How to Run Claude Code and Codex Side by Side

AI coding agents have transformed how we write software, but most developers still use them one at a time. What if you could run multiple agents simultaneously, each handling different aspects of your project? This guide shows you how to set up and manage parallel AI coding sessions for faster, more efficient development.

Why Run Multiple Agents at Once?

Running multiple AI coding agents in parallel isn't just about speed—it's about leveraging different strengths and creating better workflows.

Different Agents, Different Strengths

Each AI coding agent has its own capabilities and expertise. Claude Code excels at understanding complex codebases and refactoring, while Codex might be better suited for generating boilerplate or writing tests. By running both simultaneously, you can route tasks to the agent best equipped to handle them.

True Parallel Development

Instead of waiting for one agent to finish before starting the next task, you can work on multiple parts of your project at once. While one agent implements a new feature, another can update documentation, write tests, or refactor related code. This parallelization can dramatically reduce development time for larger projects.

Built-in Code Review

One of the most powerful use cases is having one agent write code while another reviews it. This creates a feedback loop where the reviewing agent can catch issues, suggest improvements, or identify edge cases the first agent might have missed. It's like pair programming with two AI assistants.

Context Separation

Different agents can maintain separate contexts for different parts of your application. One agent can focus deeply on the frontend while another handles backend logic, preventing context mixing and keeping each agent's understanding sharp and relevant to its specific domain.

Prerequisites

Before you start running multiple agents in parallel, you'll need a few things set up:

Install the AI Coding Tools

First, ensure you have the CLI tools installed:

# Install Claude Code
npm install -g @anthropic-ai/claude-code

# Install Codex (or your preferred alternative)
npm install -g openai-codex

Verify the installations:

claude-code --version
codex --version

A Terminal with Session Management

You'll need a terminal that supports multiple sessions or panes. Options include:

• tmux or zellij for session management
• A native terminal with split-pane support (iTerm2, Windows Terminal)
• A specialized terminal for agent workflows (like Agents UI)

For this tutorial, we'll focus on zellij and Agents UI, as they provide the best experience for managing multiple AI agent sessions.

The Basic Approach: Terminal Multiplexers

The simplest way to run multiple agents is using a terminal multiplexer like tmux or zellij. These tools let you split your terminal into multiple panes, each running its own agent session.

Using Zellij

Install zellij if you haven't already:

# macOS
brew install zellij

# Linux
cargo install zellij

Start a new zellij session:

zellij

Split your terminal into panes:

• Press Ctrl-p then d to split down (horizontal split)
• Press Ctrl-p then r to split right (vertical split)
• Press Ctrl-p then arrow keys to navigate between panes

In one pane, start Claude Code:

claude-code

In another pane, start Codex:

codex

Using tmux

For tmux users, the workflow is similar:

# Start tmux
tmux new -s agents

# Split horizontally
Ctrl-b "

# Split vertically
Ctrl-b %

# Navigate panes
Ctrl-b arrow-keys

This basic approach works, but it has limitations. Managing complex layouts, organizing multiple projects, and maintaining context across sessions becomes challenging as your workflow grows.

The Better Approach: Project-Based Terminal Organization

This is where a specialized terminal app like Agents UI shines. Instead of manually managing panes and sessions, Agents UI provides project-based organization specifically designed for AI coding workflows.

Session Persistence

Agents UI bundles zellij under the hood, providing persistent sessions that survive app restarts. Your agent conversations and terminal states are preserved, so you can close the app and return exactly where you left off.

Multi-Session Support

Create dedicated sessions for different projects or different aspects of the same project. Each session maintains its own:

• Terminal state and history
• Agent conversations and context
• Working directory and environment variables
• Custom layouts and pane configurations

Built-in Monaco Editor

Switch between terminal agents and code editing without leaving the app. The integrated Monaco editor lets you review agent-generated code, make quick edits, and keep everything in one workspace.

SSH and File Transfer

Working on remote servers? Agents UI includes SSH support with file transfer capabilities, so your agents can work on remote codebases as easily as local ones.

Setting Up Your Multi-Agent Workspace in Agents UI

Here's how to set up an optimal workspace for running multiple agents:

1. Create a New Session

Launch Agents UI and create a session for your project. Name it something descriptive like "myapp-agents".

2. Split Your Layout

Use the zellij shortcuts (Ctrl-p + d or Ctrl-p + r) to create your preferred layout. A common setup is:

• Left pane: Claude Code (for implementation)
• Right pane: Codex (for testing/review)
• Optional bottom pane: Regular terminal for git commands, builds, etc.

3. Initialize Your Agents

In each pane, navigate to your project directory and start the appropriate agent:

# Left pane
cd ~/projects/myapp
claude-code

# Right pane
cd ~/projects/myapp
codex

4. Set Agent Contexts

Give each agent a clear role from the start:

# To Claude Code:
"You'll be implementing new features for this web application.
Focus on the backend API and database models."

# To Codex:
"You'll be writing tests and reviewing code changes.
Focus on test coverage and identifying potential bugs."

Practical Workflow Examples

Now that you have multiple agents running, here are proven workflows that take advantage of parallel sessions:

Agent A Writes Code, Agent B Reviews It

This is the most powerful workflow for quality code generation.

In Claude Code (Implementation Agent):

"Implement a new user authentication endpoint using JWT tokens.
Include input validation and error handling."

Wait for Claude Code to generate the implementation. Once complete, copy the file path.

In Codex (Review Agent):

"Review the code in src/auth/jwt-handler.js. Check for:
- Security vulnerabilities
- Edge cases not handled
- Test coverage gaps
- Performance concerns"

The review agent will analyze the implementation and provide feedback. You can then take that feedback back to the implementation agent for improvements.

Agent A Handles Frontend, Agent B Handles Backend

Parallel development across the stack:

Frontend Agent:

"Create a React component for the user profile page.
It should display user info and allow editing."

Backend Agent:

"Create an API endpoint for updating user profiles.
Include validation and database updates."

Both agents work simultaneously on their respective layers. Once done, you have a complete feature ready for integration testing.

One Agent for Coding, One for Research and Documentation

Keep your codebase documented without breaking flow:

Coding Agent:

"Implement the payment processing module using Stripe."

Documentation Agent:

"Write comprehensive documentation for the payment module,
including setup instructions, API examples, and error handling."

While the first agent codes, the second agent can research best practices, write docs, or prepare migration guides.

Tips for Managing Multiple Agents

Running multiple agents efficiently requires some best practices:

1. Give Clear, Specific Roles

Don't let agents overlap or conflict. Assign each agent a specific domain or responsibility and remind them of their role when needed.

2. Use Separate Working Directories (When Appropriate)

For some workflows, having agents work in separate directories prevents conflicts:

# Agent 1
cd ~/projects/myapp/backend

# Agent 2
cd ~/projects/myapp/frontend

3. Watch for File Conflicts

If multiple agents are modifying code, be careful they don't edit the same files simultaneously. Use git status frequently and review changes before committing.

4. Maintain Context Boundaries

Don't feed the same detailed context to both agents unless necessary. Keep each agent focused on its specific domain to prevent confusion and maintain sharp, relevant responses.

5. Use Git Branches

For experimental work or when agents might conflict, use separate branches:

# Agent 1
git checkout -b feature/backend-api

# Agent 2
git checkout -b feature/frontend-ui

6. Regular Checkpoints

Periodically review what both agents have done, commit working changes, and ensure they're still aligned with your goals.

7. Terminal Naming

Use zellij's tab naming feature to keep track of which agent is which:

# Rename current pane
Ctrl-p c
# Then type the new name: "Claude - Backend"

Advanced: Three-Agent Workflows

Once you're comfortable with two agents, you can add a third for even more sophisticated workflows:

• Agent 1: Feature implementation
• Agent 2: Test writing
• Agent 3: Documentation and examples

Or:

• Agent 1: Backend API
• Agent 2: Frontend UI
• Agent 3: Integration and deployment scripts

The key is maintaining clear separation of concerns and preventing the agents from stepping on each other's toes.

Conclusion

Running multiple AI coding agents in parallel transforms your development workflow from sequential to parallel, from single-perspective to multi-perspective. Whether you use basic terminal multiplexers or a specialized tool like Agents UI, the key is organization, clear role assignment, and thoughtful workflow design.

Start with two agents handling complementary tasks—perhaps one writing code and one reviewing it. As you get comfortable with the workflow, experiment with more complex setups. You'll find that the productivity gains and code quality improvements make the initial setup effort worthwhile.

The future of AI-assisted development isn't about having one powerful assistant—it's about orchestrating multiple specialized agents working together, each contributing its unique strengths to your project.