Local vs Cloud AI Coding Agents: Claude Code, Codex, GitHub

The most useful comparison between AI coding agents is no longer a model leaderboard. It is a question of where the work runs and which system owns the files, credentials, tools, approvals, and review loop.

Claude Code describes an agentic coding tool that works across terminal, IDE, desktop, and browser surfaces. Codex cloud describes isolated cloud environments for running coding tasks in parallel. GitHub's third-party coding agents center the issue-to-pull-request workflow. Browserbase provides cloud browser, search, and page-fetching infrastructure for browser agents.

These are related products, but they occupy different layers. This guide maps the boundaries, explains the trade-offs, and shows where ClawTab fits as a local macOS and tmux control plane for Claude Code, Codex, OpenCode, and shell jobs.

Before comparing brands, separate the operating models. A single vendor can offer more than one model, but the execution boundary changes what the agent can see and how you operate it.

Operating model	Where work runs	Best for	Main trade-off
Local terminal agent	Your workstation, terminal, and local tools	Private files, local state, custom scripts, and interactive control	You own uptime, process management, and machine access
Cloud coding agent	An isolated hosted environment	Parallel tasks, clean checkouts, and work that continues while your laptop is offline	Environment setup and local state must be reproduced
GitHub-native coding agent	A repository and its issue or pull-request workflow	Async team collaboration, review, and traceable changes	GitHub becomes the primary control surface
Browser agent infrastructure	A managed browser session or web runtime	Forms, authenticated websites, search, extraction, and browser testing	Browser interaction is not the same as repository orchestration

The right choice depends on the task boundary. A browser runtime can be the right tool for a web workflow and the wrong tool for a long-lived local development environment. A cloud coding agent can be ideal for a clean pull request and inconvenient when the task depends on uncommitted files, local services, or a private keychain.

Claude Code is the clearest example of a terminal-native coding agent that has expanded across surfaces. Anthropic's official overview describes an agent that reads a codebase, edits files, runs commands, and integrates with development tools. The documentation also covers terminal, IDE, desktop, and browser usage, along with Git workflows, MCP, skills, hooks, background agents, and scheduled work.

The important distinction is that Claude Code is not limited to one execution model. A local terminal session can work directly with your checkout, local services, SSH configuration, and scripts. A desktop or web surface can provide a different handoff model. The product name alone does not tell you whether a task is local or remote; the selected surface and workflow do.

• Choose local Claude Code when the agent needs your current checkout, local test services, custom shell tools, or credentials that should remain on the Mac.
• Choose a hosted Claude Code workflow when the task can start from a clean repository and you value background execution away from the workstation.
• Add a local control layer when several sessions need persistent panes, scheduling, prompt handling, mobile visibility, or provider-level session restore.

That last requirement is the gap ClawTab targets. It does not redefine Claude Code's agent loop; it manages the local processes around it.

Codex cloud is designed for coding tasks that can run in isolated cloud environments. OpenAI positions it for parallel work and describes starting tasks from the web or connected workflows such as GitHub, Linear, and Slack. You can watch the run, inspect the diff, and open a pull request after the agent finishes.

This model changes the default question from "is my terminal still open?" to "is the task's environment reproducible?" Cloud execution is a strong fit when each task can begin from a repository checkout, install its dependencies, run its tests, and report a reviewable change without relying on uncommitted local state.

Codex cloud is a strong fit when...	Local execution is usually better when...
You want several clean attempts in parallel.	The task depends on an existing local checkout or long-lived service.
You are away from the laptop and want work to continue.	The agent needs a local Keychain secret, SSH agent, or private development tool.
The expected output is a diff or pull request.	You need to inspect or steer a live terminal process repeatedly.
Repository setup is documented and deterministic.	Environment setup is expensive, stateful, or unique to one workstation.

Cloud and local are complementary. A team can use Codex cloud for isolated implementation attempts, then use a local manager for stateful jobs, long-running watchers, and work that must stay close to the developer's machine.

GitHub's third-party coding agent documentation describes an asynchronous workflow: assign an issue or provide a prompt, let an agent make changes, review the resulting pull request, and continue the conversation through pull-request comments. GitHub documents Anthropic Claude and OpenAI Codex among the available third-party agent options.

This is a different optimization target from a local terminal. The repository, issue, pull request, review, and security checks form one traceable workstream. That is valuable for teams that want every agent task to have an explicit owner, a diff, and a review record.

• Best fit: well-scoped repository tasks, maintenance issues, test fixes, documentation changes, and review-driven team workflows.
• Less ideal: tasks that depend on a private local service, a half-finished working tree, an interactive terminal session, or a workstation-only credential.
• Operational advantage: the pull request becomes the handoff point, so humans can review, request changes, and apply normal repository protections.

GitHub-native agents can coexist with local agents. The boundary is useful: send clean, reviewable repository work to GitHub, and keep stateful or interactive work in a local terminal environment.

Browserbase describes infrastructure for agents that browse and interact with websites. Its platform combines cloud browsers with web search, page fetching, a sandbox runtime, and the controls needed to create, observe, and manage browser sessions.

That makes Browserbase relevant to queries such as AI agent browser, browser automation, authenticated web workflows, and web data extraction. It is not a direct substitute for a coding-agent manager. A browser agent can fill a form, inspect a page, or verify a deployed interface; it does not automatically provide the local tmux, repository, process, and session controls needed to operate several coding agents.

Think of browser infrastructure as a capability that can be attached to an agent workflow. A coding agent may use a browser to test a frontend. A local ClawTab job may trigger a browser workflow on the Mac. A cloud browser service may be preferable when the task needs scalable, isolated browser sessions. The choice follows the web interaction requirement, not the coding-agent brand.

ClawTab's browser triggers guide covers the complementary local approach: invoking a browser on your Mac through an API while retaining its real cookies and session. That is useful when local browser state is part of the job.

ClawTab sits below the model and beside the repository workflow. It is a local macOS control plane for running Claude Code, Codex, OpenCode, and shell processes in tmux-backed panes. The goal is not to claim cloud execution or browser infrastructure. The goal is to make local agents persistent, visible, and operable.

• Persistent processes: agents run in real tmux sessions that survive terminal disconnects and can be restored.
• Multi-provider operation: Claude Code, Codex, OpenCode, and ordinary shell commands can run side by side.
• Background scheduling: cron jobs can start local work on a schedule and keep local state available.
• Permission control: per-pane auto-yes can be enabled narrowly, while other panes continue to ask for approval.
• Remote operations: mobile and browser control lets you inspect output and answer prompts without moving the process to a cloud clone.

This makes ClawTab complementary to the other models. Use a cloud coding agent for a clean, parallel pull request. Use GitHub for review and team coordination. Use browser infrastructure for managed web interaction. Use ClawTab when the source of truth is a local Mac, a persistent tmux graph, a private toolchain, or a set of terminal agents that should keep running together.

Use the requirement, not the product name, as the first filter.

Requirement	Good starting point	Reason
Current files, local services, SSH, or private tools	Local Claude Code, Codex, or OpenCode with ClawTab	The agent can see the state that already exists on the workstation.
Several clean implementation attempts	Codex cloud or another cloud coding agent	Isolated environments make parallel comparison and cleanup easier.
Issue to pull request with team review	GitHub-native coding agent	The task, diff, review, and security checks stay in one system.
Forms, web search, page extraction, or browser testing	Browserbase or a local browser trigger	The primary capability is browser interaction, not repository orchestration.
Long-running local jobs across providers	ClawTab	tmux, cron, mobile control, and provider session restore share one local layer.
Unattended work that needs local approval later	ClawTab with remote access	The process stays local while prompts and status remain reachable.

A useful rule is to keep the agent close to the state it must understand. Put clean repository work in a reproducible cloud environment. Put private, stateful, interactive work on the Mac. Put web interaction in a browser runtime. Put review and accountability in GitHub.

Most teams do not need one universal agent runtime. They need a reliable handoff between runtimes.

1. Start with the task boundary. Decide whether the task needs local state, a clean checkout, a browser session, or a review-first workflow.
2. Run isolated repository work in the cloud. Use Codex cloud or a GitHub-native agent when the expected result is a clean diff and pull request.
3. Keep stateful work local. Use ClawTab to keep Claude Code, Codex, OpenCode, test watchers, and shell jobs running in tmux panes.
4. Attach the browser where it belongs. Use Browserbase for managed cloud browsing or local browser triggers when real local cookies and sessions are required.
5. Bring every change back to review. Whether the agent ran on a Mac or in the cloud, inspect the diff, run the relevant checks, and use the repository's normal pull-request process.

This hybrid model avoids forcing browser automation to act like a coding agent or forcing a local terminal process to behave like a cloud queue. Each layer has a clear responsibility.

Local and cloud AI coding agents are not interchangeable deployment labels. They are different operating models with different ownership of state, credentials, uptime, review, and browser access.

Use Claude Code locally when the workstation is part of the context. Use Codex cloud for isolated parallel coding tasks that can be reviewed as diffs. Use GitHub-native agents when the issue-to-pull-request loop is the product. Use Browserbase or a local browser trigger when the hard part is interacting with the web.

Use ClawTab when those local terminal agents need an operational home: persistent tmux panes, multi-provider sessions, cron scheduling, narrow permission controls, session restore, and remote monitoring. It is a local control plane, not a claim that every agent should run in the cloud or that browser infrastructure is a coding-agent manager.