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How to split a coding task across Codex and Claude agents with Openbot

Give each agent its own workspace and queue, then delegate parts of a task between them.

Who it is forA software engineer with a multi-part feature who wants Codex and Claude Code to work in parallel and hand results to each other.

The problemOne agent session loses context when a large task spans several areas, and manual copy-paste between sessions is slow.

The outcomeTwo persistent agents each hold a dedicated workspace and conversation, and they exchange messages and files inside one desktop app.

What you need first

  • A macOS 13+, Windows 10+, or x64/arm64 Linux AppImage machine, or an Ubuntu 24.04 host with systemd for the server
  • An account with at least one supported provider (Codex, Claude, Grok, OpenCode, Gemini, Cursor, or Cline), or a local model server such as Ollama or LM Studio
  • Willingness to run agents with danger-full-access and approvalPolicy never after the first-launch consent

Steps

  1. Install the Codex CLI with curl -fsSL https://chatgpt.com/codex/install.sh | sh and the Claude CLI with curl -fsSL https://claude.ai/install.sh | bash, or use the Download option in OpenBot.
  2. Download the OpenBot release for your system from GitHub Releases and open it.
  3. Create one Codex agent and one Claude agent, each with its own workspace.
  4. Queue a task for each agent, then send an agent-to-agent message asking one to review the other's output.
  5. Open a shared channel chat, assign one task owner, and use Stop or Reassign if a delegation goes wrong.

Prompt for your agent

In the nightly-labs/openbot repo, set up a Codex agent and a Claude agent in the desktop app, each with a separate workspace under <project-path>. Expected output: both agents listed with queued tasks and one agent-to-agent message sent. Verify that each agent's queue shows the task before reporting success.

Caveat. Claude subscriptions fall under provider terms, which OpenBot links but does not change, so check those terms before running heavy parallel work.

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