What Is Codex App? Features, Workflows & Best Use Cases 2026

Hey fellow AI tinkerers — if you’ve ever tried running multiple coding agents at once and ended up with a tangled repo, you know the pain. I’m Hanks, and I put AI tools through real project work, intentionally breaking things, tracking failures, and rebuilding systems.

This week, I ran OpenAI’s new Codex macOS app through parallel feature builds, mid-sprint hotfixes, and background automations—not demos, real workflows—and I wanted to see what survives, what breaks, and what actually makes my life easier.

Codex app in 60 seconds (definition + what's new)

Codex app is OpenAI's standalone macOS desktop application (launched February 2, 2026) for managing multiple coding agents in parallel without branch conflicts. Think of it as a control panel where each agent gets its own isolated workspace through Git worktrees, with diff review and scheduled background tasks.

Key differences from Codex CLI/IDE:

• Parallel threads: Multiple agents on the same repo, each with separate context
• Built-in worktrees: Agents work on isolated code copies — no interference
• Review pane: Changes queue for approval before touching local git
• Skills library: Reusable instruction bundles (Figma→code, PR review, CI analysis)
• Automations: Schedule tasks on timers, results land in review queue

Core premise: delegate long-running work to multiple agents, switch contexts freely, review before shipping.

What it's not: an IDE replacement. It syncs with VS Code/JetBrains if you have the Codex extension, but coding happens in your editor. The app is for orchestration.

Core features that matter (threads, worktrees, review, automations)

I broke these down into what actually affects daily workflow vs. what sounds good in announcements.

Project-based threads

Each project (think: a single repo or package) gets its own set of threads. Opening a thread is like starting a fresh Codex session scoped to that project directory. Your conversation history and file context stay contained.

In practice: I ran three threads at once — one refactoring authentication, one adding a feature, one investigating a performance bug. Switching between them was just clicking tabs. No "wait, which repo am I in?" confusion. No cross-contamination of context.

The catch: threads share your default sandbox settings. If you approve network access in Thread A, Thread B inherits that permission. There's no per-thread approval policy yet.

Worktrees: the boring solution that works

This is where Codex gets practical. When you start a new thread, you choose whether to work in your local checkout or create a worktree — a separate directory with its own checked-out branch.

Why this matters: you can have Agent A working on feature-x in one worktree while you're manually editing hotfix-y in your main checkout. No branch switching, no stashing, no merge conflicts between parallel tasks.

Here's what a worktree creation looks like in practice:

# Codex creates this under $CODEX_HOME/worktrees/
# Starting point: the HEAD commit of your selected branch
# State: detached HEAD (no branch pollution)

# Example: you start a thread for feature-payments
# Codex creates: ~/codex/worktrees/feature-payments/
# Based on: origin/main (or whatever branch you picked)

Each worktree has its own terminal (Cmd+J to toggle), so you can run dev servers, tests, or git operations scoped to that workspace.

The worktree gets auto-cleaned when you archive the thread — unless you pin it or create a branch from it. This keeps disk usage sane, since each worktree duplicates dependencies and build artifacts.

Real test: I ran two agents on the same repo — one doing a database migration, one implementing API changes. Both needed to run migrations and tests. With worktrees, they never collided. Without worktrees (old workflow), I'd be constantly resetting my local state.

"Threads vs projects" in plain English

Project = your codebase directory (one repo or one package in a monorepo) Thread = one task/conversation scoped to that project Worktree = optional isolated copy where that thread's changes happen

You can run unlimited threads per project. Each thread can either share your main checkout or create its own worktree. The UI makes this a dropdown choice before you start the thread.

Review pane and diff workflow

As the agent works, changes show up in an inline diff view inside the thread. You can:

• Comment on specific lines
• Approve changes and let Codex continue
• Open the diff in your editor for manual edits
• Discard the changes entirely

The key behavior: Codex doesn't touch your local git state until you explicitly merge or create a branch. This is the opposite of the CLI, which can modify files directly depending on your approval mode.

I found this useful for experimental work — letting an agent try three different approaches to the same problem, reviewing diffs, then picking the one that didn't break everything.

Skills and Automations

Skills package workflows: instructions + scripts + resources. Examples:

• implement_figma_design: Fetches Figma and generates production UI
• github_pr_review: Analyzes PR comments, applies fixes
• web_game_dev: Builds interactive web apps with testing loops

Custom skills created in the app work across CLI and IDE. The library ships with ~30 first-party skills.

Automations = skill + schedule. Example: "Daily at 9am, scan CI logs and file issues."

They run in background worktrees (git repos) or project directory (non-versioned). Results queue for review; auto-archive if nothing to report.

Test case:

Schedule: Daily 8 AM
Task: "Generate changelog from yesterday's /src/api commits, group by feature"
Result: Markdown file, ~90% accurate grouping

Limitation: Runs only while laptop is on. Cloud automations coming.

What it's great for (real scenarios)

1. Parallel features on the same repo

Two features touching shared files (auth module, API layer), tested independently before merge.

Setup: Two worktree threads from main. Agent A: OAuth flow. Agent B: rate limiting.

Result: Zero conflicts. Separate test environments. Reviewed diffs independently, merged sequentially. Time saved: ~2 hours/week.

2. Hotfix-while-building

Mid-feature, production bug hits. Need to fix without losing state.

Old: Stash → checkout main → fix → commit → checkout feature → pop stash.

Codex: New worktree thread for hotfix. Main thread keeps running. Fix, test, push. Return to feature work. No stash, no lost context.

3. Automated monitoring

Automation checking error dashboard daily, creating GitHub issues for recurring failures.

5-day test: 8 valid issues created, 2 false positives. Good enough to keep running.

4. PR review assistance

Used github_pr_review skill on 12 PRs. Caught 3 bugs, suggested 5 refactors, over-explained obvious changes in 4 cases. Useful for first-pass checks on large PRs.

What it's not for (limits + risk)

Here's where I hit walls or decided not to use it:

1. Single-agent, short tasks

If you're doing quick edits or one-off scripts, the CLI is faster. The app adds overhead for project setup, worktree creation, and review flows. Not worth it for "write a function to parse this JSON."

2. Deeply nested monorepos

The app treats each directory as a separate project. If your monorepo has 20 packages, you'll need 20 projects. Doable, but annoying.

Workaround: create one project per "package cluster" you actively work on. Don't map every folder.

3. Unattended long-running tasks (for now)

Automations require your laptop to be on and the app running. This kills use cases like "run overnight fuzz testing" or "monitor prod logs 24/7."

Cloud automations are on the roadmap, but not here yet. Until then, you need a CI pipeline for true background work.

4. Windows/Linux (currently)

macOS only at launch. Windows support is "coming soon." Linux is on the waitlist. If you're not on a Mac, this isn't an option yet.

5. Elevated permissions without rules

By default, Codex sandboxes every action: file writes limited to the project folder, network access requires approval, shell commands need permission.

For automations, you can configure "rules" to allowlist specific commands. But if your sandbox is in full-access mode, background automations run with elevated permissions. That's a security risk if you're not careful about what you schedule.

I kept mine in workspace-write mode and manually approved anything that needed network/external tools.

FAQ

Can I use Codex app without a ChatGPT subscription?

No. Requires ChatGPT Plus, Pro, Business, Enterprise, or Edu. Free/Go tiers had temporary launch access (ended mid-February 2026), now paid-only.

Do worktrees duplicate my entire repo?

Yes. Each worktree = full copy of files, dependencies, build caches. 2GB repo = 2GB per worktree. App auto-cleans unused worktrees, but disk space adds up if you pin many.

Can I move threads between laptop and cloud?

Threads started in-app stay local. Cloud threads (web interface or cloud mode) persist across devices. Can't convert local→cloud mid-task.

How do Skills differ from MCP servers?

Skills: Codex-specific instruction bundles. MCP servers: external tools providing context/capabilities to any MCP client. Codex supports both. Skills easier to create/share; MCP requires more setup but works cross-platform.

What if I close the app while an agent works?

Thread pauses. Resumes when app reopens. Automations skip runs if app isn't running at scheduled time.

System insights: when this workflow makes sense

The real decision isn't "is Codex app good?" — it's "does my work pattern need parallel agent coordination?"

This app solves a specific bottleneck: you want to delegate multiple tasks to agents without those tasks interfering with each other or your active work.

If you're working on one thing at a time, or you're comfortable with stash/branch gymnastics, the CLI is leaner.

If you're juggling feature work + hotfixes + code reviews + automated maintenance tasks, and you're tired of losing context every time you context-switch, the worktree isolation and review queue start to pay off.

The Skills library is a bonus — it makes repetitive workflows consistent — but you can ignore it entirely and still get value from the multi-threading alone.

At Macaron, we built our AI to remember your context across conversations and create custom tools with a single sentence—so when you're jumping between multiple tasks, your ideas don't stall in chat limbo. If you're testing whether this multi-agent workflow fits your pattern, try Macaron's free tier with your actual tasks and see if the context holds.