Dicklesworthstone Llm Gateway MCP Server

🚀 LLM Gateway

LLM Gateway is an open-source tool built on the Model Context Protocol (MCP), aiming to simplify the deployment and management of Large Language Model (LLM) services. It allows developers to call services from multiple LLM providers through a unified interface, including but not limited to OpenAI, Anthropic, etc.

🚀 Quick Start

LLM Gateway is an open - source tool built on the Model Context Protocol (MCP). Its main goal is to simplify the deployment and management of Large Language Model (LLM) services. Developers can use a unified interface to call services from various LLM providers such as OpenAI and Anthropic.

✨ Features

🔄 Unified API Interface

LLM Gateway offers a standardized HTTP API that supports JSON - formatted requests and responses. This enables developers to switch between different LLM services easily without modifying their code.

🧩 Multi - Model Support

It has built - in support for multiple mainstream LLM providers and can be extended to support more. Supported providers include:

• OpenAI - Supports GPT series models (GPT - 3, GPT - 3.5, GPT - 4, etc.)
• Anthropic - Supports Claude models
• Other providers to be supported soon

⚖️ Load Balancing and Routing

LLM Gateway provides intelligent load - balancing strategies. It can dynamically distribute requests to the optimal LLM provider based on the current request volume and model performance, ensuring high availability and fast response times.

📦 Batch Processing and Parallel Execution

It supports batch processing of multiple requests and can automatically perform parallel processing internally, improving overall efficiency. This is very useful for applications that need to handle a large number of requests simultaneously.

📂 Document Management

It offers document storage and management functions. It supports storing text content in a structured way and allows users to quickly retrieve the required information through search and filtering.

📊 Performance Monitoring and Analysis

It has a complete performance monitoring system that can track key metrics such as the response time and error rate of each model in real - time. It also provides logging and analysis functions to help developers troubleshoot problems and optimize performance.

📦 Installation

💨 Quick Start

1. Clone the project repository:

git clone https://github.com/your-organization/llm-gateway.git
cd llm-gateway

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment variables:

export LLM_GATEWAY_API_KEY=your_api_key_here

4. Start the service:

python -m llm_gateway.server

⚙️ Detailed Configuration

You can perform detailed configuration in the config.py file, including:

• Server settings: Specify the listening address and port.
• Model provider configuration: Add or modify supported LLM providers and their API credentials.
• Load - balancing strategy: Define the rules for request distribution.
• Logging and monitoring: Configure the log output format, monitoring endpoints, etc.

💻 Usage Examples

📨 Sending a Single Request

curl -X POST "http://localhost:8000/v1/chat/completions" \
     -H "Content-Type: application/json" \
     -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "你好，你是谁？"}]}'

📦 Batch Processing Requests

curl -X POST "http://localhost:8000/v1/batch/completions" \
     -H "Content-Type: application/json" \
     -d '{"requests": [{"model": "anthropic-claudius-2", "messages": [{"role": "user", "content": "你好，你是谁？"}]}, {"model": "gpt-4", "messages": [{"role": "user", "content": "今天天气如何？"}]}]}'

🌟 Advanced Features

🥇 Model Competition

LLM Gateway provides a model competition framework that allows developers to define scoring criteria and evaluation metrics and automatically run competitions among registered LLM providers. The results can be viewed through the Web interface.

🔒 Security Audit

It has multiple built - in security mechanisms:

• Input filtering: Prevents malicious request injection attacks.
• Rate limiting: Prevents API abuse.
• Access control: Supports permission management based on IP whitelists and JWT verification.

🧩 Extension Plugin System

Through the plugin mechanism, users can extend the functions of LLM Gateway. Currently, there are several community - contributed plugins, including:

• Data pre - processing plugins
• Custom model evaluators
• Log enhancement modules

📋 Use Cases

🏢 Enterprise Internal AI Platform

Enterprises can create their own AI service platforms by deploying LLM Gateway, integrating internal data and tools, and improving development efficiency.

📚 Research and Experimentation

Researchers can use LLM Gateway for comparative analysis and performance testing of different models, promoting the development of AI technology.

🌐 Open - Source Community Contribution

Developers can easily contribute code or documentation to LLM Gateway and jointly maintain and develop this open - source project.

💡 Best Practices

1. Configuration management: Use environment variables or configuration files to centrally manage API keys and server settings.
2. Monitoring logs: Monitor the service running status and performance metrics in real - time to detect and solve problems promptly.
3. Security audit: Conduct regular security reviews to ensure the security of the API and prevent unauthorized access.

🚀 Future Plans

• Support more LLM providers
• Enhance model management functions
• Launch a Web interface
• Optimize the performance monitoring system

LLM Gateway is a powerful and flexible tool that can help developers utilize the capabilities of large language models more efficiently. It provides great convenience for both personal projects and enterprise applications.