Top 5 Agentic Coding CLIs Compared: Claude, Codex & more

A year ago, terminal AI mostly meant asking a model to explain errors, generate commands, or edit small functions. In 2026, leading coding CLIs have become full agent runtimes that can inspect repositories, plan work, modify files, run tests, use external tools, and verify results. 

In this article, we compare five standouts agentic coding CLIs: Claude Code, Codex CLI, GitHub Copilot CLI, Antigravity CLI, and OpenCode, across autonomy, tooling, permissions, cost, and real-world engineering workflows. 

What Makes a Coding CLI Truly Agentic?

Not every command-line interface connected to an LLM is an agent. 

A traditional AI CLI may take your input, send it to a language model, and return text. An agentic coding CLI adds an execution loop around that model. 

A simplified agent loop looks like this: 

A serious coding agent therefore needs more than good code generation. 

It needs repository awareness, tools, shell execution, file editing, state management, permissions, verification, and some mechanism for deciding what to do next. 

This is why CLI agents are particularly interesting. The terminal already sits at the center of most engineering workflows. Git, package managers, test runners, Docker, Kubernetes, cloud CLIs, linters, build systems, deployment tools, and SSH are all already there. 

Give an agent controlled access to that environment and it can potentially move from suggesting work to performing it. 

1. Claude Code

Claude Code remains one of the strongest examples of what a terminal-native software agent looks like. 

Anthropic describes it as an agentic coding tool that understands a codebase, executes routine development tasks, explains complex code, and handles Git workflows through natural-language commands. It can operate from the terminal, supported IDEs, and GitHub workflows. 

Getting Started 

Anthropic currently recommends its native installer rather than the older npm installation path. 

macOS/Linux: 

curl -fsSL https://claude.ai/install.sh | bash 

Then: 

cd my-project (Replace it with your project directory) 
claude 

A useful first prompt is: 

Study this repository. 
 
Explain: 

1. the architecture, 
2. the main execution flow, 
3. where configuration lives, 
4. how tests are organized, 
5. the three areas most likely to break if I modify authentication. 

Do not edit anything yet. 

The last sentence matters.  

Good agentic coding frequently starts with separating exploration from execution. 

Claude Code Pricing 

Claude Code is included with Anthropic’s paid Claude plans. It currently costs $20/month, while Max tiers start at $100/month. Team plans currently start at $20 per seat per month when billed annually, and Enterprise uses a seat plus usage model. Usage across Claude and Claude Code can share the same plan allowance depending on the plan. 

2. OpenAI Codex CLI

Codex CLI is OpenAI’s local software-engineering agent for the terminal. 

OpenAI describes it as a cross-platform local software agent designed to produce software changes while operating safely on the user’s machine. Codex has evolved around an explicit agent loop rather than functioning as a simple shell wrapper around a language model. 

Installation 

Codex CLI can be installed using: 

npm i -g @openai/codex 

Then launch: 

codex 

OpenAI describes Codex as the same coding agent available across the terminal, editor, ChatGPT, and other product surfaces. 

Example 

That is the type of prompt where an agent harness becomes much more useful than plain code completion. 

Pricing 

Codex is included across eligible ChatGPT plans and can also use flexible usage after included allowances are exhausted. OpenAI’s current Business plan starts at $20/user/month annually, or $25 monthly, and includes Codex access. Codex usage is increasingly metered according to model and token consumption rather than a simple fixed number of prompts. 

For technical leaders, the relevant takeaway is that cost modelling needs to consider task complexity, context length, model selection, background work, and parallel agents, not merely the number of developers licensed. 

3. GitHub Copilot CLI

GitHub Copilot CLI deserves particular attention because it has changed substantially from the original gh-copilot experience. 

The old CLI extension was primarily a suggestion assistant. 

The current Copilot CLI is a genuine coding agent. 

GitHub describes it as a terminal-native autonomous agent that can plan complex tasks, edit files, execute multistep workflows, run tests, and iterate until the work is complete. Copilot CLI became generally available in February 2026. 

Installation 

The npm installation is: 

npm install -g @github/copilot 

or on macOS/Linux: 

brew install --cask copilot-cli 

Then: 

copilot 

Authentication happens through your GitHub account.

Example

Pricing 

Copilot CLI is now included across all GitHub Copilot plans, including Free. Paid individual plans currently start at: 

• Copilot Pro     $10/month 
• Copilot Pro+    $39/month 
• Copilot Max     $100/month 

Business currently starts at $19/user/month and Enterprise at $39 per user/month, with agent usage drawing from GitHub AI Credits. 

This makes Copilot particularly interesting for organizations already standardized on GitHub. 

4. Google Antigravity CLI

This is the entry many older 2026 comparisons are likely to miss. 

If you are writing this article now, simply including “Gemini CLI” without explaining Google’s transition would already make the article outdated. 

Google announced in May 2026 that it was transitioning its terminal agent experience from Gemini CLI to Antigravity CLI. On June 18, Gemini CLI stopped serving individual free, Google AI Pro, and Google AI Ultra accounts, while enterprise and API-key workflows remained supported. 

For individual users, Antigravity CLI is now Google’s primary direction.

What Is Antigravity CLI? 

Google describes Antigravity CLI as a lightweight terminal surface for invoking, monitoring, and interacting with Antigravity agents. The CLI and Antigravity’s richer interfaces share the same underlying agent harness. 

This means it is not simply a “Gemini chatbot in the terminal.” 

The CLI focuses on keyboard-driven engineering workflows while heavier orchestration can live elsewhere. 

Installation 

macOS/Linux: 

curl -fsSL https://antigravity.google/cli/install.sh | bash 

Launch with: 

agy 

Google describes the CLI as optimized for terminal usage, SSH sessions, multi-step reasoning, multi-file modification, command execution, and persistent history. 

Example 

Antigravity Pricing 

Google currently provides an Individual tier at $0/month with basic weekly limits and access to supported models. Google AI Pro and Ultra provide larger usage allowances, while organizations can access Antigravity through Google Cloud with consumption-based pricing. 

This makes Antigravity particularly important for teams already invested in Google’s developer and cloud stack. 

5. OpenCode

The first four tools come from major AI or developer-platform companies. 

OpenCode takes a fundamentally different position. 

It is an open-source coding agent that is not tied to one model provider. It can operate as a terminal interface, desktop application, or IDE extension. 

Installation 

OpenCode can be installed with: 

curl -fsSL https://opencode.ai/install | bash 

or: 

npm install -g opencode-ai 

Then: 

cd my-project 
opencode 

OpenCode can create an AGENTS.md file after inspecting the repository so the agent has persistent project-level context. 

Example

Agentic Coding CLI Architecture

Despite different product names, the five tools increasingly converge on a similar architecture. 

This architecture explains why discussing only the underlying LLM is increasingly insufficient. 

The production quality of a coding agent depends on: 

• Model 
• Context 
• Tools 
• Agent loop 
• Permissions 
• Verification 
• Memory 
• Orchestration

A slightly stronger model with poor repository context or unsafe tooling can be less useful than a slightly weaker model inside a well-designed agent harness. 

Conclusion

Agentic coding CLIs represent a shift beyond autocomplete, moving AI from code generation into software-engineering execution. By combining reasoning, repository understanding, shell access, tools, memory, permissions, subagents, and verification, systems such as Claude Code, Codex CLI, GitHub Copilot CLI, Antigravity, and OpenCode can handle increasingly large portions of the development loop. 

The key shift is where engineers spend their time: defining goals, constraints, interfaces, and verification criteria while setting safe execution boundaries. In this article, we compare the leading agentic coding CLIs and examine where each fits in modern software development. 

Frequently Asked Questions