codex-lb-load-balancer

name	codex-lb-load-balancer
description	ChatGPT/Codex multi-account load balancer with usage tracking, API key management, and OpenAI-compatible proxy
triggers	["set up codex load balancer for multiple accounts","configure chatgpt account pooling","track openai api usage across accounts","manage api keys with rate limits","deploy codex-lb with docker","balance load across multiple chatgpt accounts","set up openai compatible proxy","monitor chatgpt usage and costs"]

codex-lb Load Balancer

Skill by ara.so — Codex Skills collection.

Overview

codex-lb is a load balancer and proxy for ChatGPT/Codex accounts that pools multiple OpenAI accounts, tracks usage per account and API key, enforces rate limits, and provides a web dashboard for management. It exposes OpenAI-compatible endpoints (/v1/* and /backend-api/codex/*) for any client (Codex CLI, OpenCode, OpenClaw, OpenAI SDK).

Key features:

• Pool multiple ChatGPT accounts with automatic load balancing
• Per-account usage tracking (tokens, cost, 28-day trends)
• API key management with per-key rate limits (token, cost, time window, model)
• Dashboard with password + optional TOTP authentication
• Auto-sync available models from upstream
• WebSocket support for native Codex streaming

Installation

Docker (Recommended)

docker volume create codex-lb-data
docker run -d --name codex-lb \
  -p 2455:2455 -p 1455:1455 \
  -v codex-lb-data:/var/lib/codex-lb \
  ghcr.io/soju06/codex-lb:latest

uvx (Python)

uvx codex-lb

Dashboard: http://localhost:2455
Proxy endpoints:

• OpenAI v1: http://localhost:2455/v1
• Codex backend: http://localhost:2455/backend-api/codex

Health check: http://localhost:1455/health

Configuration

First-Run Setup (Remote Access)

When accessing the dashboard remotely for the first time, you need a bootstrap token to set the password.

Auto-generated token (default):

docker logs codex-lb
# Look for:
# ============================================
#   Dashboard bootstrap token (first-run):
#   <token>
# ============================================

Open dashboard → enter token + new password → done.

Manual bootstrap token:

docker run -d --name codex-lb \
  -e CODEX_LB_DASHBOARD_BOOTSTRAP_TOKEN=your-secret-token \
  -p 2455:2455 -p 1455:1455 \
  -v codex-lb-data:/var/lib/codex-lb \
  ghcr.io/soju06/codex-lb:latest

Local access (localhost) bypasses bootstrap entirely.

Adding Accounts

1. Open dashboard → Accounts → Add Account
2. Paste OpenAI session token or login credentials
3. Account syncs available models automatically

API Key Authentication

API key auth is disabled by default. Enable in Settings → API Key Auth when:

• Clients connect remotely
• Running in Docker/VM/container with non-local networking

When enabled, clients must pass a Bearer token:

Authorization: Bearer sk-clb-YOUR_KEY_HERE

Create API keys in dashboard → API Keys → Create Key.

Configure rate limits per key:

• Token limit (per window)
• Cost limit (USD, per window)
• Time window (e.g., 1 hour, 1 day)
• Allowed models

Environment Variables

Variable	Default	Description
CODEX_LB_DASHBOARD_BOOTSTRAP_TOKEN	auto-generated	Bootstrap token for first-run password setup
CODEX_LB_UPSTREAM_STREAM_TRANSPORT	auto	auto, websocket, or http for Codex streaming
CODEX_LB_UPSTREAM_WEBSOCKET_TRUST_ENV	false	Use environment proxies for WebSocket handshakes
CODEX_LB_DATA_DIR	/var/lib/codex-lb	Data directory for SQLite DB and state

Client Configuration

Codex CLI

~/.codex/config.toml:

model = "gpt-5.3-codex"
model_reasoning_effort = "xhigh"
model_provider = "codex-lb"

[model_providers.codex-lb]
name = "OpenAI"
base_url = "http://127.0.0.1:2455/backend-api/codex"
wire_api = "responses"
supports_websockets = true
requires_openai_auth = true

With API key auth:

[model_providers.codex-lb]
name = "OpenAI"
base_url = "http://127.0.0.1:2455/backend-api/codex"
wire_api = "responses"
env_key = "CODEX_LB_API_KEY"
supports_websockets = true
requires_openai_auth = true

export CODEX_LB_API_KEY="sk-clb-..."
codex

Enable native WebSocket streaming:

export CODEX_LB_UPSTREAM_STREAM_TRANSPORT=websocket

Verify WebSocket transport:

RUST_LOG=debug codex exec "Reply with OK only."

Healthy signals:

• CLI logs: connecting to websocket, successfully connected to websocket
• codex-lb logs: WebSocket /backend-api/codex/responses
• No fallback POST /backend-api/codex/responses

Migrate existing sessions from OpenAI provider:

# JSONL session files
find ~/.codex/sessions -name '*.jsonl' \
  -exec sed -i '' 's/"model_provider":"openai"/"model_provider":"codex-lb"/g' {} +

# SQLite state DB (>= v0.105.0)
sqlite3 ~/.codex/state_5.sqlite \
  "UPDATE threads SET model_provider = 'codex-lb' WHERE model_provider = 'openai';"

OpenCode

Important: Use the built-in openai provider with baseURL override. Custom providers with @ai-sdk/openai-compatible drop reasoning content.

Clear existing OpenAI credentials:

jq 'del(.openai)' ~/.local/share/opencode/auth.json > auth.json.tmp && mv auth.json.tmp ~/.local/share/opencode/auth.json

~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "openai": {
      "options": {
        "baseURL": "http://127.0.0.1:2455/v1",
        "apiKey": "{env:CODEX_LB_API_KEY}"
      },
      "models": {
        "gpt-5.4": {
          "name": "GPT-5.4",
          "reasoning": true,
          "options": { "reasoningEffort": "high", "reasoningSummary": "detailed" },
          "limit": { "context": 1050000,   
        
         
           
           
                
                
        
      
    
  
   

export CODEX_LB_API_KEY="sk-clb-..."
opencode

OpenClaw

~/.openclaw/openclaw.json:

{
  "agents": {
    "defaults": {
      "model": { "primary": "codex-lb/gpt-5.4" },
      "models": {
        "codex-lb/gpt-5.4": { "params": { "cacheRetention": "short" } },
        "codex-lb/gpt-5.3-codex": { "params": { "cacheRetention": "short" } }
      }
    }
  },
  "models": {
    "mode": "merge",
    "providers": {
      "codex-lb": {
         
         
         
         
          
             
             
             
             
             
             
             
          
          
             
             
             
             
             
             
             
          
        
      
    
  

export CODEX_LB_API_KEY="sk-clb-..."

OpenAI Python SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:2455/v1",
    api_key=os.environ["CODEX_LB_API_KEY"],  # or "dummy" if auth disabled
)

response = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Any OpenAI-Compatible Client

Point the base URL to:

• Chat Completions API: http://127.0.0.1:2455/v1
• Codex Backend API: http://127.0.0.1:2455/backend-api/codex

Pass API key as Authorization: Bearer sk-clb-... header if auth is enabled.

Usage Patterns

Multi-Account Load Balancing

codex-lb automatically distributes requests across healthy accounts based on:

• Account availability
• Usage limits
• Model availability per account

Add accounts in dashboard → Accounts → Add Account. Disable individual accounts temporarily without removing them.

Rate Limiting

Configure per-API-key limits in dashboard → API Keys → Edit Key:

Token limit:

Max tokens: 100000
Window: 1 hour

Cost limit:

Max cost: 5.00 USD
Window: 1 day

Model restrictions:

Allowed models: gpt-5.3-codex, gpt-5.4-mini

Usage Tracking

Dashboard → Accounts shows per-account:

• Total tokens used
• Total cost (USD)
• 28-day usage trends
• Model availability

Dashboard → API Keys shows per-key:

• Requests made
• Tokens consumed
• Cost incurred
• Rate limit status

WebSocket Streaming (Codex)

codex-lb supports native WebSocket streaming for Codex CLI:

Auto mode (default):

# Uses WebSocket for native Codex headers or preferred models
# Falls back to HTTP for others

Force WebSocket:

export CODEX_LB_UPSTREAM_STREAM_TRANSPORT=websocket

Force HTTP:

export CODEX_LB_UPSTREAM_STREAM_TRANSPORT=http

Reverse proxy setup — ensure your reverse proxy forwards WebSocket upgrades:

nginx:

location / {
    proxy_pass http://codex-lb:2455;
    proxy_http_version 1.1;
    proxy_set_header Upgrade $http_upgrade;
    proxy_set_header Connection "upgrade";
}

Caddy:

reverse_proxy localhost:2455

(Caddy handles WebSockets automatically)

Troubleshooting

Bootstrap token not working

• Local access: No token needed for localhost. Access dashboard directly.
• Remote access: Check logs for auto-generated token:

  docker logs codex-lb | grep "bootstrap token"
  

• Token used: Bootstrap token is one-time. If password is already set, log in with password.

API key rejected

• Auth disabled: Set Authorization: Bearer dummy or any non-empty string. If remote request is rejected, enable API key auth in dashboard.
• Auth enabled: Create API key in dashboard → API Keys. Pass as Authorization: Bearer sk-clb-....
• Rate limit: Check dashboard → API Keys for limit status. Increase limits or wait for window reset.

WebSocket connection fails (Codex)

• Verify transport: Run RUST_LOG=debug codex exec "test" and check logs for connecting to websocket.
• Reverse proxy: Ensure proxy forwards WebSocket upgrades (see nginx/Caddy examples above).
• Direct connection: Set CODEX_LB_UPSTREAM_WEBSOCKET_TRUST_ENV=false to bypass environment proxies.
• Fallback to HTTP: codex-lb automatically falls back. Check logs for POST /backend-api/codex/responses instead of WebSocket.

Account sync fails

• Invalid credentials: Re-add account with fresh session token.
• OpenAI API changes: Update codex-lb to latest version:

  docker pull ghcr.io/soju06/codex-lb:latest
  docker restart codex-lb
  

• Model availability: Some models may not be available on all accounts. Check dashboard → Accounts → Models.

Usage not tracking

• Check logs: Look for errors in request processing.
• Database corruption: Backup and recreate volume:

  docker stop codex-lb
  docker volume create codex-lb-data-backup
  docker run --rm -v codex-lb-data:/from -v codex-lb-data-backup:/to alpine sh -c "cp -av /from/. /to"
  docker volume rm codex-lb-data
  docker volume create codex-lb-data
  docker start codex-lb
  

Codex sessions not appearing after migration

Run migration commands to update model_provider in session files and SQLite DB:

# JSONL sessions
find ~/.codex/sessions -name '*.jsonl' \
  -exec sed -i '' 's/"model_provider":"openai"/"model_provider":"codex-lb"/g' {} +

# SQLite state DB
sqlite3 ~/.codex/state_5.sqlite \
  "UPDATE threads SET model_provider = 'codex-lb' WHERE model_provider = 'openai';"

Dashboard won't load

• Port conflict: Check if port 2455 is in use:

  lsof -i :2455
  docker ps
  

• Change port:

  docker run -d --name codex-lb -p 3000:2455 -p 1455:1455 \
    -v codex-lb-data:/var/lib/codex-lb ghcr.io/soju06/codex-lb:latest
  

  Access at http://localhost:3000.

High latency or timeout

• Add more accounts: Distribute load across more ChatGPT accounts.
• Check account health: Dashboard → Accounts → disable slow or failing accounts.
• Increase timeout: Configure client timeout (e.g., OpenAI SDK timeout=120).

Advanced Configuration

Multi-Replica Setup

For high availability, run multiple codex-lb replicas sharing the same encryption key:

# Generate shared key
SHARED_KEY=$(openssl rand -hex 32)

# Replica 1
docker run -d --name codex-lb-1 \
  -e CODEX_LB_ENCRYPTION_KEY=$SHARED_KEY \
  -p 2455:2455 -p 1455:1455 \
  -v codex-lb-data:/var/lib/codex-lb \
  ghcr.io/soju06/codex-lb:latest

# Replica 2
docker run -d --name codex-lb-2 \
  -e CODEX_LB_ENCRYPTION_KEY=$SHARED_KEY \
  -p 2456:2455 -p 1456:1455 \
  -v codex-lb-data:/var/lib/codex-lb \
  ghcr.io/soju06/codex-lb:latest

Put replicas behind a load balancer (nginx, HAProxy, etc.).

Custom Data Directory

docker run -d --name codex-lb \
  -e CODEX_LB_DATA_DIR=/custom/path \
  -v /host/custom/path:/custom/path \
  -p 2455:2455 -p 1455:1455 \
  ghcr.io/soju06/codex-lb:latest

Kubernetes/Helm

See project repository for Helm chart. Key points:

• Share encryption key across pods via Secret
• Use PersistentVolumeClaim for SQLite database
• Configure Ingress for WebSocket support

Common Patterns

Development Team Usage

Each developer gets an API key with daily cost limit:

# Team member A
client = OpenAI(
    base_url="https://codex-lb.company.com/v1",
    api_key=os.environ["DEV_A_API_KEY"],  # $10/day limit
)

# Team member B
client = OpenAI(
    base_url="https://codex-lb.company.com/v1",
    api_key=os.environ["DEV_B_API_KEY"],  # $5/day limit
)

Monitor usage in dashboard → API Keys.

CI/CD Integration

Separate API key for CI with token limit:

# .github/workflows/test.yml
- name: Run AI tests
  env:
    CODEX_LB_API_KEY: ${{ secrets.CI_CODEX_KEY }}  # 50k tokens/hour
  run: |
    pytest tests/ai/

Model-Specific Routing

Restrict expensive models to specific keys:

Production key: gpt-5.4, gpt-5.3-codex allowed, $50/day
Development key: gpt-5.4-mini, gpt-5.1-codex-mini allowed, $10/day

Configure in dashboard → API Keys → Allowed Models.

---

This skill enables AI coding agents to help developers deploy, configure, and troubleshoot codex-lb for managing multiple ChatGPT/Codex accounts with load balancing, usage tracking, and rate limiting.