Claude Code Router

What it is

Claude Code Router (CCR) is a proxy and routing layer for the Claude Code CLI. It intercepts API requests from Claude Code and redirects them to various LLM providers (OpenRouter, DeepSeek, Gemini, Ollama, etc.) based on user-defined rules. As of early 2027, it is the standard for benchmarking frontier models such as claude-5-1 against GPT-5.5, Gemini 4.0 Pro/Flash, and DeepSeek-V4, providing a unified interface for model-agnostic agentic workflows and FastMCP 3.1 tooling integrations.

What problem it solves

• Cost Optimization: Redirects expensive Claude 5.1 Sonnet/Opus requests to cheaper alternatives like DeepSeek-V4 or local models for routine background tasks.
• Regional Access: Enables users in regions where Anthropic API endpoints are restricted to use Claude Code by proxying through other supported providers.
• Model Flexibility: Allows mixing and matching models for different task domains (e.g., formal mathematical reasoning vs. high-speed boilerplate generation) within the same active Claude Code session.
• Compatibility Smoothing: Uses a "Transformer" system to bridge structural differences between provider APIs (e.g., enforcing tool parameter validation or normalizing reasoning tags).
• Latency & Reliability Management: Implements smart routing based on real-time provider health checks and response latency in dynamic inference markets.

Where it fits in the stack

Router / Gateway. It sits between the agent (Claude Code) and the upstream inference provider, acting as a programmable middleware. It is frequently deployed alongside LiteLLM for enterprise-grade load balancing and token rate-limit management.

Typical use cases

• DeepSeek Integration: Directing complex code generation and reasoning tasks to DeepSeek-V4 at a fraction of the token cost of Claude 5.1.
• Local Dev Loop: Routing lightweight background queries to a local Ollama instance (e.g., qwen3.8-coder) to eliminate external API reliance and maintain privacy.
• Enterprise Proxying: Centralizing API key management, observability, and cost auditing for engineering teams using Claude Code via OpenRouter.
• Automated Benchmarking: Executing multi-file refactoring tasks across Claude 5.1, GPT-5.5, and Gemini 4.0 Pro to evaluate model regressions and accuracy.

Strengths

• Dynamic Switching: Instantly switch underlying active models using the /model command within an active Claude Code interaction.
• Transformer System: Built-in AST and payload manipulation to guarantee tool-use compatibility for non-Anthropic models.
• Ease of Use: Features a lightweight web management dashboard (ccr ui) alongside an interactive CLI selector (ccr model).
• CI/CD Native: Full support for headless operation via NON_INTERACTIVE_MODE in automated GitHub Actions pipelines.
• MCP 3.1 Discovery: Native compatibility with the Model Context Protocol (MCP) FastMCP 3.1 Task Protocol.

Limitations

• Latency Overhead: Routing requests through a local or intermediate proxy introduces slight network latency (typically <50ms).
• Operational Complexity: Requires running and maintaining a local background proxy daemon.
• Instruction Adherence Gap: While API transformers significantly improve prompt compatibility, non-Claude models may occasionally drift on intricate multi-step agentic directives compared to native claude-5-1 execution.

When to use it

• When optimizing API token expenditure by routing secondary agent tasks to high-throughput, low-cost models.
• When requiring proxy access to Anthropic models from restricted geographical regions.
• When configuring multi-provider fallback chains to guarantee uptime during upstream provider outages.
• When running automated evaluations across multiple frontier models under identical Claude Code project prompts.

When not to use it

• When operating under a direct Claude Code Max tier where multi-provider routing is unneeded.
• When strict zero-dependency policy prohibits running background proxy daemons.
• In zero-trust air-gapped environments that restrict local loopback network listeners.

Getting started

Ensure Claude Code is installed:

npm install -g @anthropic-ai/claude-code

Install Claude Code Router:

npm install -g @musistudio/claude-code-router

Start the service:

ccr start

Configure your target model route:

ccr model openrouter/anthropic/claude-5-1

CLI examples

# Set the active model for the router
ccr model deepseek/deepseek-chat

# Launch Claude Code through the router
ccr code

# Open the web-based configuration UI
ccr ui

# Check provider health, latency, and throughput metrics
ccr health

API examples

Advanced Routing Patterns (YAML)

CCR supports advanced declarative routing rules defined in rules.yaml that trigger on prompt regex patterns or tool capabilities:

rules:
  - name: "heavy-coding"
    if: "query.matches(/refactor|implement/)"
    then: "deepseek/deepseek-chat"
    transformer: "deepseek_v4_coding"
  - name: "mcp-routing"
    if: "tools.include('brave_search')"
    then: "google/gemini-4.0-flash"

Python Routing Rules and Fallback Validation with Pydantic v2

This Python snippet validates CCR configuration files, model transformers, and failover policies using strict Pydantic v2 models.

import json
from typing import List, Optional, Dict
from pydantic import BaseModel, Field, ValidationError, ConfigDict

class FallbackPolicy(BaseModel):
    enabled: bool = Field(default=True, description="Enable automated failover to alternate model targets")
    strategy: str = Field(default="ordered", description="Failover strategy algorithm")
    targets: List[str] = Field(description="Priority list of model target routes")
    retry_on: List[int] = Field(
        default_factory=lambda: [429, 502, 503],
        validation_alias="retry_on",
        description="HTTP status codes triggering fallback"
    )

class TransformerConfig(BaseModel):
    transformers: List[str] = Field(default_factory=list, description="List of active API transformer middleware plugins")

class RouterRules(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    name: str = Field(description="Unique identifier rule name")
    condition: str = Field(validation_alias="if", description="Matching condition evaluated against query context or tool use")
    target: str = Field(validation_alias="then", description="Primary model route target")
    transformer: Optional[str] = Field(None, description="Optional payload transformer identifier")

class CCRConfig(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    fallback_policy: FallbackPolicy = Field(validation_alias="fallback_policy")
    models: Dict[str, TransformerConfig] = Field(default_factory=dict)
    rules: List[RouterRules] = Field(default_factory=list)

def validate_ccr_config(raw_json: str) -> Optional[CCRConfig]:
    try:
        data = json.loads(raw_json)
        config = CCRConfig.model_validate(data)
        return config
    except json.JSONDecodeError:
        print("Error: Input is not valid JSON.")
    except ValidationError as e:
        print(f"CCR Configuration Validation failed: {e.errors()}")
    return None

if __name__ == "__main__":
    sample_config = """
    {
      "fallback_policy": {
        "enabled": true,
        "strategy": "ordered",
        "targets": ["deepseek/deepseek-chat", "anthropic/claude-5-1"],
        "retry_on": [429, 502, 503]
      },
      "models": {
        "deepseek/deepseek-chat": {
          "transformers": ["tooluse", "inject_reasoning_reminder"]
        }
      },
      "rules": [
        {
          "name": "heavy-coding",
          "if": "query.matches(/refactor/)",
          "then": "deepseek/deepseek-chat"
        }
      ]
    }
    """
    parsed = validate_ccr_config(sample_config)
    if parsed:
        print(f"Validated CCR configuration with {len(parsed.rules)} rules successfully.")

Related tools / concepts

• Claude Code
• Aider
• LiteLLM
• MCP
• OpenRouter
• Ollama
• DeepSeek
• Fallback Patterns

Sources / references

• Official GitHub
• Project Motivation Blog Post
• Transformers & Tool Usage Blog Post

Contribution Metadata

• Last reviewed: 2027-01-07
• Confidence: high