Claude Code Mux

OpenRouter met Claude Code Router. They had a baby.

Now your coding assistant can use GLM 4.6 for one task, Kimi K2 Thinking for another, and Minimax M2 for a third. All in the same session. When your primary provider goes down, it falls back to your backup automatically.

⚡️ Multi-model intelligence with provider resilience

A lightweight, Rust-powered proxy that provides intelligent model routing, provider failover, streaming support, and full Anthropic API compatibility for Claude Code.

Claude Code → Claude Code Mux → Multiple AI Providers
              (Anthropic API)    (OpenAI/Anthropic APIs + Streaming)

Table of Contents

• Key Features
• Installation
• Quick Start
• Screenshots
• Usage Guide
• Routing Logic
• Configuration Examples
• Supported Providers
• Advanced Features
• CLI Usage
• Troubleshooting
• FAQ
• Performance
• Why Choose Claude Code Mux?
• Documentation
• Changelog
• Contributing
• License

Key Features

🎯 Core Features

• ✨ Modern Admin UI - Beautiful web interface with auto-save and URL-based navigation
• 🔐 OAuth 2.0 Support - FREE access for Claude Pro/Max, ChatGPT Plus/Pro, and Google AI Pro/Ultra
• 🧠 Intelligent Routing - Auto-route by task type (websearch, reasoning, background, default)
• 🔄 Provider Failover - Automatic fallback to backup providers with priority-based routing
• 🌊 Streaming Support - Full Server-Sent Events (SSE) streaming for real-time responses
• 🌐 Multi-Provider Support - 18+ providers including OpenAI, Anthropic, Google Gemini/Vertex AI, Groq, ZenMux, etc.
• ⚡️ High Performance - ~5MB RAM, <1ms routing overhead (Rust powered)
• 🎯 Unified API - Full Anthropic Messages API compatibility

🚀 Advanced Features

• 🔀 Auto-mapping - Regex-based model name transformation before routing (e.g., transform all claude-* to default model)
• 🎯 Background Detection - Configurable regex patterns for background task detection
• 🤖 Multi-Agent Support - Dynamic model switching via CCM-SUBAGENT-MODEL tags
• 📊 Live Testing - Built-in test interface to verify routing and responses
• ⚙️ Centralized Settings - Dedicated Settings tab for regex pattern management

Screenshots

Supported Providers

18+ AI providers with automatic format translation, streaming, and failover:

• Anthropic-compatible: Anthropic (API Key/OAuth), ZenMux, z.ai, Minimax, Kimi
• OpenAI-compatible: OpenAI, OpenRouter, Groq, Together, Fireworks, Deepinfra, Cerebras, Moonshot, Nebius, NovitaAI, Baseten
• Google AI: Gemini (OAuth/API Key), Vertex AI (GCP ADC)

Installation

Option 1: Download Pre-built Binaries (Recommended)

Download the latest release for your platform from GitHub Releases.

Linux (x86_64)

# Download and extract (glibc)
curl -L https://github.com/9j/claude-code-mux/releases/latest/download/ccm-linux-x86_64.tar.gz | tar xz

# Or download musl version (static linking, more portable)
curl -L https://github.com/9j/claude-code-mux/releases/latest/download/ccm-linux-x86_64-musl.tar.gz | tar xz

# Move to PATH
sudo mv ccm /usr/local/bin/

macOS (Intel)

# Download and extract
curl -L https://github.com/9j/claude-code-mux/releases/latest/download/ccm-macos-x86_64.tar.gz | tar xz

# Move to PATH
sudo mv ccm /usr/local/bin/

macOS (Apple Silicon)

# Download and extract
curl -L https://github.com/9j/claude-code-mux/releases/latest/download/ccm-macos-aarch64.tar.gz | tar xz

# Move to PATH
sudo mv ccm /usr/local/bin/

Windows

1. Download ccm-windows-x86_64.zip
2. Extract the ZIP file
3. Add the directory containing ccm.exe to your PATH

Verify Installation

ccm --version

Option 2: Install via Cargo

If you have Rust installed, you can install directly from crates.io:

cargo install claude-code-mux

This will download, compile, and install the ccm binary to your cargo bin directory (usually ~/.cargo/bin/).

Verify Installation

ccm --version

Option 3: Build from Source

Prerequisites

• Rust 1.70+ (install from rustup.rs)

Build Steps

# Clone the repository
git clone https://github.com/9j/claude-code-mux
cd claude-code-mux

# Build the release binary
cargo build --release

# The binary will be available at target/release/ccm

Install to PATH (Optional)

# Copy to /usr/local/bin for global access
sudo cp target/release/ccm /usr/local/bin/

# Or add to your shell profile (e.g., ~/.zshrc or ~/.bashrc)
export PATH="$PATH:/path/to/claude-code-mux/target/release"

Run Directly Without Installing (Optional)

# From the project directory
cargo run --release -- start

Quick Start

1. Start Claude Code Mux

ccm start

The server will start on http://127.0.0.1:13456 with a web-based admin UI.

💡 First-time users: A default configuration file will be automatically created at:

• Unix/Linux/macOS: ~/.claude-code-mux/config.toml
• Windows: %USERPROFILE%\.claude-code-mux\config.toml

2. Open Admin UI

Navigate to:

http://127.0.0.1:13456

You'll see a modern admin interface with these tabs:

• Overview - System status and configuration summary
• Providers - Manage API providers
• Models - Configure model mappings and fallbacks
• Router - Set up routing rules (auto-saves on change!)
• Test - Test your configuration with live requests

3. Configure Claude Code

Set Claude Code to use the proxy:

export ANTHROPIC_BASE_URL="http://127.0.0.1:13456"
export ANTHROPIC_API_KEY="any-string"
claude

That's it! Your setup is complete.

Usage Guide

Step 1: Add Providers

Navigate to Providers tab → Click "Add Provider"

Example: Add Anthropic with OAuth (🆓 FREE for Claude Pro/Max)

1. Select provider type: Anthropic
2. Enter provider name: claude-max
3. Select authentication: OAuth (Claude Pro/Max)
4. Click "🔐 Start OAuth Login"
5. Authorize in the popup window
6. Copy and paste the authorization code
7. Click "Complete Authentication"
8. Click "Add Provider"

💡 Pro Tip: Claude Pro/Max subscribers get unlimited API access for FREE via OAuth!

Example: Add ZenMux Provider

1. Select provider type: ZenMux
2. Enter provider name: zenmux
3. Select authentication: API Key
4. Enter API key: your-zenmux-api-key
5. Click "Add Provider"

Example: Add OpenAI Provider

1. Select provider type: OpenAI
2. Enter provider name: openai
3. Enter API key: sk-...
4. Click "Add Provider"

Example: Add z.ai Provider

1. Select provider type: z.ai
2. Enter provider name: zai
3. Enter API key: your-zai-api-key
4. Click "Add Provider"

Example: Add Google Gemini with OAuth (🆓 FREE for Google AI Pro/Ultra)

1. Select provider type: Google Gemini
2. Enter provider name: gemini-pro
3. Select authentication: OAuth (Google AI Pro/Ultra)
4. Click "🔐 Start OAuth Login"
5. Authorize in the popup window
6. Copy and paste the authorization code
7. Click "Complete Authentication"
8. Click "Add Provider"

💡 Pro Tip: Google AI Pro/Ultra subscribers get unlimited API access for FREE via OAuth!

Example: Add Vertex AI Provider (GCP)

1. Select provider type: ☁️ Vertex AI
2. Enter provider name: vertex-ai
3. Enter GCP Project ID: your-gcp-project-id
4. Enter Location: us-central1 (or your preferred region)
5. Click "Add Provider"

Note: Vertex AI uses Application Default Credentials (ADC). Make sure you've run gcloud auth application-default login first.

Supported Providers:

• Anthropic-compatible: Anthropic (API Key or OAuth), ZenMux, z.ai, Minimax, Kimi
• OpenAI-compatible: OpenAI, OpenRouter, Groq, Together, Fireworks, Deepinfra, Cerebras, Nebius, NovitaAI, Baseten
• Google AI: Gemini (OAuth/API Key), Vertex AI (GCP ADC)

Step 2: Add Model Mappings

Navigate to Models tab → Click "Add Model"

Example: Minimax M2 (Ultra-fast, Low Cost)

1. Model Name: minimax-m2
2. Add mapping:
   • Provider: minimax
   • Actual Model: MiniMax M2
   • Priority: 1
3. Click "Add Model"

Why Minimax M2? - $0.30/$1.20 per M tokens (8% of Claude Sonnet 4.5 cost), 100 TPS throughput, MoE architecture

Example: GLM-4.6 with Fallback (Cost Optimized)

1. Model Name: glm-4.6
2. Add mappings:
   • Mapping 1 (Primary):
     • Provider: zai
     • Actual Model: glm-4.6
     • Priority: 1
   • Mapping 2 (Fallback):
     • Provider: openrouter
     • Actual Model: z-ai/glm-4.6
     • Priority: 2
3. Click "+ Fallback Provider Add" to add more fallbacks
4. Click "Add Model"

How Fallback Works: If zai provider fails, automatically falls back to openrouter

GLM-4.6 Pricing: $0.60/$2.20 per M tokens (90% cheaper than Claude Sonnet 4.5), 200K context window

Step 3: Configure Router

Navigate to Router tab

Configure routing rules (auto-saves on change!):

• Default Model: minimax-m2 (general tasks - ultra-fast, 8% of Claude cost)
• Think Model: kimi-k2 (plan mode with reasoning - 256K context)
• Background Model: glm-4.5-air (simple background tasks)
• WebSearch Model: glm-4.6 (web search tasks)
• Auto-map Regex Pattern: ^claude- (transform Claude models before routing)
• Background Task Regex Pattern: (?i)claude.*haiku (detect background tasks)

Step 3.5: Configure Regex Patterns (Optional)

Navigate to Settings tab for centralized regex management:

• Auto-mapping Pattern: Regex to match models for transformation (e.g., ^claude-)

  • Matched models are transformed to the default model
  • Then routing logic (WebSearch/Think/Background) is applied

• Background Task Pattern: Regex to detect background tasks (e.g., (?i)claude.*haiku)

  • Matches against the ORIGINAL model name (before auto-mapping)
  • Matched models use the background model

Step 4: Save Configuration

Click "💾 Save to Server" to save configuration to disk, or "🔄 Save & Restart" to save and restart the server.

Note: Router configuration auto-saves to localStorage on change, but you need to click "Save to Server" to persist to disk.

Step 5: Test Your Setup

Navigate to Test tab:

1. Select a model (e.g., minimax-m2 or glm-4.6)
2. Enter a message: Hello, test message
3. Click "Send Message"
4. View the response and check routing logs

Routing Logic

Flow: Auto-map (transform) → WebSearch > Subagent > Think > Background > Default

0. Auto-mapping (Model Name Transformation)

• Trigger: Model name matches auto_map_regex pattern
• Example: Request with model="claude-4-5-sonnet" and regex ^claude-
• Action: Transform claude-4-5-sonnet → minimax-m2 (default model)
• Then: Continue to routing logic below
• Configuration: Set in Router or Settings tab

Key Point: Auto-mapping is NOT a routing decision - it transforms the model name BEFORE routing logic is applied.

1. WebSearch (Highest Priority)

• Trigger: Request contains web_search tool in tools array
• Example: Claude Code using web search tool
• Routes to: websearch model (e.g., GLM-4.6)

2. Subagent Model

• Trigger: System prompt contains <CCM-SUBAGENT-MODEL>model-name</CCM-SUBAGENT-MODEL> tag
• Example: AI agent specifying model for sub-task
• Routes to: Specified model (tag auto-removed)

3. Think Mode

• Trigger: Request has thinking field with type: "enabled"
• Example: Claude Code Plan Mode (/plan)
• Routes to: think model (e.g., Kimi K2 Thinking, Claude Opus)

4. Background Tasks

• Trigger: ORIGINAL model name matches background_regex pattern
• Default Pattern: (?i)claude.*haiku (case-insensitive)
• Example: Request with model="claude-4-5-haiku" (checked BEFORE auto-mapping)
• Routes to: background model (e.g., GLM-4.5-air)
• Configuration: Set in Router or Settings tab

Important: Background detection uses the ORIGINAL model name, not the auto-mapped one.

5. Default (Fallback)

• Trigger: No routing conditions matched
• Routes to: Transformed model name (if auto-mapped) or original model name

Routing Examples

Example 1: Claude Haiku with Web Search

Request: model="claude-4-5-haiku", tools=[web_search]
Config: auto_map_regex="^claude-", background_regex="(?i)claude.*haiku", websearch="glm-4.6"

Flow:
1. Auto-map: "claude-4-5-haiku" → "minimax-m2" (transformed)
2. WebSearch check: tools has web_search → Route to "glm-4.6"
Result: glm-4.6 (websearch model)

Example 2: Claude Haiku (No Special Conditions)

Request: model="claude-4-5-haiku"
Config: auto_map_regex="^claude-", background_regex="(?i)claude.*haiku", background="glm-4.5-air"

Flow:
1. Auto-map: "claude-4-5-haiku" → "minimax-m2" (transformed)
2. WebSearch check: No web_search tool
3. Think check: No thinking field
4. Background check on ORIGINAL: "claude-4-5-haiku" matches "(?i)claude.*haiku" → Route to "glm-4.5-air"
Result: glm-4.5-air (background model)

Example 3: Claude Sonnet with Think Mode

Request: model="claude-4-5-sonnet", thinking={type:"enabled"}
Config: auto_map_regex="^claude-", think="kimi-k2-thinking"

Flow:
1. Auto-map: "claude-3-5-sonnet" → "minimax-m2" (transformed)
2. WebSearch check: No web_search tool
3. Think check: thinking.type="enabled" → Route to "kimi-k2-thinking"
Result: kimi-k2-thinking (think model)

Example 4: Non-Claude Model (No Auto-mapping)

Request: model="glm-4.6"
Config: auto_map_regex="^claude-", default="minimax-m2"

Flow:
1. Auto-map: "glm-4.6" doesn't match "^claude-" → No transformation
2. WebSearch check: No web_search tool
3. Think check: No thinking field
4. Background check: "glm-4.6" doesn't match background regex
5. Default: Use model name as-is
Result: glm-4.6 (original model name, routed through model mappings)

Configuration Examples

Cost Optimized Setup (~$0.35/1M tokens avg)

Providers:

• Minimax (ultra-fast, ultra-cheap)
• z.ai (GLM models)
• Kimi (for thinking tasks)
• OpenRouter (fallback)

Models:

• minimax-m2 → Minimax (MiniMax M2) — $0.30/$1.20 per M tokens
• glm-4.6 → z.ai (glm-4.6) with OpenRouter fallback — $0.60/$2.20 per M tokens
• glm-4.5-air → z.ai (glm-4.5-air) — Lower cost than GLM-4.6
• kimi-k2-thinking → Kimi (kimi-k2-thinking) — Reasoning optimized, 256K context

Routing:

• Default: minimax-m2 (8% of Claude cost, 100 TPS)
• Think: kimi-k2-thinking (thinking model with 256K context)
• Background: glm-4.5-air (simple tasks)
• WebSearch: glm-4.6 (web search + reasoning)
• Auto-map Regex: ^claude- (transform Claude models to minimax-m2)
• Background Regex: (?i)claude.*haiku (detect Haiku models for background)

Cost Comparison (per 1M tokens):

• Minimax M2: $0.30 input / $1.20 output
• GLM-4.6: $0.60 input / $2.20 output
• Claude Sonnet 4.5: $3.00 input / $15.00 output
• Savings: ~90% cost reduction vs Claude

Quality Focused Setup

Providers:

• Anthropic (native Claude)
• OpenRouter (for fallbacks)

Models:

• claude-sonnet-4-5 → Anthropic native
• claude-opus-4-1 → Anthropic native

Routing:

• Default: claude-sonnet-4-5
• Think: claude-opus-4-1
• Background: claude-haiku-4-5
• WebSearch: claude-sonnet-4-5

Multi-Provider with Fallback

Providers:

• Minimax (primary, ultra-fast)
• z.ai (for GLM models)
• OpenRouter (fallback for all)

Models:

• minimax-m2:
  • Priority 1: Minimax → MiniMax-M2
  • Priority 2: OpenRouter → minimax/minimax-m2 (if available)
• glm-4.6:
  • Priority 1: z.ai → glm-4.6
  • Priority 2: OpenRouter → z-ai/glm-4.6

Routing:

• Default: minimax-m2 (falls back to OpenRouter if Minimax fails)
• Think: glm-4.6 (with OpenRouter fallback)
• Background: glm-4.5-air
• WebSearch: glm-4.6

Advanced Features

OAuth Authentication (FREE for Claude Pro/Max, ChatGPT Plus/Pro & Google AI Pro/Ultra)

Claude Pro/Max, ChatGPT Plus/Pro, and Google AI Pro/Ultra subscribers can use their respective APIs completely free via OAuth 2.0 authentication.

Setting Up OAuth

Via Web UI (Recommended):

For Claude Pro/Max:

1. Navigate to Providers tab → "Add Provider"
2. Select provider type: Anthropic
3. Enter provider name (e.g., claude-max)
4. Select authentication: OAuth (Claude Pro/Max)
5. Click "🔐 Start OAuth Login"
6. Complete authorization in popup window
7. Copy and paste the authorization code
8. Click "Complete Authentication"

For ChatGPT Plus/Pro:

1. Navigate to Providers tab → "Add Provider"
2. Select provider type: OpenAI
3. Enter provider name (e.g., chatgpt-codex)
4. Select authentication: OAuth (ChatGPT Plus/Pro)
5. Click "🔐 Start OAuth Login"
6. Complete authorization in popup window (port 1455)
7. Copy and paste the authorization code
8. Click "Complete Authentication"

For Google AI Pro/Ultra:

1. Navigate to Providers tab → "Add Provider"
2. Select provider type: Google Gemini
3. Enter provider name (e.g., gemini-pro)
4. Select authentication: OAuth (Google AI Pro/Ultra)
5. Click "🔐 Start OAuth Login"
6. Complete authorization in popup window
7. Copy and paste the authorization code
8. Click "Complete Authentication"

💡 Supported Models:

• Claude OAuth: All Claude models (Opus, Sonnet, Haiku)
• ChatGPT OAuth: GPT-5.1, GPT-5.1 Codex (with reasoning blocks converted to thinking)
• Gemini OAuth: All Gemini models via Code Assist API (Pro, Flash, Ultra)

Via CLI Tool:

# Run OAuth login tool
cargo run --example oauth_login

# Or if installed
./examples/oauth_login

The tool will:

1. Generate an authorization URL
2. Open your browser for authorization
3. Prompt for the authorization code
4. Exchange code for access/refresh tokens
5. Save tokens to ~/.claude-code-mux/oauth_tokens.json

Managing OAuth Tokens

Navigate to Settings tab → OAuth Tokens section to:

• View token status (Active/Needs Refresh/Expired)
• Refresh tokens manually (auto-refresh happens 5 minutes before expiry)
• Delete tokens when no longer needed

Token Features:

• 🔐 Secure PKCE-based OAuth 2.0 flow
• 🔄 Automatic token refresh (5 min before expiry)
• 💾 Persistent storage with file permissions (0600)
• 🎨 Visual status indicators (green/yellow/red)

Security Notes:

• Tokens are stored with 0600 permissions (owner read/write only)
• Never commit oauth_tokens.json to version control
• Tokens auto-refresh before expiration
• PKCE protects against authorization code interception

OAuth API Endpoints

For advanced integrations:

• POST /api/oauth/authorize - Get authorization URL
• POST /api/oauth/exchange - Exchange code for tokens
• GET /api/oauth/tokens - List all tokens
• POST /api/oauth/tokens/refresh - Refresh a token
• POST /api/oauth/tokens/delete - Delete a token

See docs/OAUTH_TESTING.md for detailed API documentation.

Auto-mapping with Regex

Automatically transform model names before routing logic is applied:

1. Navigate to Router or Settings tab
2. Set Auto-map Regex Pattern: ^claude-
3. All requests for claude-* models will be transformed to your default model
4. Then routing logic (WebSearch/Think/Background) is applied to the transformed request

Use Cases:

• Transform all Claude models to cost-optimized alternative: ^claude-
• Transform both Claude and GPT models: ^(claude-|gpt-)
• Transform specific models only: ^(claude-sonnet|claude-opus)

Example:

Config: auto_map_regex="^claude-", default="minimax-m2", websearch="glm-4.6"
Request: model="claude-sonnet", tools=[web_search]

Flow:
1. Transform: "claude-sonnet" → "minimax-m2"
2. Route: WebSearch detected → "glm-4.6"
Result: glm-4.6 model

Background Task Detection with Regex

Automatically detect and route background tasks using regex patterns:

1. Navigate to Router or Settings tab
2. Set Background Regex Pattern: (?i)claude.*haiku
3. All requests matching this pattern will use your background model

Use Cases:

• Route all Haiku models to cheap background model: (?i)claude.*haiku
• Route specific model tiers: (?i)(haiku|flash|mini)
• Custom patterns for your naming convention

Important: Background detection checks the ORIGINAL model name (before auto-mapping)

Streaming Responses

Full Server-Sent Events (SSE) streaming support:

curl -X POST http://127.0.0.1:13456/v1/messages \
  -H "Content-Type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "minimax-m2",
    "max_tokens": 1000,
    "stream": true,
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Supported Providers:

• ✅ Anthropic-compatible: ZenMux, z.ai, Kimi, Minimax
• ✅ OpenAI-compatible: OpenAI, OpenRouter, Groq, Together, Fireworks, etc.

Provider Failover

Automatic failover with priority-based routing:

[[models]]
name = "glm-4.6"

[[models.mappings]]
actual_model = "glm-4.6"
priority = 1
provider = "zai"

[[models.mappings]]
actual_model = "z-ai/glm-4.6"
priority = 2
provider = "openrouter"

If z.ai fails, automatically falls back to OpenRouter. Works with all providers!

CLI Usage

Start the Server

# Start with default config (~/.claude-code-mux/config.toml)
# Config file is automatically created if it doesn't exist
ccm start

# Start with custom config
ccm start --config path/to/config.toml

# Start on custom port
ccm start --port 8080

Default Config Location:

• Unix/Linux/macOS: ~/.claude-code-mux/config.toml
• Windows: %USERPROFILE%\.claude-code-mux\config.toml (e.g., C:\Users\<username>\.claude-code-mux\config.toml)

Run in Background

Using nohup (Unix/Linux/macOS)

# Start in background
nohup ccm start > ccm.log 2>&1 &

# Check if running
ps aux | grep ccm

# Stop the server
pkill ccm

Using systemd (Linux)

Create /etc/systemd/system/ccm.service:

[Unit]
Description=Claude Code Mux
After=network.target

[Service]
Type=simple
User=your-username
WorkingDirectory=/path/to/claude-code-mux
ExecStart=/path/to/claude-code-mux/target/release/ccm start
Restart=on-failure
RestartSec=5s

[Install]
WantedBy=multi-user.target

Then:

# Reload systemd
sudo systemctl daemon-reload

# Enable on boot
sudo systemctl enable ccm

# Start service
sudo systemctl start ccm

# Check status
sudo systemctl status ccm

# View logs
sudo journalctl -u ccm -f

Using launchd (macOS)

Create ~/Library/LaunchAgents/com.ccm.plist:

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.ccm</string>
    <key>ProgramArguments</key>
    <array>
        <string>/path/to/claude-code-mux/target/release/ccm</string>
        <string>start</string>
    </array>
    <key>RunAtLoad</key>
    <true/>
    <key>KeepAlive</key>
    <true/>
    <key>StandardOutPath</key>
    <string>/tmp/ccm.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/ccm.error.log</string>
</dict>
</plist>

Then:

# Load and start
launchctl load ~/Library/LaunchAgents/com.ccm.plist

# Stop
launchctl unload ~/Library/LaunchAgents/com.ccm.plist

# Check status
launchctl list | grep ccm

Other Commands

# Show version
ccm --version

# Show help
ccm --help

Supported Features

• ✅ Full Anthropic API compatibility (/v1/messages)
• ✅ Token counting endpoint (/v1/messages/count_tokens)
• ✅ Extended thinking (Plan Mode support)
• ✅ Streaming responses (SSE format)
• ✅ System prompts (string and array formats)
• ✅ Tool calling
• ✅ Vision (image inputs)
• ✅ Auto-mapping with regex patterns
• ✅ Provider failover with priority-based routing
• ✅ Auto-strip incompatible parameters for OpenAI models

Troubleshooting

Check if server is running

curl http://127.0.0.1:13456/api/config/json

Enable debug logging

Set environment variable:

RUST_LOG=debug ccm start

Or update your config file (~/.claude-code-mux/config.toml):

[server]
log_level = "debug"

Test routing directly

curl -X POST http://127.0.0.1:13456/v1/messages \
  -H "Content-Type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "minimax-m2",
    "max_tokens": 100,
    "messages": [{"role": "user", "content": "Hello"}]
  }'

View real-time logs

# If running with RUST_LOG
RUST_LOG=info ccm start

# Check system logs
tail -f ~/.claude-code-mux/ccm.log

Performance

• Memory: ~6MB RAM (vs ~156MB for Node.js routers) - 25x more efficient
• Startup: <100ms cold start
• Routing: <1ms overhead per request
• Throughput: Handles 1000+ req/s on modern hardware
• Streaming: Zero-copy SSE streaming with minimal latency

FAQ

export ANTHROPIC_BASE_URL="http://127.0.0.1:13456"
export ANTHROPIC_API_KEY="any-string"
claude

Why Choose Claude Code Mux?

🎯 Two Core Advantages

1. Automatic Failover 🔄

Priority-based provider fallback - if your primary provider fails, automatically route to backup:

[[models]]
name = "glm-4.6"

[[models.mappings]]
actual_model = "glm-4.6"
priority = 1
provider = "zai"

[[models.mappings]]
actual_model = "z-ai/glm-4.6"
priority = 2
provider = "openrouter"

If zai fails → automatically falls back to openrouter. No manual intervention needed.

💡 Why This Matters: Claude Code Router doesn't have failover - if a provider goes down, your workflow stops. With Claude Code Mux, you get uninterrupted coding even during provider outages.

2. Simpler & More Efficient ⚡️

💡 What This Means

Reliability: Automatic failover keeps you coding when providers go down. (CCR lacks this)

Faster Setup: Built-in UI (no ccr ui needed) + simpler TOML config.

Performance: 25x more memory efficient (6MB vs 156MB).

Claude Pro/Max Compatible: OAuth 2.0 authentication supported (CCR requires API key only).

Simplicity: TOML is easier than JSON with complex transformer configurations.

Documentation

• Design Principles - Claude Code Mux design philosophy and UX guidelines
• URL-based State Management - Admin UI URL-based state management pattern
• LocalStorage-based State Management - Admin UI localStorage-based client state management

Changelog

See CHANGELOG.md for detailed release history or view GitHub Releases for downloads.

Contributing

We love contributions! Here's how you can help:

🐛 Report Bugs

Found a bug? Open an issue with:

• Clear description of the problem
• Steps to reproduce
• Expected vs actual behavior
• Your environment (OS, Rust version)

💡 Suggest Features

Have an idea? Start a discussion or open an issue with:

• Use case description
• Proposed solution
• Alternative approaches considered

🔧 Submit Pull Requests

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests: cargo test
5. Run formatting: cargo fmt
6. Run linting: cargo clippy
7. Commit with clear message
8. Push and create a Pull Request

📝 Improve Documentation

• Fix typos or unclear explanations
• Add examples or use cases
• Translate docs to other languages
• Create tutorials or guides

🌟 Support the Project

• Star the repo on GitHub
• Share with others who might benefit
• Write blog posts or create videos
• Join discussions and help other users

See CONTRIBUTING.md for detailed guidelines.

License

MIT License - see LICENSE

Acknowledgments

• claude-code-router - Original TypeScript implementation inspiration
• Anthropic - Claude API
• Rust community for amazing tools and libraries

Made with ⚡️ in Rust