As a developer constantly looking for tools to enhance my workflow, I've been fascinated by the recent emergence of AI-powered command-line interface (CLI) agents.
These tools are revolutionising how developers interact with their codebases, automating routine tasks, and providing intelligent assistance directly in the terminal.
In this blog, I'll compare two leading CLI agents: OpenAI's Codex and Anthropic's Claude Code.
But wait, what’s a CLI Agent 🤔?
CLI Agent - Next CLI Evolution
CLI Agent is the next evolution to CLI Space - an AI-powered assistants that operate directly in your Terminal.
These tools combine the efficiency of command-line operations with the intelligence of large language models (LLMs), enabling developers to perform complex tasks using natural language commands rather than memorising syntax.
They can search codebases, explain functionality, edit files, run tests, and even manage git operations—all through conversational prompts.
But how are they able to do it all at once? Do they follow the same architecture, problem-solving strategy, or what additional features can they use?
Let’s find out!
Architecture: OpenAI Codex vs Claude Code
non-geeks can skip to last subsection - Which one to choose?
OpenAI Codex vs. Claude Code - Both being CLI agents, they implement fundamentally different architectures and approaches to solve complex challenges in the field of automated software development assistance.
Let’s explore both of them at a in details!
1. Orchestration
Orchestration refers to the coordination and management of multiple tasks, workflows, or processes to ensure they work together smoothly as a unified system.
Here is all you need to know about the orchestration mechanism for both:
| Feature (Orchestration) | OpenAI Codex | Claude Code |
|---|---|---|
| Execution Environment | Cloud-based Docker containers with network isolation | Runs locally in your terminal, no cloud dependency |
| Task Handling | Parallel independent tasks in isolated environments | Single-agent execution; no built-in parallelism |
| Context Management | Uses AGENTS.md for configuration |
No native context management files |
| Task Delegation | Single agent per task; no inter-task communication | Manual task execution; no delegation features |
| Workflow Patterns | Linear execution with predefined scripts | User-driven workflows; no predefined patterns |
| Integration Surface | GitHub-centric with PR generation | Integrates with local tools and version control |
| Task Monitoring | Real-time progress tracking with logs | Terminal output; no structured monitoring |
| Error Handling | Automatic test reruns until passing results | Relies on user to handle errors manually |
| Network Requirements | Requires internet connectivity | Can operate offline after initial setup |
| Task Duration | Typically, 1–30 minutes per task | Varies based on task complexity and user input |
| Security Boundary | Network-disabled containers with explicit dependency setup | Runs within local directory; user-managed security |
| API Integration | REST-based interface through ChatGPT platform | Uses OpenAI API via provided key |
| Multi-Agent Coordination | Independent agents without coordination | No multi-agent support |
| CI/CD Integration | Through GitHub Actions via PR creation | Manual integration with CI/CD pipelines |
| Contextual Awareness | Limited to preloaded repository state | Depends on local context; no dynamic gathering |
| Tooling Ecosystem | Preinstalled dependencies in base images | Leverages existing local toolchains |
| Execution Verification | Terminal logs and test outputs as evidence | Terminal output: user verifies results |
| Task Resumption | No native resume capability | No built-in task resumption |
| IDE Integration | Through GitHub Copilot extension | No direct IDE integration |
| Enterprise Scaling | Cloud-native horizontal scaling | Scaling depends on local machine capabilities |
| Thinking Mode Control | Fixed execution patterns | User controls execution flow manually |
Key points to remember are:
Open AI Codex
- OpenAI Codex is a cloud-based coding assistant that can handle multiple tasks simultaneously, keeping each one separate and distinct. It reads code, makes changes, runs tests, and checks for errors independently.
- You can give it simple setup files (Agents.md) to help it understand your project. Codex also powers tools like GitHub Copilot, offering helpful code suggestions.
- It’s good at solving problems and even fixing bugs, making coding easier for beginners and experts.
Claude Code
- Claude Code runs on your computer, giving you more control over your workflow. It breaks down big tasks into smaller ones and keeps track of progress, allowing it to continue even if something goes wrong.
- You can guide it with simple instructions, and it manages everything independently.
- Claude works well for personal projects or automated tasks, fitting smoothly into your workflow.
Next comes the Memory management.
2. Memory Management
Memory Management (in the context of LLM) means how the system handles and organises the information it uses during processing, especially when generating responses.
It helps LLM decide what to remember at the moment, how to fit huge models into limited space, and ensure everything runs smoothly without crashing or forgetting important information too soon.
Here is all you need to know about the memory management mechanism for both:
| Memory Feature | OpenAI Codex | Claude Code |
|---|---|---|
| Context Handling | Uses files in current folder, no automatic gathering | Automatically finds and uses relevant files |
| Long-term Memory | Does not remember between sessions | Saves memory in special Markdown files |
| Codebase Understanding | Only sees files you give it | Explores whole project to understand it |
| Memory Optimization | No special memory management | Adjusts thinking time based on task complexity |
| Conversation History | Does not keep past conversation history | Remembers past chats and decisions |
| Tool Integration | Simple manual configuration | Connects with other tools for better memory handling |
| Security Considerations | Runs locally with basic safety | Stores data locally to keep it secure |
| Enterprise Scaling | Depends on your computer | Can scale up for bigger projects |
| Retrieval Mechanisms | No advanced search | Can search its saved knowledge |
| Training Data Influence | Uses general training data | Focuses on the current project |
| Context Injection | You manually provide files | Automatically includes relevant files |
| Memory Verification | No built-in verification | Tracks changes with version control |
| Token Management | Fixed token limit | Adjusts token use per task |
| Debugging Support | Basic outputs, manual debugging | Records assumptions to help debug |
| Code Pattern Recognition | Learns from pre-training data | Builds a knowledge graph from project |
Key points to remember are:
Open AI Codex
- Codex learns from a huge amount of past code.
- Codex retains information during a session using its context window, without storing data in files or between sessions.
- Codex uses strict safety checks and doesn’t store it on the computer.
- Codex checks code correctness by running tests.
Claude Code
- Claude focuses on the current project to gain a better understanding.
- Claude saves memory in special Markdown files
- Claude keeps data safe by storing it only on the computer.
- Claude can track changes and help with debugging using git
Next comes the Monitoring.
3. Monitoring
Monitoring (in the context of LLM) refers to keeping track of how the model is functioning to ensure it's performing the right tasks safely and efficiently.
Here is all you need to know about monitoring for both
| Monitoring Feat | OpenAI Codex | Claude Code |
|---|---|---|
| Progress Tracking | Shows task progress and timing in real-time | Shows each step it takes while working |
| Code Changes Visibility | Lists file edits with before/after view | Shows changes before making them |
| Security Checks | Blocks risky code patterns automatically | Warns before doing anything unsafe |
| Error Handling | Runs tests again if they fail | Explains errors in simple language |
| Integration with Tools | Works with GitHub for reviewing code | Connects to tools for tracking performance |
| User Control Levels | You approve or reject whole tasks | Let's choose between suggest, auto-edit, or full auto |
| Historical Tracking | Saves logs of everything it does | Keeps a record of chats and steps in Markdown |
| Alert System | Alerts you when tests fail | Warns if project isn’t using version control |
| Environment Setup | Uses config files to set up the workspace | Detects and adapts to the project automatically |
| Collaboration Features | Makes GitHub pull requests for team reviews | Shares updates and changes in chat |
Key points to remember are:
OpenAI Codex
- Blocks dangerous code automatically
- All-or-nothing approval — you must approve or reject the entire task
- Saves logs as terminal output only, not structured or searchable
- Relies on GitHub PRs for collaboration and code reviews
Claude Code
- Warns about unsafe actions but lets you decide
- Gives flexible control — Suggest, Auto Edit, or Full Auto modes
- Keeps a detailed history in Markdown, including conversation and steps
- Detects missing version control, promoting safer project structure
- Shares updates via chat, supporting more interactive collaboration
Next comes the most crucial aspect, Security.
4. Security
Security refers to protecting the model and its users from malicious activities, such as hackers, data leaks, or misuse.
Here is all you need to know about monitoring for both
| Security Feature | OpenAI Codex CLI | Claude Code CLI |
|---|---|---|
| Execution Isolation | Runs tasks in secure cloud containers - docker | Runs tasks in local, project-specific folders |
| Network Access | Blocks internet during tasks | Uses custom firewall rules |
| Data Privacy | Keeps code local during processing | Deletes data after 30 days, no long-term storage |
| Permission Model | Uses three-step approval system | Lets you skip repeated approvals with "don't ask again" |
| Malware Prevention | Checks for harmful code patterns | Blocks risky commands like curl and wget
|
| Enterprise Integration | Supports Portkey for compliance | Supports SSO with tools like Okta and Azure |
| Prompt Injection Defense | Uses tests to catch harmful prompts | Cleans inputs and checks context |
| Version Control Safety | Locks changes with GitHub integration | Warns if files aren’t tracked by Git |
| Network Attack Surface | Fully offline container prevents network threats | Only allows safe, approved web access |
| Data Transmission | Sends code through OpenAI’s cloud API | Connects directly with no third-party handling |
Key points to note are:
OpenAI Codex
- Uses cloud containers with network isolation.
- Processes code via cloud API.
- Integrates Portkey for compliance controls.
- Focuses on test validation and hazard analysis.
- Completely disables network access.
Claude Code
- Employs local sandboxes with firewall rules.
- Keeps everything local unless explicitly shared.
- Supports login with SSO (E.g., Okta, Azure).
- Filters unsafe inputs and blocks risky commands
- Only connects to approved websites (whitelist only)
5. Which one should you choose?
So, based on all the above differences, it's easy to understand that:
- OpenAI Codex: Choose if your main focus is cloud development, teamwork and security.
- Claude Code: Choose if your main focus is local development, control and flexible workflows.
I prefer Claude Code, and it will become relevant in the next section.
So, let’s fire up both the agent and start working.
Practical Usage Review
All the technical architecture and features are great, but it’s of no use if they fail in practice. I tested both the CLI Agents, and here is my review of them.
1. Installation Support & Easiness
After a brief Google search, I found both repositories for OpenAI Codex and Claude Code, followed the instructions provided in the README section, and got it set up in under 3 minutes each (using the npm command), which suggests the documentation is robust.
However, I didn’t like the idea that you need to define a .env file at the project or global level to start using model support. I think it should be integrated within a CLI / prompt-based.
Now let’s talk about interface & ease of use
2. Interface & Ease of Use
At first glance, the Claude Code interface seemed more polished, with a better UI/UX and navigational support, including a questionnaire, commands, and permissions.
You can see for yourself (after all setup) 👇
For OpenAI Codex, I was left hanging, mainly to figure things out myself using /help command. There were no questionnaires or commands. The only thing Codex CLI asked me was for permission.
The UI is also not polished, and navigational support is mainly provided through commands.
Worst of all, the default model (gpt-4o-latest) was not supported, so had hard time figuring right model using \model command.
You can see for yourself 👇
However, based on the first impression, nothing can be easily said. So, let’s test these beasts on some real-world developer-focused tasks.
3. Codebase Understanding
As a developer, I often have to juggle between multiple codebases and sometimes need to understand what each codebase does. This is a tiring task.
Let’s compare the performance of OpenAI Codex and Claude code.
Task Prompt
explain me entire code base. Also includes subfolders.
Keep the explanation simple, easy to understand and beginner friendly.
Follow the format : Overview, Details, How to run , Final Thoughts
Open AI Codex Output
Conversational Style → Explained well but missed the DB initialization logic present in the readme file.
Sadly, the default output is in Markdown - why use Markdown in the terminal? 😕
Claude Code Output
Instruction-Based: Detailed and well-put.
However, it missed the DB initialization logic present in the readme file, just like the codex.
Final Thoughts
Ignoring the markdown in the output, I would like to go with OpenAI Codex as it provides more detailed explanations and describes the repository in a much better manner.
However, if prompt rewriting is not an issue, I'd choose Claude Code due to its clean, friendly, and succinct output, as well as its developer-friendly experience.
Now let’s test both CLI agents on solving bugs!
4. Solving Bugs
Trust me, I spend more time fixing bugs than writing code. Though I learn a lot,
It's a good hindrance to project progress.
So, let’s see how much I can rely on OpenAI Codex and Claude Code bug fixes.
For this test, I will be using my side project -vehicle-parking-app. This will help me evaluate the performance of the agents better.
Task Prompt
'Are there any errorrs in my code?' # for codex
'Can you check what all errors are there' # for claude
OpenAI Codex Output
Codex was spot on, it identified all the bugs, fixed them, ran few verification and extra tests and generated a final summary with me in control 👇
Let’s see if Claude Code does any better.
Claude Code Output
Claude didn’t only fix the code; he optimised my entire codebase and in a very integrative manner, Insane 🤯.
Claude generated a to-do list, worked on each of them separately, used tool calls (defined in agent’s system prompt) if needed and generated a final task summary; all keeping me in the loop even on auto mode 👇
Final Thoughts
Both agents fixed the bugs, but OpenAI remain focused on whatever was the task at hand, while Claude Code took it a step further and even refactored my entire code base for optimization.
Additionally, OpenAI corrected all the errors, but never generated the step-by-step plan that Claude had created.
Seeing the capabilities of Calude Code amazed me, but specific care needs to be taken when using it for code fixes.
Failure to do so might bring unexpected changes to the codebase. Be Careful!
Fixing bugs is one thing, but what about building things from scratch?
Let’s test it out next!
5. Building Things from Scratch
Vibe coding is standard nowadays, and I do vibe code sometimes.
Let’s see if I can use both agents to build a nice task tracker - a basic CRUD app.
It's the one I coded with lovable.dev
Task Prompt
I will be giving the same prompt I gave to lovable.
Design a to-do list app with categories, drag-to-reorder tasks and progress tracker as progress bar. Ensure modern, clean
and good ui/ux functionality when creating the ui. Make sure all 3 component are functional
OpenAI Codex Output
Understood what I wanted to make, without task generation or tool calling, but it wasn't aesthetically pleasing. Now let’s test Claude's code.
Claude Code Output
The UI is nice compared to Codex; I understood the intent behind the website design and generated a step-by-step plan. Worked on each step separately to make all features functional.
Final Thoughts
Both are JS-based codes, but Claude Code took a step-by-step approach and generated modular code, while OpenAI did it all in one file, which is not a good practice.
If I had to choose a vibe coding buddy, Claude's code would be my first choice.
Anyway, let’s wrap up this comprehensive blog with final thoughts based on testing Bode CLI Agents.
Final Thoughts
Both OpenAI Codex & Claude Code are new CLI agents, but Code seems more polished and developer-friendly. On the contrary, Codex seems more of an MVP and requires time to mature.
However, the choice depends on the use case:
- If you're looking for an AI tool that integrates deeply with your coding workflow and offers hands-on assistance, Codex CLI is a good choice
- If you prefer a conversational partner to guide you through coding challenges, Claude Code might be more your style.
Ultimately, as these CLI agents evolve, it's exciting to see how they’re reshaping the way we write and interact with code, whether you want full-on collaboration or just a helpful co-pilot by your side.
With this, we have come to the end of the blog. Feel free to drop your experience using these tools in the comments.
See Ya 👋
Top comments (1)
Want a simple and short version. Get it at Codex vs Claude - which on is best