--- title: Using Codex with corteza --- # Using Codex with corteza corteza is provider-plural R-native agent tooling. It can run with Anthropic, OpenAI, Moonshot, Ollama, and ChatGPT-account-backed Codex access through `llm.api`. Codex support currently needs development builds of `tinyoauth` and `llm.api`, then corteza from CRAN: ```r eval=FALSE remotes::install_github("cornball-ai/tinyoauth") remotes::install_github("cornball-ai/llm.api") install.packages("corteza") ``` For most developers, subscription access via ChatGPT-account-backed Codex is dramatically more cost effective than metered API keys. Claude Code no longer offers this. A current ChatGPT subscription can drive the same R package workflow as API-key-backed models: inspect a project, edit files, run R code, run tests, and review a git diff. The provider changes, the R development loop stays portable. ## Prerequisites You need: - corteza installed. - The development build of `llm.api` with `openai_codex` support installed. corteza imports `llm.api`. - `tinyoauth` installed for ChatGPT-account-backed Codex login through `llm.api::openai_codex_login()`. - Access to the OpenAI or Codex model you plan to use. corteza talks to OpenAI and Codex through `llm.api`. It does not require the Codex CLI for the examples in this vignette. ## Authentication For ChatGPT-account-backed Codex access, run the device-code login once: ```r eval=FALSE llm.api::openai_codex_login() ``` The login prints a verification URL and code. `tinyoauth` handles the token cache, so later corteza sessions can use `provider = "openai_codex"` without another login. ## Using Codex on an R package A typical package-development session starts from a project root with: ```sh corteza::chat(provider = "openai_codex") ``` Then give corteza a scoped prompt, for example: ```text Inspect this package, run the tests, and propose the smallest change needed to fix failing checks. ``` Then you're off to the races! If you're entirely new to LLM agents in your CLI (aka, an agent harness), you may want to start with a more established harness like codex or Claude Code. ## Provider-plural workflow The same project workflow can target another provider by changing the provider and, optionally, the model: ```r eval=FALSE corteza::chat(provider = "anthropic") corteza::chat(provider = "moonshot") corteza::chat(provider = "ollama", model = "llama3.2") ``` Project config can make that choice persistent: ```json { "provider": "openai_codex", "model": "gpt-5.3-codex-spark", "tools": "core" } ``` Save that as `.corteza/config.json` in the project root when you want the CLI and `chat()` defaults to follow the project.