codex-mcp-swarm 1.1.0

Parallel Codex MCP server with live task monitoring, batch wait, and full flag parity

codex-mcp-swarm

An MCP server that wraps OpenAI's Codex CLI with true parallel execution and live task monitoring. Zero dependencies -- single Python file, stdlib only.

Why?

The official codex mcp-server processes requests sequentially. If your MCP client (Claude Code, etc.) needs to run 5 Codex tasks, they queue up one after another. This server spawns each task as an independent subprocess, so they run in parallel.

Unique features no other Codex MCP wrapper has:

• Batch wait -- launch N tasks, call codex_wait once, get all results when they finish
• Live status -- see what each Codex task is doing right now (last tool call, current reasoning, progress)
• Full flag parity -- same parameters as the official Codex MCP tool (sandbox, approval-policy, cwd, model, config, etc.)
• Drop-in config -- accepts the same -c key=value server args as codex mcp-server

Tools

Tool	Description
codex	Synchronous execution (drop-in replacement for official)
codex_async	Fire-and-forget -- returns a task_id immediately
codex_reply	Continue a previous session via codex exec resume
codex_status	Live view: tools called, last command, current thinking
codex_wait	Block until multiple tasks complete, return all results

Installation

Claude Code

claude mcp add codex-swarm -- uvx codex-mcp-swarm \
  -c model=gpt-5.4 \
  -c approval_policy=never \
  -c sandbox_mode=danger-full-access \
  --skip-git-repo-check

That's it. No clone, no setup. uvx downloads and runs it directly from PyPI.

Note: Requires uv (curl -LsSf https://astral.sh/uv/install.sh | sh). Alternatively, use pipx run codex-mcp-swarm instead of uvx codex-mcp-swarm.

Manual (~/.claude.json)

{
  "mcpServers": {
    "codex-swarm": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "codex-mcp-swarm",
        "-c", "model=gpt-5.4",
        "-c", "approval_policy=never",
        "-c", "sandbox_mode=danger-full-access",
        "--skip-git-repo-check"
      ]
    }
  }
}

The -c flags are identical to codex mcp-server -- copy-paste your existing config.

Usage

Parallel execution

1. Call codex_async with prompt A  -->  task_id: "abc123"
2. Call codex_async with prompt B  -->  task_id: "def456"
3. Call codex_async with prompt C  -->  task_id: "ghi789"
4. Call codex_wait(task_ids=["abc123", "def456", "ghi789"])
   --> blocks until all finish, returns all results

Live monitoring

Call codex_status(task_ids=["abc123"])
-->
=== Task abc123 (45s elapsed) ===
Phase: running
Tools called: 23
Last tool: exec_command(grep -rn "handleError" src/)
Output: Analyzing error handling patterns across the codebase...

Session continuity

1. Call codex(prompt="Review this file")  -->  result + session persisted
2. Call codex_reply(threadId="<session-uuid>", prompt="Now fix the bug you found")

Server flags

Flag	Description
-c key=value	Config default (repeatable). Same format as codex mcp-server.
--skip-git-repo-check	Allow running outside git repos.
--ephemeral	Don't persist session files. Disables codex_reply.

Per-call parameters

All parameters from the official Codex MCP tool are supported:

• prompt (required)
• model -- override server default
• sandbox -- read-only, workspace-write, danger-full-access
• approval-policy -- untrusted, on-failure, on-request, never
• cwd -- working directory
• profile -- config profile from config.toml
• config -- object of key=value overrides
• base-instructions, developer-instructions, compact-prompt

Environment variables

Variable	Default	Description
CODEX_SWARM_LOG	/tmp/codex_mcp_swarm.log	Log file path
CODEX_SWARM_LOG_LEVEL	WARNING	Log level (DEBUG, INFO, WARNING, ERROR)
CODEX_SWARM_TASK_DIR	/tmp/codex_swarm_tasks	Task output storage directory

Requirements

• Python 3.8+
• Codex CLI installed and authenticated
• No pip dependencies (stdlib only)

Credits

Originally inspired by jeanchristophe13v/codex-mcp-async. Rewritten with full flag parity, JSONL status parsing, batch wait, and session reply support.

License

MIT