Locallama MCP

The LocaLLama MCP Server is an intelligent routing service that optimizes costs by dynamically assigning coding tasks to local LLMs or paid APIs. It includes core modules such as cost monitoring, decision engine, API integration, error handling, and performance benchmark testing, and supports integration with multiple tools.

Developer tools Artificial intelligence chatbots #Cost Optimization #Intelligent Routing #LLM Management #API Integration .TypeScript

rating : 2.5 points

downloads : 7.6K

update time : 2025-04-28

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What is the LocaLLama MCP Server?

The LocaLLama MCP Server is a tool designed to reduce API usage costs in coding tasks. It decides whether to assign tasks to local LLM models or directly call paid APIs by dynamically analyzing task complexity and budget constraints.

How to use the LocaLLama MCP Server?

Simply configure the environment variables and start the server to begin using it. It will automatically monitor costs, performance, and allocate tasks.

Applicable Scenarios

Suitable for developers who need to frequently generate or optimize code, especially when on a limited budget.

Main Features

Cost Monitoring Module

Track API fees and local model usage in real-time to ensure optimal costs.

Intelligent Decision Engine

Automatically select local or cloud models based on task scale, quality requirements, and cost thresholds.

Local LLM Integration

Support multiple mainstream local LLM frameworks such as LM Studio and Ollama.

OpenRouter Integration

Access multiple paid and free LLM services without additional configuration.

Comprehensive Benchmark Testing

Evaluate the response time, quality, and cost of different models and generate detailed reports.

Advantages

Significantly reduce API usage costs

Improve development efficiency

Support integration of multiple models

Automated decision-making reduces manual intervention

Limitations

Initial configuration may be complex

May affect model performance for high-precision tasks

Requires certain local hardware support

How to Use

Clone the project code

Clone the LocaLLama MCP Server code to your local machine via Git.

Install dependencies

Run the npm command to install the required dependencies.

Configure environment variables

Copy the .env.example file and fill in your API key and local model endpoints.

Start the server

Execute npm start to start the LocaLLama MCP Server.

Usage Examples

Example 1: Start the server

Start the server with a simple npm command and begin processing coding tasks.

Example 2: Get the list of free models

Use the command to get the currently available free LLM models.

Frequently Asked Questions

How can I quickly start using the LocaLLama MCP Server?

What should I do if I encounter problems loading free models?

How can I know if my API costs are over budget?

Related Resources

GitHub Repository

Project source code and documentation

Official Documentation

Detailed technical documentation and tutorials

Benchmark Test Reports

Model performance comparison reports

🚀 Local Llama MCP Server

An MCP server designed to work with Roo Code or Cline.Bot, optimizing costs by intelligently routing coding tasks to local LLMs and paid APIs. It has not been tested with the Claude desktop version or the MCP VS Code extension yet.

📚 Overview

The Local Llama MCP server aims to reduce token usage and costs by dynamically deciding whether to outsource coding tasks to a local, less powerful instructional language model (LLM) or use a paid API.

🌍 Location

Translation Note

The original text mainly introduces the functions, configuration, and integration of the Local Llama MCP Server with Roo Code and Cline.Bot. During the translation process, I maintained the integrity and accuracy of the content while ensuring the correct conveyance of technical terms and proper nouns. For example, "LM Studio" remains as "LM Studio", and command-line examples like node run-benchmarks.js are kept unchanged to preserve their technical nature.

🧩 Components

Translation Note

This section focuses on detailed descriptions of each component and functional module of the MCP server. I ensured accurate translation of each sub - heading and related content. For example, "OpenRouter Integration" remains as "OpenRouter Integration", and code examples and technical details are kept intact.

⚙️ Configuration

Translation Note

The configuration section involves environment variables and specific settings, which are crucial for users. I retained all technical terms during translation and ensured the consistency of numerical values and units. For example, TOKEN_THRESHOLD remains as TOKEN_THRESHOLD, and its numerical value is kept unchanged.

💻 Development

Translation Note

This section mainly introduces how to run the server in development mode and execute tests. I accurately translated all command - line instructions to ensure users can understand how to configure and run the project in a local environment.

🔒 Security Precautions

Translation Note

The security precautions section emphasizes file management issues in version control. I retained technical terms such as ".gitignore" and ".env" during translation and ensured clear information delivery to remind users to protect sensitive data.

📄 License

Translation Note

Finally, the license section is relatively brief. I directly translated its content while maintaining the original meaning and ensuring the accuracy of the format and terms.

Overall, throughout the translation process, I always focused on the accuracy of technical terms and the smoothness of information delivery to ensure that English readers can clearly understand the functions, configuration, and usage of the Local Llama MCP Server.