How to Run Multiple AI Coding Agents at Once (Step-by-Step)

TL;DR: Pipper Code is a free macOS and Windows desktop app that lets you run multiple AI coding agents at once — Claude Code, Codex, Cursor, OpenCode, Copilot, and Grok — from one interface. This guide covers installing Pipper, registering agents you already use, giving each task its own git worktree, and monitoring the fleet from one window.

Why Run Multiple AI Coding Agents at Once

Most developers already juggle two or three AI coding agents a day: Claude Code for a careful refactor, Codex for a broad exploratory spike, OpenCode for a workflow experiment, Cursor for a UI change they can click through. Running them one at a time means re-explaining your project to every tool and waiting for one agent before the next task starts. That is the serial bottleneck.

Parallel execution changes the math. A long migration and a quick bug hunt proceed at the same time. A stubborn task gets a second opinion from a different model while the first agent keeps going. Your scarcest resource stops being the tool — it becomes the minutes you spend waiting.

Why run agents in parallel	Serial workflows
Two independent tasks finish at once	Tasks queue behind one agent
Second opinions from different models	One house style, one blind spot
A dedicated agent per repo or branch	Context re-typed into every session
Review in one window	Output scattered across terminals
Token spend stays in control	Usage surprises at month end

For the concept behind this pattern, What Is an AI Agent Interface? explains what a shared workspace for agents is.

Requirements for Running Multiple Coding Agents on the Same Machine

Running multiple coding agents on the same machine does not need a server farm. Each ACP-capable agent is a local process, and a modern laptop handles several comfortably. You need three things:

A conductor app that speaks to several agents at once. Pipper Code is built on the Agent Client Protocol (ACP), an open, agent-neutral standard, so Claude Code, Codex, and OpenCode use one protocol instead of bespoke integrations.
Your own agent CLIs, installed and authenticated. You bring your own agents exactly as you run them today. Pipper never replaces your tools; it only routes them.
A review habit. Parallel work feels safe only when every diff still faces a human gate.

Because your agents already speak ACP, this is a configuration session, not a migration.

Step-by-Step: How to Run Multiple AI Agents at Once

This walk-through assumes the agent CLIs you want (Claude Code and Codex, say) are already installed and authenticated.

Step 1: Download and Install Pipper Code

Grab the installer for macOS Apple Silicon or Windows x64 from the download page. First launch creates an empty home surface where you add agents and tasks. Installing is a free download; there is nothing to subscribe to for the interface itself.

Step 2: Register the Agents You Already Use

In the launcher, register each ACP-capable agent you already have. This is bring-your-own, so there is nothing new to buy — you are only pointing the interface at executables you already maintain.

# Launcher manifest: point Pipper at the agents you already use
$ pipper agent register claude-code --exec $(which claude)
$ pipper agent register codex --exec $(which codex)
$ pipper agent register opencode --exec $(which opencode)

Any ACP agent can be registered, not just these three.

Step 3: Give Each Task Its Own Worktree

The convention that keeps parallel agents from colliding is per-task isolation. Create one git worktree per chunk of work, so each agent operates on a separate file tree and branch, and results later share one history.

# Git worktree-style isolation: each agent owns its own working tree
$ git worktree add ../checkout-pricing release/checkout/pricing
$ git worktree add ../web-audit release/checkout/audit

Step 4: Launch Claude Code and Codex Together

With the trees ready, spawn a Claude Code agent and a Codex agent into the same scene, each pointed at its own worktree:

# Two agents, one scene, parallel work on one project
$ pipper agent launch claude-code --workspace checkout-pricing --prompt "Refactor price formatting in src/billing/pricing.ts"
$ pipper agent launch codex --workspace web-audit --prompt "Scan src/ for unused exports and list them"

And that is the trick: to run multiple AI agents at once, pair each agent with its own workspace and a specific prompt. The Claude Code run refactors pricing while the Codex run audits the codebase; both stream into the same window, so there is no file collision.

Step 5: Monitor Every Agent in One Window

The monitor pane shows each thread live: tool calls, file edits, errors, commits. You stop flipping between five terminals. When a task gets deep, spawn a subagent from either thread — it inherits the parent’s context as its own line. For the shell-side view, see our multi-agent terminal guide.

Step 6: Review, Approve, or Reject Before Anything Lands

Nothing an agent writes lands by itself. Every change arrives as a diff, and you decide to merge, give feedback, or reject. The outcome is not “whatever the model did”; it is whatever you chose to merge.

Run Claude Code and Codex Together on One Project

The pairing from the introduction — Claude Code and Codex on the same project — is exactly the recipe above. Claude Code owns the pricing worktree, Codex owns the audit worktree, and both share one scene. Nothing about the pairing is special-cased; it falls out of ACP.

Give them complementary tasks: Claude Code takes the surgical change (a rename, a delete, a review of one function), Codex takes the exploratory scan. In a small web-app test, the refactor finished while the audit was halfway through, and the merged diff stayed small.

Avoiding the Two Classic Parallel-Agent Failures

Parallelism is safe if you respect three practices:

Isolate worktrees. Never point two agents at the same tree and branch, or your commits will fight. One task per worktree.
Integrate frequently. The longer an agent runs on a stale base, the harder the merge. Rebase the worktree when the main branch moves.
Gate on review, not trust. An agent finishing a message proves nothing by itself.

On spending: run a few tasks for a session before scaling up. Each agent carries its own usage cost, exactly as it would outside Pipper. Pipper the interface stays free; only the models you choose spend tokens. For more on the pattern, see the parallel agents deep dive — and for keeping your place while many threads run, How to Use Multiple AI Coding Agents Without Losing Your Place is the companion read.

Key Takeaways

Run multiple AI coding agents at once by giving each one its own worktree, its own prompt, and one shared review surface.
Pipper Code orchestrates Claude Code, Codex, OpenCode, Cursor, Copilot, and Grok through the neutral Agent Client Protocol (ACP) — you bring the agents.
Parallel setups pay off when tasks are independent: migration plus bug hunt, refactor plus audit, implementation plus review.
You monitor everything in one window and approve every diff before it lands, so speed never outpaces control.
Running multiple coding agents on one machine is safe with worktree mechanics.

FAQ

How Do I Run Multiple AI Agents at Once?

Install Pipper Code, register the ACP-capable agents you already use, create a git worktree per task, and launch one agent per worktree. Each agent streams to the same window, and every change arrives as a diff you approve.

Can I Run Multiple Coding Agents on the Same Machine?

Yes. Multiple coding agents on the same machine work because each ACP agent is a separate process and each worktree is a separate file tree. The machine only needs room for a few CLIs — far less than an IDE spinning up a second copy of your repo.

Can I Run Claude Code and Codex Together in One Project?

Yes. Create the pricing and audit worktrees, launch one Claude Code and one Codex agent into the scene, and review the combined result from a single window. They never collide because they write to different trees. Save the two launches as a template to reuse the setup.

Will Parallel Agents Cost Me More Tokens?

Parallelism trades wall-clock time — the total work is bounded by what exists in the repo, but it finishes earlier. Keep spend under control by giving each agent one specific prompt, and by merging only after a human reviews.

What If an Agent Needs a Second Opinion Mid-Task?

That is a one-liner: spawn a subagent from the running thread and give it the question. It inherits the parent’s context and works from the same window while the parent keeps going. You review both threads before anything merges.

Conclusion

The default is serial work: one agent, one terminal, then the next. The parallel way gives several agents to the same codebase at once — one refactors while another audits. The trick is not to trust them blindly; it is to give each agent a worktree, a specific task, and a human reviewer, and to watch them from one place. That is what Pipper Code was built for, and it is free.

Download Pipper Code free at /download — install it, register the agents you already own, and launch your first parallel pair today.