CodeGPT

CodeGPT doesn't push you toward one vendor. It connects to Claude, GPT, Gemini, Grok, and open models, either through CodeGPT credits or by using your own API keys across more than a dozen providers. You can switch the model per task while the code stays where it is. That matters because model strengths differ by job, and a tool that locks you to one is a tool you outgrow.

The agent reads your codebase first, proposes a plan you can adjust, and only then writes code. It handles multi-step tasks like adding a feature or refactoring a module rather than just suggesting the next line. You stay in the loop: approve each change before it lands, which is the difference between an assistant and an autopilot.

Every change the AI suggests comes with a real-time diff preview. You accept, reject, or edit each one before it touches your files. For anyone wary of an AI quietly rewriting working code, the review step is the feature that makes agent mode usable on a real project.

Autocomplete supplies AI code completion as you type, based on your codebase and project structure. It runs separately from chat and agent tasks, and on the paid plans it's unlimited. Fast inline suggestions are still how most people spend their editing time, so this covers the highest-frequency case.

You can connect directly to your own provider accounts, or run models locally through Ollama, LM Studio, or Docker. BYOK routes usage and billing to providers you already trust, while local models keep source code off external servers. Both options matter to teams that can't send code to a third party by default.

Model Context Protocol support links the assistant to external APIs, documentation, and databases. Rules let you set coding standards the assistant follows for consistent output. Terminal access lets it run commands, but it waits for your approval before anything changes. Together they turn the editor into a place where agents can do real work under your control.