If you’re working with AI coding assistants in 2026, you’ve probably hit this scenario: Claude Code nails a refactoring task in minutes. An hour later, GPT-4 solves an architecture problem Claude struggled with. You’re paying for multiple subscriptions, manually switching between terminals, losing context, and tracking costs in a spreadsheet.

Developers on Hacker News have been vocal about this friction: “I typically bounce between Claude Code and Codex for the same project, and generally enjoy using both to check each other,” wrote one user. Another noted, “I dunno, feels like the models have different weak/strong points, sometimes I can sit with Claude Code for an hour with some issue, try it with Codex and have it solved in five minutes, and also the opposite happens.”

The problem isn’t the models—it’s the workflow.

Introducing codex-router

codex-router is a lightweight CLI tool that acts as an intelligent proxy between developers and their AI coding subscriptions. It analyzes your coding tasks, routes them to the optimal model, orchestrates parallel agent sessions, and tracks costs across all providers.

Think of it as the missing orchestration layer that makes multi-model AI workflows actually usable.

How It Works

Instead of manually choosing which AI tool to use for each task, you simply run:

codex-router task "refactor authentication module"

codex-router analyzes the task complexity, checks your configured models and budgets, and automatically routes to the best option. Simple refactors go to fast, cheap models. Complex architectural decisions go to frontier models. You get optimal results without thinking about it.

Parallel Orchestration

Working on a project with multiple independent components? Run multiple AI agents in parallel:

codex-router task "update API endpoints and add tests" --parallel 2

The tool splits subtasks across agents, streams unified output to your terminal, and coordinates the work. It’s like tmux for AI coding agents, but with intelligent task distribution and aggregated results.

Real-Time Cost Tracking

One of the biggest pain points developers mentioned was hitting usage limits unexpectedly. “I have yet to hit usage limits with Codex. I continuously reach it with Claude,” one developer noted.

codex-router tracks token usage and costs in real-time across all providers:

codex-router status --show-costs

You see exactly how much each task cost, which models you’re using most, and where your budget is going. Set spending limits per task:

codex-router task "add feature X" --budget 0.50

If the task exceeds the budget, it stops. No surprise API bills.

Auto-Fallback and Rate Limit Handling

Hit a rate limit mid-task? codex-router automatically falls back to alternative models. Your workflow doesn’t stop because Claude’s rate limit reset timer is ticking down.

Installation and Setup

Install via pip:

pip install codex-router

Configure your API keys once:

codex-router config --set-key claude YOUR_CLAUDE_KEY
codex-router config --set-key openai YOUR_OPENAI_KEY
codex-router config --set-default-model claude

That’s it. One config file, all your AI tools accessible through a single interface.

Why This Matters

Existing solutions require manual model selection (OpenCode) or are complex orchestration frameworks (Conductor). codex-router is different: it’s a simple CLI that makes intelligent decisions for you, optimizes costs automatically, and enables parallel workflows without configuration overhead.

As one developer put it: “The agent orchestration point is interesting - using faster, smaller models for routine tasks while reserving frontier models for complex reasoning.” That’s exactly what codex-router does, automatically.

Try It

codex-router is open source (MIT licensed) and available now:

If you’re tired of context-switching between AI coding tools, give it a try. It’s built for developers who want to leverage multiple models without the workflow friction.

Star the repo if you find it useful, and contributions are welcome.