ccLoad

AI API gateway for Claude Code, Codex, Gemini, and OpenAI.

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Smart routing | Automatic failover | Model-aware cooldown | Multi-URL scheduling | Protocol transforms | Live monitoring | Cost control

ccLoad removes the operational mess of running multiple AI API upstreams. It keeps Claude Code, Codex, Gemini, and OpenAI-compatible clients on one stable gateway, then handles upstream selection, failover, cooldown, protocol conversion, request visibility, and cost limits in the service instead of in every client script.

🤖 Built with Codex and GPT-5.6

During OpenAI Build Week, Codex powered by GPT-5.6 was the primary engineering agent used to:

• Trace request routing, failover, cooldown, protocol conversion, and dashboard flows across the Go backend and embedded web UI.
• Implement and review model-scoped cooldown handling for upstream 5xx, key-level 429, model-unavailable 404, and explicit model-retirement 410 failures without unnecessarily cooling an entire channel.
• Refine the model-status and call-statistics UI, update the English and Chinese documentation, and verify the result with focused Go tests, builds, and browser walkthroughs.
• Prepare the reproducible demo and Devpost submission while keeping architecture, security, and final-review decisions under human control.

GPT-5.6 is also integrated into the product itself: ccLoad exposes GPT-5.6 through OpenAI-compatible and Codex Responses endpoints, includes Sol, Terra, and Luna model presets, calculates their standard, priority, flex, cached-token, and long-context costs, and applies routing and model-scoped cooldown decisions to them like any other configured upstream model.

The repository's AGENTS.md and CLAUDE.md provide persistent engineering constraints so Codex works against the same KISS-first review and testing rules in every session.

🎯 What ccLoad Solves

Common failure modes when you run several AI API channels:

• Manual channel switching: Different keys, validity windows, quotas, and upstream URLs quickly become hard to manage.
• Rate limits and upstream failures: 429, 502, 504, expired keys, and overloaded providers should not stop the client workflow.
• Opaque request status: Without live request visibility, long streaming requests become guesswork.
• HTTP 200 with error content: Some upstreams return a successful HTTP status while the response body is an actual error.
• Cost drift: Shared gateways need per-channel and per-token limits, not spreadsheet accounting after the bill arrives.

ccLoad handles those cases with:

• Smart routing: High-priority channels are selected first; channels at the same priority use smooth weighted round-robin.
• Automatic failover: Failed keys, models, channels, and URLs are skipped according to the classified error scope.
• Model-aware cooldown: Structured model_cooldown responses, upstream HTTP 5xx failures, key-level 429 rate limits, model-unavailable 404 errors, and explicit model-retirement 410 errors all cool only the actual upstream model first; other models on the same channel remain available. The channel is promoted to cooldown only after every configured model or every enabled key is cooling.
• Multi-URL scheduling: A single channel can use multiple upstream URLs, weighted by observed latency and health.
• Per-URL protocol routing: Each URL can declare the upstream wire protocols it accepts. Explicit declarations route directly; an empty declaration tries the client protocol first and caches the working fallback.
• Responses WebSocket bridging: Authenticated Codex clients can keep a downstream WebSocket while each candidate uses native Codex WebSocket or the existing HTTP/SSE transport.
• Live monitoring: Active requests, logs, token usage, TTFB, cost, and upstream details are visible in the web dashboard.
• Soft-error detection: HTTP 200 responses that are actually errors trigger the same failover path as regular upstream failures. Common cases include:
  • JSON responses containing {"error": {...}} structure
  • Responses with type field set to "error"
  • Explicit rate limits in SSE error events (rate_limit_exceeded / too_many_requests) are handled as 429
  • Plain text messages like "当前模型负载过高" / "Current model load too high" (load warnings)

✨ Key Features

• 🚀 High-Performance Architecture - Gin framework, 1000+ concurrent connections, high-performance caching
• 🧮 Local Token Counting - API-compliant local token estimation, <5ms response, 93%+ accuracy, supports large-scale tool scenarios
• 🎯 Smart Error Classification - Distinguishes Key/Model/Channel/Client errors, soft error detection (200 masquerading as error), SSE rate-limit errors as 429, 1308 quota handling
• 🔀 Smart Routing - Priority + smooth weighted round-robin channel selection, pre-filters cooled channels, multi-key load balancing, health-based dynamic sorting (confidence factor prevents small sample over-penalization)
• 🛡️ Failover - Key, model, and channel failures share one exponential-backoff policy; explicit upstream reset deadlines take priority, and model-scoped failures switch channels without cooling the whole channel
• 🔒 Race-Safe - Key selector race condition protection, startup config validation, automatic resource cleanup
• 📊 Real-time Monitoring - Built-in trend analysis, logging, and stats dashboard, Token usage stats with time range selection and per-token classification, runtime status panel with process metrics (CPU, RSS, GC)
• 🎯 Transparent Proxy - Supports Claude Code, Codex, Gemini, and OpenAI compatible APIs with smart auth detection
• 🔑 OAuth Channels - Codex (ChatGPT), Anthropic (Claude), Antigravity, and xAI OAuth credentials with automatic refresh where supported; Codex personal access token (PAT) authorization; Z.ai Coding Plan (ZCode) browser authorization or API-key import; Cursor user API-key import; and Zed native sign-in (trial bound to a real Zed installation's system_id), with batch quota refresh, invalid-credential cleanup, and auto-disable for permanently rejected credentials
• 📅 OAuth Quota Cost Tracking - Per-credential weekly/monthly standard-cost accumulation aligned to upstream quota windows, plus manual Codex quota reset when a reset credit is available
• 🔌 Responses WebSocket - Downstream Codex WebSocket sessions bridge to native Codex WebSocket or HTTP/SSE candidates with transcript-aware failover
• 📦 Simple Deployment - Embedded SQLite; the Cursor SDK Bridge is managed automatically when needed
• 🔒 Secure Authentication - Token-based admin interface and API access control
• 🏷️ Build Tags - GOTAGS support, high-performance JSON library enabled by default
• 🐳 Docker Support - Multi-arch images (amd64/arm64), automated CI/CD
• ☁️ Cloud Native - Container deployment support, GitHub Actions auto-build
• 🤗 Hugging Face - One-click deployment to Hugging Face Spaces, free hosting
• 💰 Cost Limits - Per-channel daily cost limits, per-token cost limits
• 🚦 Channel RPM Limits - Per-channel rolling 60-second request caps, 0=unlimited
• 🚧 Channel Concurrency Limits - Per-channel in-flight request caps, 0=unlimited
• 🗝️ Per-Key Model Allowlists - Restrict which channel models each Key serves; empty means unrestricted, and channels whose Keys all decline the model are skipped
• 🧠 Model Thinking Suffix - Append (minimal/low/medium/high/xhigh/max), (none), (auto), or a numeric budget to any model name; ccLoad maps it to the upstream protocol's thinking parameters while routing on the base name
• 🖼️ Multimodal Fallback - Route requests containing images/files from non-vision models to configured fallback models (model_multimodal_fallback), applied before thinking-suffix handling and channel/Key selection
• 🕒 Channel Time Windows - Optional HH:MM availability window per channel (server local time, cross-midnight supported); channels outside their window are fully excluded from routing
• 🔐 Token Restrictions - Per-token cost limits, model restrictions, channel allowlist/denylist, and concurrency caps for fine-grained access control
• ⏱️ TTFB Monitoring - Streaming request first byte time tracking for upstream latency diagnosis
• 🌐 Multi-URL Load Balancing - Multiple URLs per channel with latency-weighted random selection
• 🧭 Per-Channel Proxy - Route a channel's upstream traffic through an http/https/socks5/socks5h proxy with isolated connection pools
• 💵 service_tier Pricing - OpenAI priority/flex/default tier multipliers for accurate cost accounting
• 🖼️ Image Tool Billing - Responses image_generation/gpt-image-2 cost accounting
• 📉 Tiered Pricing - GPT-5.4/Qwen-Plus/Gemini long-context step pricing, auto-applies lower rate at token thresholds
• 🔄 Per-URL Protocol Routing - Explicit Anthropic/OpenAI/Codex/Gemini capability per URL, with native-first automatic detection when left empty
• 💬 Conversational Model Testing - Channel/model/chat testing modes with image upload, reasoning level, built-in search, and chat export
• 🎨 Image Generation Testing - Dedicated tab that renders generated images through either the Images API or Chat Completions, with size/quality/background/output-format controls
• 🔍 Debug Logs - Upstream request/response raw data capture with sensitive header masking, essential for troubleshooting
• 🕐 Scheduled Checks - Background periodic channel availability probing, auto-detect failed channels
• 🔄 Release Channels - Stable updates by default, with an opt-in preview channel; check interval is configurable from the admin settings page, plus a manual check button for on-demand checks
• 🧩 Custom Request Rules - Per-channel HTTP header & JSON body rewriting (remove/override/append), with auth header protection, CRLF guard, and capacity caps
• 🎛️ Log Column Customization - Show/hide table columns per preference, settings persist in browser localStorage

🏗️ Architecture Overview

Every channel accepts all four client protocols. Upstream protocol selection is controlled by protocol_transform_mode and each structured URL's protocols declaration. upstream is strict client-protocol passthrough. auto tries the client protocol first, then probes OpenAI → Anthropic → Codex → Gemini while skipping the protocol already attempted, and advances only after an uncommitted capability error. local prioritizes URLs with explicit declarations and follows each URL's declared order; only when every URL is undeclared does it try Anthropic → Codex → OpenAI → Gemini. Incompatible URLs are skipped without a request or cooldown. Successful automatic detection is cached per URL and request family until restart or channel configuration changes. Only stable endpoint-level non-model 404/405 responses cache an all-protocols-unsupported result for that URL and request family; it is probed again after 10 minutes. Request-dependent 400/403/500 responses and local transform failures are retried on the next request.

🚀 Quick Start

Choose the deployment method that suits you best:

Method	Difficulty	Cost	Use Case	HTTPS	Persistence
🐳 Docker	⭐⭐	VPS required	Production, high performance	Config required	✅
🤗 Hugging Face	⭐	Free	Personal use, quick trial	✅ Auto	✅
🔧 Source Build	⭐⭐⭐	Server required	Development, customization	Config required	✅
📦 Binary	⭐⭐	Server required	Lightweight, simple setup	Config required	✅

Method 1: Docker Deployment (Recommended)

Using pre-built images (Recommended):

# Option 1: Using docker-compose (Simplest)
curl -o docker-compose.yml https://raw.githubusercontent.com/caidaoli/ccLoad/master/docker-compose.yml
curl -o .env https://raw.githubusercontent.com/caidaoli/ccLoad/master/.env.docker.example
# Edit .env file to set CCLOAD_PASS (required, service exits without it)
docker-compose up -d

# Option 2: Run image directly
docker pull ghcr.io/caidaoli/ccload:latest
docker run -d --name ccload \
  -p 8080:8080 \
  -e CCLOAD_PASS=your_secure_password \
  -v ccload_data:/app/data \
  ghcr.io/caidaoli/ccload:latest

Building from source:

# Clone project
git clone https://github.com/caidaoli/ccLoad.git
cd ccLoad

# Build and run with docker-compose
cp .env.docker.example .env  # edit .env to set CCLOAD_PASS
docker-compose -f docker-compose.build.yml up -d

# Or build manually
docker build -t ccload:local .
docker run -d --name ccload \
  -p 8080:8080 \
  -e CCLOAD_PASS=your_secure_password \
  -v ccload_data:/app/data \
  ccload:local

Method 2: Source Build

# Clone project
git clone https://github.com/caidaoli/ccLoad.git
cd ccLoad

# Build project (uses high-performance JSON library by default)
go build -tags sonic -o ccload .

# Or use Makefile
make build

# Run in development mode
go run -tags sonic .
# Or
make dev

Method 3: Binary Download

# Download binary for your platform from GitHub Releases
wget https://github.com/caidaoli/ccLoad/releases/latest/download/ccload-linux-amd64
chmod +x ccload-linux-amd64
./ccload-linux-amd64

When a Cursor channel exists, ccLoad automatically downloads its pinned SDK Bridge, verifies the embedded SHA-256, and installs it atomically under its managed state directory. For offline installations, download the matching archive from the official Cursor SDK Bridge releases, place its cursor-sdk-bridge executable beside ccLoad, or set CURSOR_SDK_BRIDGE_BIN.

Method 4: Hugging Face Spaces Deployment

Hugging Face Spaces provides free container hosting with Docker support, ideal for personal and small team use.

Deployment Steps

1. Login to Hugging Face

   Visit huggingface.co and log into your account

2. Create New Space

   • Click "New" → "Space" in the top right
   • Space name: ccload (or custom name)
   • License: MIT
   • Select the SDK: Docker
   • Visibility: Public or Private (private requires paid subscription)
   • Click "Create Space"

3. Create Dockerfile

   Create a Dockerfile in the Space repository:

   FROM ghcr.io/caidaoli/ccload:latest
   ENV TZ=Asia/Shanghai
   ENV PORT=7860
   ENV SQLITE_PATH=/tmp/ccload.db
   EXPOSE 7860
   

   Create via:

   Method A - Web Interface (Recommended):

   • Click "Files" tab on Space page
   • Click "Add file" → "Create a new file"
   • Enter Dockerfile as filename
   • Paste the content above
   • Click "Commit new file to main"

   Method B - Git Command Line:

   # Clone your Space repository
   git clone https://huggingface.co/spaces/YOUR_USERNAME/ccload
   cd ccload
   
   # Create Dockerfile
   cat > Dockerfile << 'EOF'
   FROM ghcr.io/caidaoli/ccload:latest
   ENV TZ=Asia/Shanghai
   ENV PORT=7860
   ENV SQLITE_PATH=/tmp/ccload.db
   EXPOSE 7860
   EOF
   
   # Commit and push
   git add Dockerfile
   git commit -m "Add Dockerfile for ccLoad deployment"
   git push
   

4. Configure Environment Variables (Secrets)

   In Space settings (Settings → Variables and secrets → New secret):

   Variable	Value	Required	Description
   CCLOAD_PASS	None	✅ Required	Admin interface password
   CCLOAD_API_TOKENS	token1|production,token2|development	Optional	Pre-seed API access tokens on startup

   Note: API access tokens can be pre-seeded with CCLOAD_API_TOKENS or managed in the Web admin interface /web/tokens.html.

5. Wait for Build and Startup

   After pushing Dockerfile, Hugging Face will automatically:

   • Pull pre-built image (~30 seconds)
   • Start application container (~10 seconds)
   • Total time ~1-2 minutes (3-5x faster than source build)

6. Access Application

   After build completes, access via:

   • App URL: https://YOUR_USERNAME-ccload.hf.space
   • Admin Interface: https://YOUR_USERNAME-ccload.hf.space/web/
   • API Endpoint: https://YOUR_USERNAME-ccload.hf.space/v1/messages

   First Access Note:

   • If Space is sleeping, first access takes 20-30 seconds to wake
   • Subsequent accesses respond immediately

Hugging Face Deployment Characteristics

Advantages:

• ✅ Completely Free: Public Spaces are permanently free with CPU and storage
• ✅ Fast Deployment: Pre-built image, 1-2 minutes (3-5x faster than source build)
• ✅ Auto HTTPS: No SSL certificate configuration needed
• ✅ Auto Restart: Automatic restart after crashes
• ✅ Version Control: Git-based, easy rollback and collaboration
• ✅ Simple Maintenance: Only 5-line Dockerfile, no source code management

Limitations:

• ⚠️ Resource Limits: Free tier provides 2 CPU + 16GB RAM
• ⚠️ Sleep Policy: 48 hours without access triggers sleep, first access takes ~20-30s to wake
• ⚠️ Fixed Port: Must use port 7860
• ⚠️ Public Access: Spaces are public by default, must configure API tokens via Web admin to access /v1/* APIs (otherwise 401)

Data Persistence

Important: Hugging Face Spaces Storage Policy

Due to Hugging Face Spaces limitations (/tmp directory clears on restart), we strongly recommend using an external MySQL or PostgreSQL database for complete data persistence:

Option 1: Hybrid Storage Mode (Recommended, Best Performance)

• ✅ Local authoritative I/O: Config, credentials, keys, cooldowns, and logs commit to SQLite first, keeping remote latency off the scheduling path
• ✅ Eventually consistent primary: Writes are coalesced by entity and retried every 10 seconds after failure
• ⚠️ Single-instance semantics: Multiple hybrid writers and external primary writes are unsupported; process exit may lose in-memory pending syncs
• ✅ Stats caching: Smart TTL cache reduces repetitive aggregate queries
• Configuration: Add one primary DSN (CCLOAD_MYSQL or CCLOAD_POSTGRES) plus CCLOAD_ENABLE_SQLITE_REPLICA=1 in Secrets

Dockerfile Example (Hybrid Mode):

FROM ghcr.io/caidaoli/ccload:latest
ENV TZ=Asia/Shanghai
ENV PORT=7860
# Configure in Secrets: CCLOAD_MYSQL or CCLOAD_POSTGRES, plus CCLOAD_ENABLE_SQLITE_REPLICA=1
EXPOSE 7860

Option 2: Pure External Database Mode

• ✅ Complete Persistence: Channel configs, logs, and stats all preserved
• ✅ Restart-Safe: Data stored externally, unaffected by Space restarts
• ⚠️ Database Latency: Stats page latency depends on the remote database and region
• Configuration: Add exactly one of CCLOAD_MYSQL or CCLOAD_POSTGRES in Secrets

Recommended Free MySQL Services:

• TiDB Cloud Serverless - Free 5GB storage, MySQL compatible, no connection limits, recommended first choice
• Aiven for MySQL - Free 1GB storage, multi-region support

MySQL Configuration Example (TiDB Cloud):

1. Register for TiDB Cloud account
2. Create Serverless Cluster (free)
3. Get connection info, format: user:password@tcp(host:4000)/database?tls=true
4. Add CCLOAD_MYSQL variable in Hugging Face Space Secrets
5. (Optional) Enable Hybrid Mode: Add CCLOAD_ENABLE_SQLITE_REPLICA=1 for best performance
6. Restart Space, all data will auto-persist to MySQL

PostgreSQL Configuration Example:

CCLOAD_POSTGRES=postgres://user:password@host:5432/ccload?sslmode=require

URL and libpq keyword DSNs are supported. Do not set CCLOAD_MYSQL and CCLOAD_POSTGRES at the same time.

Dockerfile Example (Pure External Database):

FROM ghcr.io/caidaoli/ccload:latest
ENV TZ=Asia/Shanghai
ENV PORT=7860
# Configure CCLOAD_MYSQL or CCLOAD_POSTGRES in Secrets; SQLITE_PATH is not required
EXPOSE 7860

Option 3: Local Storage Only (Not Recommended)

• ⚠️ Data Loss: /tmp clears on Space restart, channel config lost
• ⚠️ Manual Recovery: Must re-import via Web interface or CSV
• Use case: Temporary testing only

Update Deployment

With pre-built images, updates are simple:

Image Refresh:

• When new version image (ghcr.io/caidaoli/ccload:latest) is released
• Click "Factory rebuild" in Space settings to pull latest image
• Or wait for Hugging Face auto-restart (typically after 48 hours)

Manual Trigger Update:

# Add empty commit to trigger rebuild
git commit --allow-empty -m "Trigger rebuild to pull latest image"
git push

Version Pinning (Optional): To lock specific version, modify Dockerfile:

FROM ghcr.io/caidaoli/ccload:v4.7.0  # Specify version
ENV TZ=Asia/Shanghai
ENV PORT=7860
ENV SQLITE_PATH=/tmp/ccload.db
EXPOSE 7860

Basic Configuration

Choose SQLite, MySQL, or PostgreSQL based on the deployment shape. MySQL and PostgreSQL are mutually exclusive.

SQLite Mode (Default):

# Set environment variables
export CCLOAD_PASS=your_admin_password
export PORT=8080
export SQLITE_PATH=./data/ccload.db

# Or use .env file
echo "CCLOAD_PASS=your_admin_password" > .env
echo "PORT=8080" >> .env
echo "SQLITE_PATH=./data/ccload.db" >> .env

# Start service
./ccload

MySQL Mode:

# 1. Create MySQL database
mysql -u root -p -e "CREATE DATABASE ccload CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;"

# 2. Set environment variables
export CCLOAD_PASS=your_admin_password
export CCLOAD_MYSQL="user:password@tcp(localhost:3306)/ccload?charset=utf8mb4"
export PORT=8080

# Or use .env file
echo "CCLOAD_PASS=your_admin_password" > .env
echo "CCLOAD_MYSQL=user:password@tcp(localhost:3306)/ccload?charset=utf8mb4" >> .env
echo "PORT=8080" >> .env

# 3. Start service (auto-creates tables)
./ccload

PostgreSQL Mode:

# 1. Create the database and user in PostgreSQL

# 2. Set environment variables
export CCLOAD_PASS=your_admin_password
export CCLOAD_POSTGRES="postgres://user:password@localhost:5432/ccload?sslmode=disable"
export PORT=8080

# 3. Start service (auto-creates and migrates tables)
./ccload

Docker + MySQL:

# Option 1: docker-compose (Recommended)
cat > docker-compose.mysql.yml << 'EOF'
version: '3.8'
services:
  mysql:
    image: mysql:8.0
    environment:
      MYSQL_ROOT_PASSWORD: rootpass
      MYSQL_DATABASE: ccload
      MYSQL_USER: ccload
      MYSQL_PASSWORD: ccloadpass
    volumes:
      - mysql_data:/var/lib/mysql
    ports:
      - "3306:3306"
    healthcheck:
      test: ["CMD", "mysqladmin", "ping", "-h", "localhost"]
      interval: 10s
      timeout: 5s
      retries: 5

  ccload:
    image: ghcr.io/caidaoli/ccload:latest
    environment:
      CCLOAD_PASS: your_admin_password
      CCLOAD_MYSQL: "ccload:ccloadpass@tcp(mysql:3306)/ccload?charset=utf8mb4"
      PORT: 8080
    ports:
      - "8080:8080"
    depends_on:
      mysql:
        condition: service_healthy

volumes:
  mysql_data:
EOF

docker-compose -f docker-compose.mysql.yml up -d

# Option 2: Direct run (requires existing MySQL service)
docker run -d --name ccload \
  -p 8080:8080 \
  -e CCLOAD_PASS=