work — refactors, features, and debugging — where the agent needs to run tests and iterate rather than produce one diff.SubscriptionHosted or self-hosted
CursorDevelopers who want completion, chat, and agentic editing in one editor with no assembly required.SubscriptionHosted
Aideropen sourceTerminal-first developers who want an agent that is model-agnostic and whose every change is a reviewable commit.Open sourceRuns locally
Clineopen sourceWorking inside VS Code with visible, approvable steps rather than handing over autonomy.Open sourceRuns locally
OpenHandsopen sourceAutonomous runs where you want isolation by construction, and for building on an agent loop you can modify.Open sourceHosted or self-hosted

How to choose

Where it runs

Terminal, inside an editor, or in a sandbox in the cloud. Terminal and editor agents act with your local credentials and can run anything you can. Sandboxed agents are safer by construction and slower to set up.

How it asks permission

Approve-every-command is safe and tiring; full autonomy is fast until it runs a migration you did not want. The useful question is whether the granularity is configurable per tool or per command, not whether the agent is "safe".

Which models it can drive

Some agents are tied to one provider; others let you point them anywhere, including at a gateway or a local model. This decides whether your agent cost is fixed or something you can tune — see the price board for what each model actually costs.

How it handles a repository too large to read

Every serious repo exceeds the context window. Search-then-read, subagents, and compaction are the three common answers, and how well an agent does this is most of the quality difference on real codebases.

Frequently asked

What is the difference between a coding agent and code completion?

Completion predicts the next lines in the current file. An agent works across the whole repository — reading files, editing several at once, running commands and tests, and reacting to the output.

Can coding agents run my tests?

Most can execute shell commands, which includes your test suite. This is the capability that makes an agent useful rather than merely fast: it can verify its own change instead of handing you something plausible.

Are AI coding agents safe to run on a production repository?

They run with whatever permissions you give them. Work on a branch, keep the diff reviewable, and configure approval for destructive commands. Sandboxed agents that produce a pull request are the conservative option.

How much do coding agents cost to run?

Tools with their own subscription bundle the model cost. Tools where you supply an API key bill you for tokens, and agentic loops are token-hungry — long tool-calling sessions with a large cached prefix. The calculator prices that shape directly.