3 Best Command-Line AI Coding Assistants: TRAE-CLI, Gemini CLI, and Claude Code Reviewed

The world of software development is rapidly evolving, and artificial intelligence is at the forefront of this transformation. AI-powered coding assistants are no longer a futuristic concept; they are here, revolutionizing how developers write, debug, and manage code.

These CLI tools can understand natural language, generate code snippets, refactor existing code, and even help debug complex issues. Imagine having an intelligent pair programmer available 24/7, right in your terminal. This is the promise of command-line interface (CLI) AI agents.

In July 2025, several powerful CLI AI coding agents are making waves. We’ll dive into three of the most exciting: TRAE-CLI, Gemini CLI, and Claude Code. Each offers a unique approach to integrating AI into your development workflow. TRAE-CLI positions itself as a fully free and open-source alternative, aiming to be a robust tool for general software engineering tasks.

Gemini CLI, backed by Google, is gaining significant traction with its impressive capabilities and the ability to integrate various “MCP” (Model Configuration Protocol) servers, enhancing its functionality with features like checkpointing and sequential thinking. Claude Code, from Anthropic, is lauded for its sophisticated understanding of code, its ability to handle complex instructions, and its impressive performance on coding benchmarks.

This article will explore each of these CLI agents in detail. We will cover their core features, how they work, their pricing models (where applicable), and provide a comparison to help you decide which might be the best fit for your needs.

Whether you’re looking for a free, open-source solution or a cutting-edge platform with advanced features, understanding these tools is key to staying ahead in modern software development. We’ll also discuss how they stack up against each other and what their emergence signifies for the future of coding. Prepare to discover the next generation of developer tools that are set to redefine productivity and innovation.

Unpacking TRAE-CLI: The Open-Source AI Coding Companion

TRAE-CLI emerges as a compelling option for developers seeking a powerful, yet entirely free and open-source AI coding agent. Its core mission is to provide a terminal-based AI assistant capable of handling general software engineering tasks. This means it’s designed to understand your natural language instructions and translate them into actions within your development environment. The project’s commitment to open-source under the MIT license is a significant draw, inviting community contributions and fostering transparency.

Developed by ByteDance, TRAE-CLI is an LLM-based agent focused on assisting with complex software engineering workflows. It’s not just for simple code snippets but for more involved tasks like project scaffolding, refactoring, and debugging. It integrates with various tools and LLM providers, offering flexibility in how you leverage its AI capabilities. Currently in its alpha stage, TRAE-CLI is actively being developed, with a welcoming stance toward community contributions.

Key Features and Functionality of TRAE-CLI

TRAE-CLI boasts several standout features:

• Multi-LLM Support: It integrates with providers like OpenAI and Anthropic, with a recommendation for Anthropic APIs due to their perceived quality.
• Trajectory Recording: This feature allows users to save and review the agent’s execution path, aiding in debugging or understanding complex workflows.
• Flexible Configuration: Users can customize the agent to suit their needs via a config file.
• Alpha Stage Development: As an evolving project, it benefits from rapid iteration and community input.

These features make TRAE-CLI a versatile tool for developers who value both functionality and the ability to contribute to its growth.

Getting Started with TRAE-CLI: A Step-by-Step Guide

Installing TRAE-CLI is straightforward with these steps:

1. Clone the Repository: Use git clone https://github.com/bytedance/TRAE-agent.git to download the project.
2. Navigate to the Directory: Enter the cloned directory with cd TRAE-agent.
3. Install Dependencies: Run uv sync to set up all necessary dependencies. If uv isn’t installed, follow the instructions on the uv website to install it (e.g., via pip install uv).
4. Activate the Virtual Environment:

• On Windows: venv\Scripts\activate
• On macOS/Linux: source venv/bin/activate
  This step ensures the trae command is recognized.

After activation, TRAE-CLI is ready to use from your terminal.

Configuration and Usage Examples

Configuration is managed through a file (e.g., config.json) in the TRAE-CLI directory. Here, you can:

• Set API keys for OpenAI or Anthropic.
• Choose your preferred LLM provider and model (e.g., GPT-4 or Claude Sonnet).

Usage examples include:

• Basic Task: trae run "Create a Python script that calculates Fibonacci numbers" generates a script.
• Project-Specific Task: trae run "Add unit tests for the utils module" --working-dir /path/to/project targets a specific directory.
• Interactive Mode: trae interactive starts a chat-like session for ongoing tasks.

These commands showcase TRAE-CLI’s ability to handle both simple and complex software engineering tasks.

Gemini CLI: Powering Up Your Workflow with MCPs and Checkpointing

Gemini CLI has quickly become a favorite among developers for its robust AI capabilities, particularly its integration with “MCP” (Model Configuration Protocol) servers and its advanced checkpointing feature. Backed by Google, it offers a highly adaptable and intelligent coding assistant, with functionality that can be extended almost limitlessly through external configurations.

The setup requires Node.js (version 18 or higher) and is typically installed via an npx command. Its power shines through MCP server integrations and checkpointing, which we’ll explore below.

Enhancing Gemini CLI with MCP Servers

MCPs allow you to extend Gemini CLI with specialized AI models or workflows. Examples include:

• Sequential Thinking MCP: Breaks down complex tasks into steps.
• GitHub MCP: Enhances interaction with GitHub repositories.

To add an MCP:

1. Create a .gemini folder in your project root.
2. Add a settings.json file listing MCP configurations.
3. Restart Gemini CLI to apply changes.

This extensibility makes Gemini CLI a standout for tailored development needs.

The Game-Changing Feature: Checkpointing

Checkpointing provides a versioning system for AI-driven code changes:

• Enable it in settings.json with checkpointing: true.
• Use /restore to revert to previous states if needed.

This safety net allows developers to experiment with AI modifications confidently.

Practical Usage and Workflow with Gemini CLI

Launch Gemini CLI with its npx command and interact conversationally:

• “Add dark and light mode features” generates a to-do list for execution.
• Checkpoints save progress, reversible with /restore.

Its MCP flexibility and versioning make it ideal for complex projects.

Claude Code: Sophistication and Performance in AI Coding

Claude Code, from Anthropic, excels in depth and accuracy, built specifically for coding tasks. It uses advanced code traversal (e.g., grep and regex) and performs exceptionally with Claude 4 models, setting benchmarks in agentic coding.

Key Differentiators of Claude Code

• Sustained Performance: Runs consistently for hours.
• Smart Execution: Knows when to act, test, or use tools.
• Competitive Pricing: The “Max” plan offers high output for cost.

It’s flexible across terminal environments, enhancing its usability.

Navigating Claude Code: Modes and Workflow

Setup involves an installation command and API key/login. Key modes include:

• Manual: Approve each action.
• Auto: Executes confidently.
• Planning: Strategizes complex tasks.

A to-do list feature organizes multi-step instructions.

Real-World Application and Future Implications

Claude Code handles complex requests (e.g., “Replace the homepage with a hero”) and iterates effectively, acting as a collaborative partner. It’s a glimpse into natural language-driven coding’s future.

Comparing the Titans: TRAE-CLI vs. Gemini CLI vs. Claude Code

Here’s how they stack up:

Who Should Choose Which Agent?

• TRAE-CLI: Ideal for budget-conscious, open-source enthusiasts.
• Gemini CLI: Perfect for customizable, version-controlled workflows.
• Claude Code: Best for sophisticated, high-performance coding needs.

Frequently Asked Questions About CLI AI Coding Agents

As these tools become more integrated into the development landscape, several common questions arise. Understanding these can help clarify their utility and potential.

1.What is the primary benefit of using a CLI AI coding agent?

The main advantage is increased productivity and efficiency. These agents can automate repetitive coding tasks, help with debugging, generate boilerplate code, and even assist in complex problem-solving, all directly within your familiar terminal environment. They act as intelligent assistants, speeding up development cycles.

2.Can these agents replace human developers?

No, not entirely. While they are incredibly powerful, they are best viewed as tools to augment human developers. They excel at tasks that are repetitive, well-defined, or require extensive pattern matching. However, human developers are still essential for strategic thinking, complex problem-solving, architectural design, and understanding nuanced project requirements.

3.How do these agents handle project-specific context?

Most CLI agents, including Tre-CLI and Gemini CLI, require you to specify a working directory or operate within a project’s root. This allows them to access your codebase, understand file structures, and provide contextually relevant assistance. Claude Code also uses sophisticated methods to traverse and understand your entire project.

4.What are “MCPs” in the context of Gemini CLI?

MCP stands for Model Configuration Protocol. In Gemini CLI, MCPs are essentially plugins or extensions that allow you to integrate different AI models, specialized functionalities, or pre-defined workflows. This makes Gemini CLI highly adaptable and extensible, enabling it to perform a wide range of tasks beyond basic code generation.

5.Is it safe to give AI agents access to my codebase?

This is a valid concern. When using proprietary tools like Claude Code or Gemini CLI (with Google’s models), your code is sent to their servers for processing. It’s crucial to review their privacy policies and terms of service. For open-source tools like Tre-CLI, you can potentially run them with local models or have more control over data handling, but you should still be mindful of what information is shared. Always consider the sensitivity of the project you’re working on.

Conclusion: Embracing the Future of Coding with AI Assistants

TRAE-CLI, Gemini CLI, and Claude Code are reshaping software development. TRAE-CLI offers a free, transparent, community-driven solution, perfect for collaborative growth. Gemini CLI’s extensibility and checkpointing cater to adaptable, controlled workflows. Claude Code’s sophistication suits complex challenges. Your choice depends on budget, customization, and performance priorities—each tool brings the future of coding to your terminal.