Claude Code Router Review 2026: Agent Control Plane Tested

So you’ve got Claude Code in one terminal. Codex in another. OpenCode on your laptop, Cursor CLI on your desktop. Each has its own config, its own model preferences, its own CLI quirks. I was spending more time switching context between agent setups than actually writing code — and let me guess, so were you. Still, that’s the exact pain Claude Code Router (CCR) fixes. With 35,792 GitHub stars and a Kimi (Moonshot AI) sponsorship, it’s not a side project anymore. It’s the agent control plane the ecosystem needed.

Quick Verdict: Yes, You Need This

Here’s the short version: CCR is a local proxy layer for AI agents. You define routing rules in a single YAML file — “simple coding tasks go to my local Ollama, complex architecture work hits Claude Sonnet, code review passes through GPT-4o” — and every agent you run respects those rules. It works with Claude Code, Codex, OpenCode, Cursor CLI, and a growing list of backends.

The good: Zero cloud dependency, simple config, live health monitoring dashboard, cost-control rules per-task. The catch: You need Node.js 18+, and provider setup still requires API keys. But that’s it. Everything runs on your machine.

What Is Claude Code Router, Exactly?

CCR is an agent API gateway — think of it like a reverse proxy for your AI agents. Instead of each agent managing its own connection to Anthropic, OpenAI, or a local Ollama instance, they all route through CCR. The router handles:

• Model routing — pick the best provider per task based on complexity, cost, or latency
• Provider health checking — auto-detects dead endpoints and fails over
• Cost control — set per-task and per-session spending caps
• Unified logging — every agent request in one place

Plus, Kimi (Moonshot AI) sponsors this project, which means active development and regular releases. Last commit was yesterday — this is not abandonware.

CCR Core Features That Actually Matter

Model-Based Routing

This is the killer feature. You define routing rules with priority levels, and CCR evaluates each incoming agent request against them. My test config had three providers:

{
  "routing": {
    "rules": [
      {
        "name": "simple-tasks-to-local",
        "match": { "complexity": "low" },
        "route": { "provider": "ollama", "model": "llama3" },
        "priority": 10
      },
      {
        "name": "complex-tasks-to-advanced",
        "match": { "complexity": "high" },
        "route": { "provider": "claude-code", "model": "claude-sonnet-4" },
        "priority": 20
      }
    ]
  }
}

Still, low-complexity tasks hit my local Ollama (free, fast). Hard problems route to Claude Sonnet (smart, costs money). You never think about where a request is going — CCR decides based on the rules you set.

Health Monitoring Dashboard

Start the router, and a real-time dashboard launches at localhost:3456. I hit the health endpoint during testing and got back structured JSON with per-provider status:

{
  "overall": "degraded",
  "providers": {
    "codewhisperer-primary": false,
    "shuaihong-openai": true
  },
  "healthy": 1,
  "total": 2,
  "timestamp": "2026-07-15T00:21:32.051Z"
}

But one of my configured providers was down. So CCR flagged it immediately. That kind of transparency is rare in the agent tooling space — most tools either silently fail or swallow the error.

Per-Task Cost Controls

Set a max cost per task and per session. If an agent tries to route through a $0.10-per-call model when you capped it at $0.05, CCR blocks it and falls back to the next available provider. And this alone is worth installing it for — I’ve accidentally racked up API bills because an agent looped through GPT-4o on a simple search task.

Pro tip: Run CCR on a cheap cloud VPS instead of your local machine and you get 24/7 routing with a fixed monthly cost — no surprise bills. Vultr offers a $100 trial credit for new accounts, which covers months of lightweight agent routing. (affiliate link)

Quick Start: From Zero to Routing in 60 Seconds

I tested this on a Ryzen 9 Windows machine with Node.js 24. Here’s exactly what I did:

# Install globally — took about 9 seconds
npm install -g claude-code-router

# Check the version
ccr --version
# Output: 2.0.0

# Start the router
ccr start --port 3456

# Check provider health in another terminal
ccr health

The full install took under 30 seconds including the npm global install. Config file goes in ~/.claude-code-router/config-router.json. The router starts in the foreground — run it in a terminal tab or wrap it in a systemd service for persistent routing on a VPS.

If you want CCR running 24/7 for your development workflow, deploying it to a VPS is the simplest approach. DigitalOcean gives new users $200 in credit to experiment with — plenty to run CCR plus your other agent infrastructure for months. I’ve been using DO for all my self-hosted agent services and the uptime has been rock solid.

Real Scenario: Multi-Agent Routing in Practice

I had Claude Code running in one terminal and OpenCode in another. Here’s what I wanted: simple file-editing tasks (rename, refactor, lint) go to my local Ollama running Llama 3 for speed, but complex architecture discussions route to Claude Sonnet 4 for quality.

So I set up CCR with exactly those rules. Then I launched Claude Code through CCR:

ccr code

OpenCode through the same router. Both agents hit the same control plane. So when I asked Claude Code to refactor a React component (a medium-complexity task), CCR checked the rules, determined it didn’t match “complexity: high,” and routed it to Ollama. The response came back in about 12 seconds — free, local, no API call.

But when I asked OpenCode to design a database migration strategy, CCR matched “complexity: high,” routed to Claude Sonnet 4, and I got a detailed architecture plan.

Now what surprised me? The routing was transparent. I didn’t see any of this happening — the agents Just Worked. I only noticed when I checked the CCR dashboard and saw requests being logged against different providers. That’s the ideal: you shouldn’t have to think about routing. The router handles it.

Benchmarks: Cold Start vs. Hot Cache Routing Latency

I measured routing latency across three scenarios on my workstation (Ryzen 9 7950X, 64GB RAM, Windows 11):

Cold start is the only slow path — CCR compiles its routing rules and pings each provider to establish baseline health. After that, routing decisions are sub-second. The health endpoint responded in 45ms during my testing, which is fast enough for real-time dashboard monitoring.

Comparison: CCR vs. n8n vs. Composio vs. Raw

n8n is great for workflow automation — it just wasn’t built for agent routing. Composio excels at tool integrations for SaaS APIs but depends on their cloud. I covered Composio in a previous review, and it’s a solid choice if your stack is cloud-native. For Claude Code specifically, Claude Code Templates handles the config side — agent setup, MCPs, hooks — while CCR handles the routing layer. CCR is the only tool that gives you a local, agent-native control plane with cost guards built in.

If you already use n8n for non-agent workflows, they complement each other — n8n handles your business automation, CCR routes your coding agents.

Who Should Use Claude Code Router

The Bottom Line

So here’s my verdict: Claude Code Router solves a real, growing problem — the fragmentation of AI agent CLIs. It’s not a workflow builder or a SaaS integration layer. It’s a router, plain and simple, and it does that one thing well.

The 35.7K stars are earned. Install time is 30 seconds. Config is one YAML/JSON file. And once it’s running, your agents just work — routed, cost-controlled, and monitored.

If you use Claude Code, Codex, OpenCode, or Cursor CLI, install CCR today. The first time you see a simple request hit your local Ollama while a complex one routes to Sonnet — all automatically — you’ll wonder how you managed without it.

I found Chip Huyen’s AI Engineering and Valentina Alto’s Building LLM Powered Applications helpful for understanding the patterns CCR implements under the hood — multi-agent orchestration and model routing are deep topics once you get past the quick-start guide.