LocaLLama MCP Server
by Heratiki
LocaLLama MCP Server optimizes costs by intelligently routing coding tasks between local LLMs and paid APIs. It dynamically decides whether to offload tasks to local LLMs versus using paid APIs to reduce token usage and costs.
Last updated: N/A
What is LocaLLama MCP Server?
LocaLLama MCP Server is a tool designed to reduce token usage and costs associated with coding tasks by dynamically routing them between local, less capable instruct LLMs (e.g., LM Studio, Ollama) and paid APIs based on cost and quality considerations.
How to use LocaLLama MCP Server?
- Clone the repository. 2. Install dependencies using
npm install. 3. Build the project withnpm run build. 4. Configure the.envfile with your local LLM endpoints, API keys, and thresholds. 5. Start the server usingnpm start. 6. Integrate with tools like Cline.Bot by adding the server to your MCP settings.
Key features of LocaLLama MCP Server
Cost & Token Monitoring Module
Decision Engine with configurable thresholds
API Integration & Configurability (LM Studio, Ollama, OpenRouter)
Fallback & Error Handling
Benchmarking System for comparing local and paid models
Use cases of LocaLLama MCP Server
Reducing costs for coding tasks by utilizing local LLMs when appropriate.
Dynamically routing tasks based on cost, quality, and token usage.
Benchmarking the performance of different LLMs to inform routing decisions.
Integrating with tools like Cline.Bot and Roo Code for automated task routing.
FAQ from LocaLLama MCP Server
What is the purpose of the Cost & Token Monitoring Module?
What is the purpose of the Cost & Token Monitoring Module?
It queries the current API service for context usage, cumulative costs, API token prices, and available credits to inform the decision engine.
How does the Decision Engine work?
How does the Decision Engine work?
It defines rules that compare the cost of using the paid API against the cost (and potential quality trade-offs) of offloading to a local LLM, using configurable thresholds.
What local LLMs are supported?
What local LLMs are supported?
The server supports integration with LM Studio and Ollama, using standardized API calls.
How does the OpenRouter integration work?
How does the OpenRouter integration work?
It allows access to free and paid models from various providers, automatically retrieving and tracking free models and maintaining a local cache.
Where are benchmark results stored?
Where are benchmark results stored?
Benchmark results are stored in the benchmark-results directory and include individual task performance metrics, summary reports, and comprehensive analysis of model performance.