Use Claude Code and Codex Side by Side

Using Claude Code and Codex side by side is not mainly a model comparison problem. It is a context management problem.

Two agents can be useful for different tasks, different review passes, or different implementation approaches. They can also create a mess if they write into the same checkout, respond to the same vague prompt, or leave you with two overlapping diffs and no clean way to decide what happened.

The practical workflow is simple: keep execution local, keep sessions separate, keep branches or worktrees separate, and use one control surface to supervise the work.

Junction is built for that kind of separation. It runs a daemon on the machine that owns the code, then gives you a browser interface for local agent sessions, output, approvals, Git state, and diff review. The goal is not to blend Claude Code and Codex into one ambiguous conversation. The goal is to keep each run understandable.

Side by side does not mean same context

The fastest way to lose control is to ask two agents to work on the same files in the same checkout at the same time.

That creates several problems:

• one agent may overwrite the other's changes,
• summaries become hard to trust,
• test results may apply to a mixed state,
• review gets harder because the branch no longer has one story,
• and you cannot tell whether a problem came from the prompt, the implementation, or the collision.

A better rule is one task, one session, one branch or worktree.

Claude Code's own workflow docs describe worktree use for isolated sessions and subagents, including separate worktrees for parallel work. The current Claude Code common workflows docs explain that worktrees let sessions operate in separate branches and directories. Codex CLI is also local-first in the sense that OpenAI describes it as a terminal agent that can read, change, and run code on your machine in the selected directory; the current setup docs are at Codex CLI.

The shared lesson is not provider-specific: isolate the filesystem state before comparing outputs.

A clean separation model

Use this model when running Claude Code and Codex side by side:

This keeps the workflow understandable. Each agent can still use the local repository and tools, but their work does not silently merge until a human chooses what to do.

When to use two agents

Side-by-side agents are useful when the second run creates independent value.

Good cases:

• ask one agent to implement and the other to review,
• explore two possible fixes in separate worktrees,
• use one agent for a read-only investigation and another for implementation,
• compare approaches for a narrow refactor,
• or have one agent check test gaps after another opens a branch.

Bad cases:

• tiny one-file changes,
• tasks with unclear requirements,
• work that needs product judgment before code,
• changes that touch sensitive shared state,
• or broad refactors where coordination costs exceed the benefit.

The point is not to double every task. The point is to give important tasks another angle without destroying the review path.

What Junction contributes

You can run agents from terminals manually, but the overhead grows as soon as you have more than one active run.

Junction gives you a web control surface around the local daemon. That means you can keep an eye on separate Claude Code and Codex sessions without hunting through terminal tabs. You can inspect live output, watch for approval prompts, stop a run that drifts, and review diffs from the same place.

This is especially useful when the sessions run on more than one machine. Each daemon keeps its own provider authentication, GitHub login, and local configuration. If you need unlimited daemons and open chats, Junction Core is the plan designed for that; the Free plan is better for trying one saved daemon connection and two active or open chats. You can compare those limits on pricing.

A concrete side-by-side workflow

Imagine you need to fix a flaky setup page test.

Do not start with "Claude and Codex, both fix this in the same repo." Start with separation.

First session:

Claude Code: inspect the failing setup page test and propose the smallest fix. Do not edit files until you explain the cause.

Second session:

Codex: independently inspect the same failing test and identify whether the issue is test setup, component behavior, or copy drift. Do not edit files yet.

At this stage, both runs can be read-only. You are comparing diagnoses, not racing implementations.

If both point to the same fix, choose one implementation path. If they disagree, ask each to explain the evidence. If the task is still worth parallel implementation, put each agent in a separate branch or worktree before edits begin.

After implementation:

• review changed file lists,
• compare diffs,
• run the relevant tests,
• keep the smaller correct branch,
• discard the branch that added unnecessary scope.

That is a controlled workflow. It uses two agents without letting them trample the same working tree.

Avoid context laundering

Context laundering happens when one agent's unsupported conclusion gets passed to another as if it were fact.

For example:

Claude says the bug is in pricing state. Codex, fix pricing state.

That may bias the second agent toward the wrong area. A better prompt is:

Independently inspect this failing test. Do not assume the previous diagnosis is correct. Report the most likely cause and the evidence.

Use the second agent as an independent check, not a rubber stamp.

How this differs from a dashboard post

A dashboard tells you what is running and what needs attention. A side-by-side workflow tells you how to keep multiple runs from corrupting each other's context.

Both matter. If you want the dashboard rubric, read What an AI Coding Agent Dashboard Should Actually Do. If you want the Git isolation layer behind parallel work, read Git Worktree Isolation for AI Coding Agents.

This post is narrower: do not mix contexts until there is something worth merging.

Tradeoffs worth accepting

Side-by-side runs cost more attention. You have to name tasks clearly, keep branches separate, and review two outputs. If the task is small, one agent is usually enough.

The extra structure is worth it when:

• the change is important,
• the first answer is uncertain,
• the failure is hard to diagnose,
• the review needs a second perspective,
• or the implementation has multiple plausible paths.

The structure is not worth it when the work is obvious. Do not turn every small edit into a multi-agent process.

Start with two separate sessions

The safest way to try this is not a complex orchestration system. Start with two separate local sessions and one bounded task.

Use Claude Code for one read-only diagnosis. Use Codex for another. Compare the evidence. Then let only one agent implement, or isolate both implementations in separate worktrees before editing.

Start with the Junction setup guide and create separate local sessions for Claude Code and Codex. Once the separation model feels natural, use pricing to decide whether you need more daemons or open chats.