Codex: review, pricing, security and alternatives

What Codex does

Codex belongs to the coding agents family: they read a repository, edit files, run the tests and fix things until everything passes, in a terminal or an editor. On our grid it ranks #4 of the 74 projects of this family listed in the directory.

You use it from: terminal (CLI), code editor (IDE), and developer SDK. Usable models: GPT (OpenAI) and local models (Ollama, llama.cpp, vLLM…). It can run on your own machine or server.

Who is Codex for?

It is aimed at developers and technical teams who want to hand off part of the work on their codebase while keeping the final review. Typical tasks people give it:

• fix a bug from an issue or an error message
• write or extend tests
• migrate code to a new library version
• explain an unfamiliar codebase

It is a good candidate if you are comfortable in a terminal, you want the AI right inside your code editor, your data must stay on your machines, and you want the option of a local model, with no API fees.

Access and human control

According to its documentation, Codex can:

• run commands in a terminal
• read and edit files
• call online services and APIs

By default it asks for your confirmation before sensitive actions (command, send, delete). Keep that setting, at least at first: full-auto mode saves time but removes the last safeguard.

Codex pricing

The code is free, under the Apache-2.0 licence.

Getting started with Codex

1. Install it: the project ships as a compiled Rust binary (direct download or cargo install). The exact command is in the repository README.
2. Pick the model: an API key from a provider, or a local model if your machine has enough graphics memory (the VRAM calculator tells you).
3. Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
4. Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.

Security: risk level

A coding agent runs commands on your machine: work on a Git branch, review the changes and isolate it in a container for long tasks.

Points found for Codex:

• Supervised sensitive access: it can act on the system, but asks for confirmation by default.
• Outside content: it reads web pages or API responses, which may contain booby-trapped instructions (prompt injection).

Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.

Project activity and releases

Created on April 13, 2025, the openai/codex repository has 130,788 stars, 20,631 forks and 24,423 open issues, with 12,591 commits in total. It received changes this month.

Over the last 52 weeks: 11,205 commits, an average of 215.5 per week.

1,245 releases in total; 4 in the last twelve months. Latest release: rust-v0.164.0, on October 23, 2026.

Codex vs other coding agents

GitHub stars of the best-rated open-source projects in the coding agents family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.

Codex and other coding agents at a glance

The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.

How we rated it

Score: 89/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 37/40: 130,788 stars on a log scale (40 points from about 200,000).
• Activity 27/30: last commit less than a month ago, 4 releases in six months, 11,205 commits in a year.
• Framework 25/30: licence, default human approval, self-hosting, model choice and documentation site.

The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.