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OpenAI Codex Agentic CLI Goal Feature

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TL;DR

OpenAI Codex is an open-source agentic coding CLI tool that lets developers run AI-powered coding agents in their terminal. The /goal feature (enabled via a config.toml flag) introduces an autonomous looping mode where the agent iterates across multiple sessions until a high-level project goal is fully completed. Standard AI coding sessions are single-shot or short-context; the /goal command solves the problem of long-horizon software tasks by allowing the agent to persistently loop and self-direct across many sessions until a broadly defined objective is achieved, without requiring constant human re-prompting.

What it actually is

  • What: OpenAI Codex is an open-source agentic coding CLI tool that lets developers run AI-powered coding agents in their terminal. The /goal feature (enabled via a config.toml flag) introduces an autonomous looping mode where the agent iterates across multiple sessions until a high-level project goal is fully completed.
  • Who built it / maintained by: OpenAI
  • Status: beta
  • Why it matters: Standard AI coding sessions are single-shot or short-context; the /goal command solves the problem of long-horizon software tasks by allowing the agent to persistently loop and self-direct across many sessions until a broadly defined objective is achieved, without requiring constant human re-prompting.
  • How it compares to alternatives:
  • Claude Code
  • GitHub Copilot Workspace
  • Aider
  • Cursor
  • Devin
  • Cline
  • GitHub stars: 87,104 · License: Apache-2.0 · Archived: no

Links

  • Repo: https://github.com/openai/codex
  • Docs: https://openai.com/index/openai-codex

Kickstarter guide

Install the Codex CLI via npm (npm install -g @openai/codex). Navigate to your home directory and open ~/.codex/config.toml, then add [features] goals = true to enable the experimental /goal feature. Restart Codex — you will see a warning that the feature is under development — then use the /goal slash command in a session to define a high-level project objective for the agent to autonomously pursue across multiple iterations.