How to run Claude Code and Codex together in free open-source AI god mode

Most developers today bounce between multiple AI coding tools: Claude Code for front-end polish, Codex for data-heavy logic, maybe Cursor or others for day-to-day coding. The problem is they don’t talk to each other — you become the middleman, copying prompts, pasting code, and rebuilding context over and over.

Tracer changes that. It’s a free, open-source desktop workspace that lets multiple coding agents like Claude Code and Codex share context, communicate directly, and collaborate on the same project. Think of it as giving your AI agents a walkie-talkie and a shared office instead of separate chat windows.

What Tracer actually is

Tracer is not another coding model or chatbot. It’s a coordination layer that sits around the AI coding agents you already use. You bring your own subscriptions and API keys (Claude Code, Codex, Cursor, Open Code, etc.), and Tracer turns them into a collaborative workspace.

Each project inside Tracer has its own shared context: files, artifacts, history, and tasks. Any agent you attach to that project can see and work with the same information. That means one agent can plan, another can implement, and a third can review — all inside a single environment.

Key features of the Tracer workspace

Once installed, Tracer opens into a desktop workspace designed specifically for multi-agent coding. It’s much more than a chat box.

Bring your own agents

Tracer connects to the coding agents and models you already use. For example, if you have Claude Code and Codex set up, Tracer can automatically detect and expose them inside the workspace. You can then configure which agent handles which type of task, or let Tracer decide dynamically.

This makes it a powerful companion to tools and models like Claude Code and Codex, similar in spirit to how open-source coding powerhouses designed for agents are changing developer workflows.

Shared project directories

Each Tracer project is tied to a folder on your machine. All generated files, artifacts, and history live there. The recommendation is simple: create a dedicated directory for each project or agent combo (for example, a folder just for Claude Code + Codex work) so you don’t end up with code scattered everywhere.

Because everything is stored in one place, agents can safely modify, extend, and review each other’s work without stepping on each other’s toes.

Multi-view interface and sub-agents

Tracer’s UI lets you open multiple panes side by side. Each pane can be tied to a different agent or sub-agent, so you can literally watch Claude Code and Codex work in parallel. Under the hood, Tracer can spawn sub-agents for specific tasks — for example, one agent to plan and another to implement.

You can run multiple workflows at once, each with its own hierarchy of sub-agents working on separate parts of the same project.

Artifacts, limits, and resource tracking

Artifacts give you a live visualization of what agents are creating: plans, specs, UI mockups, and more. Instead of scrolling through long chat logs, you can quickly see the current phase, what’s been completed, and what’s next.

Tracer also tracks token usage and resource consumption per agent, so you can see how much your Claude Code plan or Codex intelligence agent is costing in real time, along with CPU and memory usage.

Built-in skills and slash commands

Tracer includes a skill library that works across your agents. These are reusable behaviors and prompts for things like planning, debugging, code review, documentation, debating different approaches, or breaking a big project into tickets.

You can trigger many of these via slash commands, giving you a quick way to standardize workflows like "create a spec," "review this code," or "debug this module" regardless of which model is executing them.

How agent-to-agent collaboration works

The most powerful part of Tracer is agent-to-agent (A2A) communication. Instead of you relaying messages between Claude Code and Codex, Tracer lets them talk directly.

Here’s what that looks like in practice:

• One agent creates a plan or spec for a feature.
• Another agent implements that plan in code.
• A third agent reviews the implementation, finds issues, and sends feedback back to the builder agent.
• The builder agent immediately fixes the issues based on that feedback.

All of this happens inside the same shared context. The agents don’t need you to re-explain the project or paste code back and forth. Tracer handles the routing and coordination.

Example: Claude Code + Codex building an AI market dashboard

To see how this works in a real scenario, imagine building a modern, AI-powered market intelligence dashboard. The goal is a web app that tracks markets, your portfolio, financial news, alerts, and offers AI-assisted research.

Assigning roles to each agent

In this setup, you can give each agent a clear role:

• Claude Code focuses on the core application and front-end experience — layout, design, interactive charts, and the overall premium look and feel.
• Codex focuses on market intelligence and research features — building the financial news feed, research tools, and alert logic.

Because they’re both running inside Tracer, they share the same project files and history. When their responsibilities overlap, they can directly exchange messages through the workspace.

From prompt to multi-phase build

You start by giving Tracer a single high-level prompt describing the app you want: a market dashboard with portfolio tracking, watchlists, news, alerts, and AI research. Inside that prompt, you can explicitly state which parts should be handled by Claude Code and which by Codex, or let Tracer decide automatically.

Tracer then spins up a main agent (for example, Claude Opus 5) to orchestrate the work. It breaks the project into phases, such as:

• Phase 0: Foundation – set up the UI structure, mock market data, and basic tokens.
• Phase 1: Intelligence – spawn a Codex-based agent to implement the market research and news components.

As each phase runs, Tracer logs the plan, the actions taken, and the artifacts produced, so you can follow the build step by step.

Live UI and iterative fixes

As the agents work, you can preview the app live. In the market dashboard example, the first iteration already includes:

• A market overview page with key indices and stocks like the S&P and Nvidia.
• A portfolio view showing holdings and performance.
• A watchlist of tracked tickers.
• A news section powered by the Codex-based research agent.
• An alerts panel for market movements.

When something breaks or doesn’t behave as expected, the agents fix it collaboratively. For instance, if Codex notices that a notification function isn’t working correctly, it can send a message back to the Claude Code instance asking for a fix. Claude Code updates the implementation, validates it, and then informs Codex that the issue is resolved — all without you manually mediating.

Inside the finished Market+ app

The end result in this example is a polished "Market+" application built almost entirely through agent collaboration inside Tracer.

The app includes:

• Clean, modern UI – a premium layout with interactive charts and smooth navigation.
• Stock and portfolio views – switch between tickers, see portfolio performance, and manage a watchlist.
• AI-assisted questions – ask about your risk profile, specific stocks, or what happened in the market today.
• Research agent – a dedicated AI research panel that answers deeper questions about companies, sectors, or events.
• News feed – curated stories relevant to the stocks you’re watching, including context like yield changes or shifts in AI chip valuations.
• Alerts – configurable notifications based on price movements or other triggers, which can be wired to live APIs for real-time data.

What’s notable is not just the app itself, but how it was built: multiple agents planning, building, and reviewing in a single orchestrated workflow. This is the same direction many modern coding tools and models are heading, as seen with systems like advanced Claude Code and Codex productivity setups.

Epic mode and spec-driven development

Tracer also includes an "epic mode" for spec-driven development. You can open this in a separate window and use it to define detailed specs before any code is written.

In epic mode, agents help you:

• Turn vague ideas into structured feature specs.
• Break large projects into smaller, trackable tasks.
• Align implementation work with the agreed spec.

Once the spec is ready, Tracer’s agents can execute against it, ensuring that what gets built matches the original plan.

Collaboration, versioning, and team workflows

Beyond solo use, Tracer supports team workflows. You can share a workspace so other people on your team can collaborate on the same project, viewing the same context, artifacts, and history.

From there, you can:

• Run terminal commands directly inside Tracer.
• Manage Git operations from the workspace.
• Commit the generated app once you’re happy with it.
• Have one agent perform a second review pass before merging changes.

This turns Tracer into a hub where human developers and multiple AI agents work together on the same codebase.

Why this approach matters for the future of coding

Tracer doesn’t try to replace Claude Code, Codex, or any other coding agent. Instead, it wraps them in a smarter workspace that lets them collaborate. As coding agents continue to improve, the ability to coordinate multiple specialized agents may become just as important as the models themselves.

By giving your agents shared context, communication channels, and structured workflows, you move from "one AI assistant in a chat box" to a small AI team that can plan, build, and review complex projects with minimal hand-holding — and you get all of this in a free, open-source desktop app.

If you already rely on AI for coding, Tracer is one of the most powerful ways to level up those tools and unlock a true multi-agent "AI god mode" for your development workflow.