Understanding Claude Code, Codex, and OpenClaw

Recently, a friend who is an independent developer asked me, “Are you using Claude Code or Codex? I’ve been struggling to choose between the two for almost a week.”

I replied: “You’re confused about the wrong direction.”

These two are fundamentally different, and with the emergence of OpenClaw, the entire discussion has reached a new level.

In the past three months, these three tools have sparked intense discussions among developers, probably the most I’ve seen in my over ten years in this field. However, most discussions have remained at the level of “which is better,” without clarifying the fundamental differences between them.

This article aims to clarify this matter.

Conceptual Framework

Before discussing each tool, I want to emphasize that these three products do not belong to the same level; comparing them directly is as odd as comparing VS Code and Docker.

They correspond to three different layers in the AI productivity stack:

• First Layer, Brain: The large language models themselves, such as Claude, GPT, and DeepSeek, responsible for understanding and reasoning.
• Second Layer, Hand: Programming agents like Claude Code and Codex, which integrate the capabilities of large models into your codebase, responsible for executing specific development tasks.
• Third Layer, Operating System: Agent runtime platforms like OpenClaw, which schedule multiple tools and models, manage long-term tasks, and run continuously.

In simpler terms: Claude Code and Codex are employees, while OpenClaw is the company. The former helps you write code, while the latter manages this group of AIs working for you.

Claude Code: The AI Engineer That Understands Your Codebase Best

Claude Code is a terminal-native programming agent launched by Anthropic in May 2025, developing faster than many anticipated. By early 2026, it had become the most widely used product in the AI programming tools market—an almost 1000-participant survey showed it had a 46% approval rate, while the second-ranked Cursor only had 19%.

What Did Claude Code Do Right?

Its core design decision prioritized “understanding the entire codebase” over simply “writing a runnable piece of code.”

For example, if you take over a chaotic Node.js project from two years ago with sparse documentation and complex dependencies, and you ask Claude Code to fix a login authentication bug, a typical AI assistant would modify the pasted code directly and provide you with a local patch. In contrast, Claude Code first reads the CLAUDE.md (your project’s rules configuration file), scans related files, and understands the upstream and downstream relationships of the authentication logic within the entire system before making changes. It knows how changes in one area might affect others.

This difference may not be apparent when handling simple functions, but it becomes significant when dealing with real projects.

Subagents + Checkpoint: Two Key Features to Note

In the second half of 2025, Claude Code introduced two important mechanisms: Subagents and Checkpoint.

Subagents allow a complex task to be divided among multiple specialized AI instances for parallel execution. For instance, when refactoring an authentication module, one Subagent handles database migration, another modifies API routes, and a third manages frontend state changes, while the main Agent coordinates and integrates the results. Each Subagent has an independent context window, allowing up to 10 to run simultaneously without interference.

Checkpoint addresses another concern: the fear that AI might break the code. It automatically archives the current state before each modification, allowing you to revert to any historical point using the Esc Esc or /rewind command. With this safety mechanism, you can confidently assign larger and more complex tasks to it.

A Practical Detail

The CLAUDE.md file is often overlooked but is crucial. You can write the project’s tech stack version, prohibited libraries, database schema summaries, and code style rules in it. Statistics show that a well-written CLAUDE.md can reduce about 80% of the “Claude forgot” issues.

Use Cases

Claude Code is best suited for quickly getting up to speed with unfamiliar codebases, handling complex bugs across multiple files, performing systematic refactoring, and development tasks that require AI to truly understand your project’s overall structure rather than just executing local commands.

It offers comprehensive access methods: Terminal CLI, VS Code plugin (Beta version released by the end of 2025), web interface, and desktop app. Subscribing to Claude Pro (starting at $20/month) allows usage, and enterprise users can also deploy it privately via Bedrock or Vertex AI.

Codex: Taking Task Outsourcing to Another Level

In 2025, OpenAI launched Codex in April (not the previous code completion model, but a new software engineering agent) and subsequently released a macOS desktop app by the end of the year, with Windows versions following in 2026.

Fundamental Differences in How Codex and Claude Code Work

Claude Code operates on a “human-machine collaboration” model: you supervise its work in real-time, reviewing each step and adjusting directions as needed. This is a co-pilot mode where the human is in charge.

Codex, on the other hand, is about “task outsourcing”: you clearly describe a task, and it executes it autonomously in an isolated sandbox environment, returning results and a PR for your review. You don’t need to monitor it continuously.

This difference significantly impacts actual workflows. Codex is suitable for tasks where you know what needs to be done but don’t want to spend energy supervising each step. For example, you can say, “Help me complete unit tests for this module” or “Help me migrate the calling method of this old interface to the new version,” then move on to other tasks and return later to check the results.

Parallelism is Codex’s Core Advantage

Codex supports genuine multi-task parallelism: multiple Agent instances work in independent cloud sandboxes, each pre-installed with your codebase and development environment. If you have five independent tasks, you can start five Agents to process them simultaneously instead of queuing them.

The desktop app’s design philosophy is that of a “command center”: the left side displays the project list, while the right side shows all running Agent threads, allowing you to switch between tasks, check progress, and comment or manually modify in the diff view.

Safety Design is Another Priority for Codex

By default, Codex’s sandbox disables external network access, and file modifications are restricted to specified directories. This design is intentional—isolated execution and presenting results after completion is much safer than operating directly on your local environment. However, for tasks requiring internet access, network permissions can be manually enabled.

Additionally, Codex includes a code review feature that can automatically review your PRs directly on GitHub, acting like an asynchronous code reviewer.

Open Source CLI Version of Codex

If you want to run Codex in a local terminal, there is a fully open-source CLI version written in Rust, supporting npm and Homebrew installations, allowing configuration of local models (including Ollama) and MCP access to external tools. Its core logic is consistent with cloud Codex but is better suited for developers wanting complete control over the execution environment.

Use Cases

Codex is suitable for clear, well-defined development tasks (writing features, fixing bugs, writing tests); for those who wish to free their hands and wait for results asynchronously; for scenarios requiring multi-task parallelism; and for teams already deeply integrated into the ChatGPT ecosystem (account interoperability without requiring additional registration).

A ChatGPT Plus subscription ($20/month) includes Codex usage credits.

OpenClaw: Not a Tool, But an Operating System for Running AI

OpenClaw is the most difficult to define and the easiest to misunderstand among the three.

It is an open-source project released by Austrian developer Peter Steinberger in November 2025 under the name Clawdbot. After its release, it went viral, surpassing 240,000 GitHub Stars within two months, becoming one of the fastest-growing projects in GitHub history (without exception, surpassing React). It was later renamed Moltbot due to a trademark complaint from Anthropic, and after Steinberger felt the name was “too awkward to pronounce,” it was changed to OpenClaw three days later.

In February this year, Steinberger announced his joining OpenAI, and the project was handed over to the open-source foundation for continued maintenance.

What Exactly is OpenClaw?

In one sentence: it is a system that allows AI to continuously work for you.

It runs locally, connects to your chosen large language models (Claude, GPT, DeepSeek, local Ollama, etc.), and integrates this AI into over 20 messaging platforms like WhatsApp, Telegram, Slack, Discord, and iMessage. You send a message to the AI, and it executes tasks—reading files, running scripts, controlling browsers, sending emails, managing calendars, monitoring servers, etc.

The fundamental difference from Claude Code and Codex is that it is not a tool that works only when your computer is on and you are staring at the screen. You can set up a Mac Mini at home to run OpenClaw 24/7 and send messages to it from anywhere via your phone to have it help you with tasks.

Four Core Components

OpenClaw’s architecture consists of four parts:

• Gateway: The entry point for receiving messages and distributing commands.
• Agent: The core that executes specific tasks.
• Skills: Expandable capability modules, with thousands available in the community-maintained ClawHub marketplace.
• Memory: Persistent user preferences, project information, and historical context across sessions.

The Skills system is the most interesting part. You can install Skills written by others to extend the AI’s capabilities or write your own. The community has Skills for handling Solana wallets, automatically posting to Instagram, monitoring GitHub Actions, and more.

Why Many People Struggle to Use It

OpenClaw has higher requirements for users; it is not a tool that you can just install and use.

The most common mistake is throwing a vague task at the AI, such as “help me manage my work.” The AI does not know what that means. The correct way to use OpenClaw is to clearly design the workflow—what the trigger conditions are, what steps to execute, and how to provide feedback on the results—and then configure this process.

Another barrier is the design of Skills. Good Skills are atomic and have single responsibilities; many beginners mix too much logic in their Skills, making it difficult to troubleshoot when issues arise.

OpenClaw’s maintainer, Shadow, once said on Discord, “If you don’t know how to run commands in the command line, this project is already too dangerous for you to use safely.” This statement is very straightforward, but it’s true.

Security Issues: A Necessary Discussion

The biggest controversy surrounding OpenClaw in recent months has been security issues.

After its launch in November last year, the first critical vulnerability (CVE-2026-25253, CVSS score 8.8) was discovered in January this year—an attacker could induce you to visit a malicious webpage, allowing JavaScript to connect to your local OpenClaw gateway via WebSocket, stealing authentication tokens and gaining complete control over your entire Agent, including disabling the sandbox and executing arbitrary commands.

In the following weeks, several other CVEs were disclosed, involving command injection, path traversal, Webhook authentication bypass, and more. The ClawHub Skills marketplace also found hundreds of malicious skill packages disguised as legitimate tools, executing data theft or installing keyloggers in the background.

Security research institutions have scanned and found that at one point, over 130,000 OpenClaw instances were directly exposed on the public internet, most of which had no authentication configured. The Ministry of Industry and Information Technology of China also issued a security warning in March this year, urging government agencies and state-owned banks to limit usage.

Currently, the recommended minimum secure version is 2026.2.26; if you are still running earlier versions, please update immediately.

It is important to clarify: these security issues do not imply that OpenClaw’s core product concept is flawed. The root of the problem lies in the combination of “great capabilities, loose default configurations, and rapid deployment”—any system with superuser privileges will encounter issues if it defaults to no authentication and unrestricted access. The team’s response speed has been quite fast, with most CVEs patched within 24 hours of disclosure.

However, this also indicates that OpenClaw is not suitable for casual installation and use. If you plan to deploy it in production, you need to seriously enhance security.

Use Cases

OpenClaw is suitable for technically capable users who can configure and maintain it for: 24/7 automation tasks (monitoring alerts, scheduled inspections, automatic daily reports); cross-platform message-triggered workflows; personal automation assistants (remotely controlling local servers via phone messages); and model-agnostic scenarios (wanting to choose models and retain data sovereignty).

It is free under the MIT license, but costs for running local models or calling cloud APIs are borne by the user, with light usage costing about $10-30/month.

Comparison Summary: A Table of Differences

Which One Should You Use?

If you are a coding developer, Claude Code is the top choice. It has the deepest understanding of codebases, is the easiest to get started with, and integrates best with daily development workflows.

If you have a bunch of well-defined development tasks, such as writing a batch of tests or migrating old interfaces, and you don’t want to supervise the process, Codex is the more suitable option. Asynchronous, parallel, and freeing your hands is its core value.

If you want AI to do a wider range of tasks for you—not just coding, but also automating operations, scheduled tasks, and cross-system collaboration, and you have the ability to ensure security—OpenClaw is the only option. It represents a different way of working with AI: not you using AI, but AI continuously working for you.

If you want to play with advanced combinations, there is a mature approach: use OpenClaw as the scheduling layer, triggering tasks that call Claude Code or Codex to execute specific programming tasks, and then have OpenClaw summarize results and send notifications. This is a true AI Agent architecture, with each of the three layers performing its role.

Final Thoughts

I have observed many developers stumbling over these three tools, and the most common issue is not choosing the wrong tool, but choosing the wrong level of usage.

Using Claude Code to “automatically manage all work”—that is what OpenClaw is designed to do; Claude Code is not intended for that. Using OpenClaw for simple bug fixes—where the complexity of configuration is not worth it—can be handled by Claude Code in two minutes.

Tools are not superior or inferior; they only fit different needs. Choosing the right level and using the right scenarios is the true path to efficiency improvement.

These three products represent not just three tools but three depths of AI involvement in development work: coding assistance, task hosting, and continuous autonomy. Where you currently stand depends on how much trust you are willing to place in AI and how much time and capability you have to manage it.

It’s not you using AI; the future is you managing a group of AIs. This shift is happening, and all three tools are early samples of this process.