gpt-load

Multi-channel AI proxy with intelligent key rotation. 智能密钥轮询的多渠道 AI 代理。

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GPT-Load is a high-performance, enterprise-grade AI API transparent proxy service designed for enterprises and developers needing to integrate multiple AI services. Built with Go, it features intelligent key management, load balancing, and comprehensive monitoring capabilities for high-concurrency production environments. The tool serves as a transparent proxy service, preserving native API formats of various AI service providers like OpenAI, Google Gemini, and Anthropic Claude. It supports dynamic configuration, distributed leader-follower deployment, and a Vue 3-based web management interface. GPT-Load is production-ready with features like dual authentication, graceful shutdown, and error recovery.

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GPT-Load

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A high-performance, enterprise-grade AI API transparent proxy service designed specifically for enterprises and developers who need to integrate multiple AI services. Built with Go, featuring intelligent key management, load balancing, and comprehensive monitoring capabilities, designed for high-concurrency production environments.

For detailed documentation, please visit Official Documentation

Features

Transparent Proxy: Complete preservation of native API formats, supporting OpenAI, Google Gemini, and Anthropic Claude among other formats
Intelligent Key Management: High-performance key pool with group-based management, automatic rotation, and failure recovery
Load Balancing: Weighted load balancing across multiple upstream endpoints to enhance service availability
Smart Failure Handling: Automatic key blacklist management and recovery mechanisms to ensure service continuity
Dynamic Configuration: System settings and group configurations support hot-reload without requiring restarts
Enterprise Architecture: Distributed leader-follower deployment supporting horizontal scaling and high availability
Modern Management: Vue 3-based web management interface that is intuitive and user-friendly
Comprehensive Monitoring: Real-time statistics, health checks, and detailed request logging
High-Performance Design: Zero-copy streaming, connection pool reuse, and atomic operations
Production Ready: Graceful shutdown, error recovery, and comprehensive security mechanisms
Dual Authentication: Separate authentication for management and proxy, with proxy authentication supporting global and group-level keys

Supported AI Services

GPT-Load serves as a transparent proxy service, completely preserving the native API formats of various AI service providers:

OpenAI Format: Official OpenAI API, Azure OpenAI, and other OpenAI-compatible services
Google Gemini Format: Native APIs for Gemini Pro, Gemini Pro Vision, and other models
Anthropic Claude Format: Claude series models, supporting high-quality conversations and text generation

Quick Start

System Requirements

Go 1.24+ (for source builds)
Docker (for containerized deployment)
MySQL, PostgreSQL, or SQLite (for database storage)
Redis (for caching and distributed coordination, optional)

Method 1: Docker Quick Start

docker run -d --name gpt-load \
    -p 3001:3001 \
    -e AUTH_KEY=your-secure-key-here \
    -v "$(pwd)/data":/app/data \
    ghcr.io/tbphp/gpt-load:latest

Please change your-secure-key-here to a strong password (never use the default value), then you can log in to the management interface: http://localhost:3001

Method 2: Using Docker Compose (Recommended)

Installation Commands:

# Create Directory
mkdir -p gpt-load && cd gpt-load

# Download configuration files
wget https://raw.githubusercontent.com/tbphp/gpt-load/refs/heads/main/docker-compose.yml
wget -O .env https://raw.githubusercontent.com/tbphp/gpt-load/refs/heads/main/.env.example

# Edit the .env file and change AUTH_KEY to a strong password. Never use default or simple keys like sk-123456.

# Start services
docker compose up -d

Before deployment, you must change the default admin key (AUTH_KEY). A recommended format is: sk-prod-[32-character random string].

The default installation uses the SQLite version, which is suitable for lightweight, single-instance applications.

If you need to install MySQL, PostgreSQL, and Redis, please uncomment the required services in the docker-compose.yml file, configure the corresponding environment variables, and restart.

Other Commands:

# Check service status
docker compose ps

# View logs
docker compose logs -f

# Restart Service
docker compose down && docker compose up -d

# Update to latest version
docker compose pull && docker compose down && docker compose up -d

After deployment:

Access Web Management Interface: http://localhost:3001
API Proxy Address: http://localhost:3001/proxy

Use your modified AUTH_KEY to log in to the management interface.

Method 3: Source Build

Source build requires a locally installed database (SQLite, MySQL, or PostgreSQL) and Redis (optional).

# Clone and build
git clone https://github.com/tbphp/gpt-load.git
cd gpt-load
go mod tidy

# Create configuration
cp .env.example .env

# Edit the .env file and change AUTH_KEY to a strong password. Never use default or simple keys like sk-123456.
# Modify DATABASE_DSN and REDIS_DSN configurations in .env
# REDIS_DSN is optional; if not configured, memory storage will be enabled

# Run
make run

After deployment:

Access Web Management Interface: http://localhost:3001
API Proxy Address: http://localhost:3001/proxy

Use your modified AUTH_KEY to log in to the management interface.

Method 4: Cluster Deployment

Cluster deployment requires all nodes to connect to the same MySQL (or PostgreSQL) and Redis, with Redis being mandatory. It's recommended to use unified distributed MySQL and Redis clusters.

Deployment Requirements:

All nodes must configure identical AUTH_KEY, DATABASE_DSN, REDIS_DSN
Leader-follower architecture where follower nodes must configure environment variable: IS_SLAVE=true

For details, please refer to Cluster Deployment Documentation

Configuration System

Configuration Architecture Overview

GPT-Load adopts a dual-layer configuration architecture:

1. Static Configuration (Environment Variables)

Characteristics: Read at application startup, immutable during runtime, requires application restart to take effect
Purpose: Infrastructure configuration such as database connections, server ports, authentication keys, etc.
Management: Set via .env files or system environment variables

2. Dynamic Configuration (Hot-Reload)

System Settings: Stored in database, providing unified behavioral standards for the entire application
Group Configuration: Behavior parameters customized for specific groups, can override system settings
Configuration Priority: Group Configuration > System Settings > Environment Configuration
Characteristics: Supports hot-reload, takes effect immediately after modification without application restart

Static Configuration (Environment Variables)

Server Configuration:

Setting	Environment Variable	Default	Description
Service Port	`PORT`	3001	HTTP server listening port
Service Address	`HOST`	0.0.0.0	HTTP server binding address
Read Timeout	`SERVER_READ_TIMEOUT`	60	HTTP server read timeout (seconds)
Write Timeout	`SERVER_WRITE_TIMEOUT`	600	HTTP server write timeout (seconds)
Idle Timeout	`SERVER_IDLE_TIMEOUT`	120	HTTP connection idle timeout (seconds)
Graceful Shutdown Timeout	`SERVER_GRACEFUL_SHUTDOWN_TIMEOUT`	10	Service graceful shutdown wait time (seconds)
Follower Mode	`IS_SLAVE`	false	Follower node identifier for cluster deployment
Timezone	`TZ`	`Asia/Shanghai`	Specify timezone

Security Configuration:

Setting	Environment Variable	Default	Description
Admin Key	`AUTH_KEY`	-	Access authentication key for the management end, please change it to a strong password
Encryption Key	`ENCRYPTION_KEY`	-	Encrypts API keys at rest. Supports any string or leave empty to disable encryption. See Data Encryption Migration

Database Configuration:

Setting	Environment Variable	Default	Description
Database Connection	`DATABASE_DSN`	`./data/gpt-load.db`	Database connection string (DSN) or file path
Redis Connection	`REDIS_DSN`	-	Redis connection string, uses memory storage when empty

Performance & CORS Configuration:

Setting	Environment Variable	Default	Description
Max Concurrent Requests	`MAX_CONCURRENT_REQUESTS`	100	Maximum concurrent requests allowed by system
Enable CORS	`ENABLE_CORS`	false	Whether to enable Cross-Origin Resource Sharing
Allowed Origins	`ALLOWED_ORIGINS`	-	Allowed origins, comma-separated
Allowed Methods	`ALLOWED_METHODS`	`GET,POST,PUT,DELETE,OPTIONS`	Allowed HTTP methods
Allowed Headers	`ALLOWED_HEADERS`	`*`	Allowed request headers, comma-separated
Allow Credentials	`ALLOW_CREDENTIALS`	false	Whether to allow sending credentials

Logging Configuration:

Setting	Environment Variable	Default	Description
Log Level	`LOG_LEVEL`	`info`	Log level: debug, info, warn, error
Log Format	`LOG_FORMAT`	`text`	Log format: text, json
Enable File Logging	`LOG_ENABLE_FILE`	false	Whether to enable file log output
Log File Path	`LOG_FILE_PATH`	`./data/logs/app.log`	Log file storage path

Proxy Configuration:

GPT-Load automatically reads proxy settings from environment variables to make requests to upstream AI providers.

Setting	Environment Variable	Default	Description
HTTP Proxy	`HTTP_PROXY`	-	Proxy server address for HTTP requests
HTTPS Proxy	`HTTPS_PROXY`	-	Proxy server address for HTTPS requests
No Proxy	`NO_PROXY`	-	Comma-separated list of hosts or domains to bypass the proxy

Supported Proxy Protocol Formats:

HTTP: http://user:pass@host:port
HTTPS: https://user:pass@host:port
SOCKS5: socks5://user:pass@host:port

Dynamic Configuration (Hot-Reload)

Basic Settings:

Setting	Field Name	Default	Group Override	Description
Project URL	`app_url`	`http://localhost:3001`	❌	Project base URL
Global Proxy Keys	`proxy_keys`	Initial value from `AUTH_KEY`	❌	Globally effective proxy keys, comma-separated
Log Retention Days	`request_log_retention_days`	7	❌	Request log retention days, 0 for no cleanup
Log Write Interval	`request_log_write_interval_minutes`	1	❌	Log write to database cycle (minutes)
Enable Request Body Logging	`enable_request_body_logging`	false	✅	Whether to log complete request body content in request logs

Request Settings:

Setting	Field Name	Default	Group Override	Description
Request Timeout	`request_timeout`	600	✅	Forward request complete lifecycle timeout (seconds)
Connection Timeout	`connect_timeout`	15	✅	Timeout for establishing connection with upstream service (seconds)
Idle Connection Timeout	`idle_conn_timeout`	120	✅	HTTP client idle connection timeout (seconds)
Response Header Timeout	`response_header_timeout`	600	✅	Timeout for waiting upstream response headers (seconds)
Max Idle Connections	`max_idle_conns`	100	✅	Connection pool maximum total idle connections
Max Idle Connections Per Host	`max_idle_conns_per_host`	50	✅	Maximum idle connections per upstream host
Proxy URL	`proxy_url`	-	✅	HTTP/HTTPS proxy for forwarding requests, uses environment if empty

Key Configuration:

Setting	Field Name	Default	Group Override	Description
Max Retries	`max_retries`	3	✅	Maximum retry count using different keys for single request
Blacklist Threshold	`blacklist_threshold`	3	✅	Number of consecutive failures before key enters blacklist
Key Validation Interval	`key_validation_interval_minutes`	60	✅	Background scheduled key validation cycle (minutes)
Key Validation Concurrency	`key_validation_concurrency`	10	✅	Concurrency for background validation of invalid keys
Key Validation Timeout	`key_validation_timeout_seconds`	20	✅	API request timeout for validating individual keys in background (seconds)

Data Encryption Migration

GPT-Load supports encrypted storage of API keys. You can enable, disable, or change the encryption key at any time.

View Data Encryption Migration Details

Migration Scenarios

Enable Encryption: Encrypt plaintext data for storage - Use --to <new-key>
Disable Encryption: Decrypt encrypted data to plaintext - Use --from <current-key>
Change Encryption Key: Replace the encryption key - Use --from <current-key> --to <new-key>

Operation Steps

Docker Compose Deployment

# 1. Update the image (ensure using the latest version)
docker compose pull

# 2. Stop the service
docker compose down

# 3. Backup the database (strongly recommended)
# Before migration, you must manually backup the database or export your keys to avoid key loss due to operations or exceptions.

# 4. Execute migration command
# Enable encryption (your-32-char-secret-key is your key, recommend using 32+ character random string)
docker compose run --rm gpt-load migrate-keys --to "your-32-char-secret-key"

# Disable encryption
docker compose run --rm gpt-load migrate-keys --from "your-current-key"

# Change encryption key
docker compose run --rm gpt-load migrate-keys --from "old-key" --to "new-32-char-secret-key"

# 5. Update configuration file
# Edit .env file, set ENCRYPTION_KEY to match the --to parameter
# If disabling encryption, remove ENCRYPTION_KEY or set it to empty
vim .env
# Add or modify: ENCRYPTION_KEY=your-32-char-secret-key

# 6. Restart the service
docker compose up -d

Source Build Deployment

# 1. Stop the service
# Stop the running service process (Ctrl+C or kill process)

# 2. Backup the database (strongly recommended)
# Before migration, you must manually backup the database or export your keys to avoid key loss due to operations or exceptions.

# 3. Execute migration command
# Enable encryption
make migrate-keys ARGS="--to your-32-char-secret-key"

# Disable encryption
make migrate-keys ARGS="--from your-current-key"

# Change encryption key
make migrate-keys ARGS="--from old-key --to new-32-char-secret-key"

# 4. Update configuration file
# Edit .env file, set ENCRYPTION_KEY to match the --to parameter
echo "ENCRYPTION_KEY=your-32-char-secret-key" >> .env

# 5. Restart the service
make run

Important Notes

⚠️ Important Reminders:

Once ENCRYPTION_KEY is lost, encrypted data CANNOT be recovered! Please securely backup this key. Consider using a password manager or secure key management system
Service must be stopped before migration to avoid data inconsistency
Strongly recommended to backup the database in case migration fails and recovery is needed
Keys should use 32 characters or longer random strings for security
Ensure ENCRYPTION_KEY in .env matches the --to parameter after migration
If disabling encryption, remove or clear the ENCRYPTION_KEY configuration

Key Generation Examples

# Generate secure random key (32 characters)
openssl rand -base64 32 | tr -d "=+/" | cut -c1-32

Web Management Interface

Access the management console at: http://localhost:3001 (default address)

Interface Overview

The web management interface provides the following features:

Dashboard: Real-time statistics and system status overview
Key Management: Create and configure AI service provider groups, add, delete, and monitor API keys
Request Logs: Detailed request history and debugging information
System Settings: Global configuration management and hot-reload

API Usage Guide

Proxy Interface Invocation

GPT-Load routes requests to different AI services through group names. Usage is as follows:

1. Proxy Endpoint Format

http://localhost:3001/proxy/{group_name}/{original_api_path}

{group_name}: Group name created in the management interface
{original_api_path}: Maintain complete consistency with original AI service paths

2. Authentication Methods

Configure Proxy Keys in the web management interface, which supports system-level and group-level proxy keys.

Authentication Method: Consistent with the native API, but replace the original key with the configured proxy key.
Key Scope: Global Proxy Keys configured in system settings can be used in all groups. Group Proxy Keys configured in a group are only valid for the current group.
Format: Multiple keys are separated by commas.

3. OpenAI Interface Example

GPT-Load currently supports two OpenAI-compatible group types:

openai (OpenAI Chat Completions format)
openai-response (OpenAI Responses format)

Assuming a group named openai was created:

Original invocation:

curl -X POST https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer sk-your-openai-key" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4.1-mini", "messages": [{"role": "user", "content": "Hello"}]}'

Proxy invocation:

curl -X POST http://localhost:3001/proxy/openai/v1/chat/completions \
  -H "Authorization: Bearer your-proxy-key" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4.1-mini", "messages": [{"role": "user", "content": "Hello"}]}'

Changes required:

Replace https://api.openai.com with http://localhost:3001/proxy/openai
Replace original API Key with the Proxy Key

OpenAI Responses format example (openai-response group):

curl -X POST http://localhost:3001/proxy/openai-response/v1/responses \
  -H "Authorization: Bearer your-proxy-key" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-4.1-mini", "input": "Hello"}'

4. Gemini Interface Example

Assuming a group named gemini was created:

Original invocation:

curl -X POST https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent?key=your-gemini-key \
  -H "Content-Type: application/json" \
  -d '{"contents": [{"parts": [{"text": "Hello"}]}]}'

Proxy invocation:

curl -X POST http://localhost:3001/proxy/gemini/v1beta/models/gemini-2.5-pro:generateContent?key=your-proxy-key \
  -H "Content-Type: application/json" \
  -d '{"contents": [{"parts": [{"text": "Hello"}]}]}'

Changes required:

Replace https://generativelanguage.googleapis.com with http://localhost:3001/proxy/gemini
Replace key=your-gemini-key in URL parameter with the Proxy Key

5. Anthropic Interface Example

Assuming a group named anthropic was created:

Original invocation:

curl -X POST https://api.anthropic.com/v1/messages \
  -H "x-api-key: sk-ant-api03-your-anthropic-key" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model": "claude-sonnet-4-20250514", "messages": [{"role": "user", "content": "Hello"}]}'

Proxy invocation:

curl -X POST http://localhost:3001/proxy/anthropic/v1/messages \
  -H "x-api-key: your-proxy-key" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{"model": "claude-sonnet-4-20250514", "messages": [{"role": "user", "content": "Hello"}]}'

Changes required:

Replace https://api.anthropic.com with http://localhost:3001/proxy/anthropic
Replace the original API Key in x-api-key header with the Proxy Key

6. Supported Interfaces

OpenAI Chat Completions Format (openai):

/v1/chat/completions - Chat conversations
/v1/completions - Text completion
/v1/embeddings - Text embeddings
/v1/models - Model list
And all other OpenAI-compatible interfaces

OpenAI Responses Format (openai-response):

/v1/responses - Unified response generation
/v1/models - Model list
And all other OpenAI Responses-compatible interfaces

Gemini Format:

/v1beta/models/*/generateContent - Content generation
/v1beta/models - Model list
And all other Gemini native interfaces

Anthropic Format:

/v1/messages - Message conversations
/v1/models - Model list (if available)
And all other Anthropic native interfaces

7. Client SDK Configuration

OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(
    api_key="your-proxy-key",  # Use the proxy key
    base_url="http://localhost:3001/proxy/openai"  # Use proxy endpoint
)

response = client.chat.completions.create(
    model="gpt-4.1-mini",
    messages=[{"role": "user", "content": "Hello"}]
)

Google Gemini SDK (Python):

import google.generativeai as genai

# Configure API key and base URL
genai.configure(
    api_key="your-proxy-key",  # Use the proxy key
    client_options={"api_endpoint": "http://localhost:3001/proxy/gemini"}
)

model = genai.GenerativeModel('gemini-2.5-pro')
response = model.generate_content("Hello")

Anthropic SDK (Python):

from anthropic import Anthropic

client = Anthropic(
    api_key="your-proxy-key",  # Use the proxy key
    base_url="http://localhost:3001/proxy/anthropic"  # Use proxy endpoint
)

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Hello"}]
)

Important Note: As a transparent proxy service, GPT-Load completely preserves the native API formats and authentication methods of various AI services. You only need to replace the endpoint address and use the Proxy Key configured in the management interface for seamless migration.

Related Projects

New API - Excellent AI model aggregation management and distribution system

Contributing

Thanks to all the developers who have contributed to GPT-Load!

License

MIT License - see LICENSE file for details.

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The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 697

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k