locallama-mcp
LocaLLama MCP Server is designed to optimize coding task efficiency by dynamically routing them between local and paid AI APIs. It features cost and token monitoring, a decision engine, and a configurable API interface. The server supports integration with various models and includes tools for robust error handling and performance benchmarking.
What is the primary purpose of the LocalLama MCP Server?
The primary purpose is to optimize costs by intelligently routing coding tasks between local LLMs and paid APIs, reducing token usage and expenses.
How does the Decision Engine work?
The Decision Engine defines rules to compare the cost and quality trade-offs between using local LLMs and paid APIs, with configurable thresholds for task offloading.
What happens if the paid API is unavailable?
The server implements fallback mechanisms to handle such scenarios, ensuring reliable operation even if the paid API is unavailable.
Can I configure which local models to use?
Yes, the server provides a configuration interface to specify endpoints for local instances and select which models to use for different tasks.
How can I benchmark the performance of models?
You can use the benchmarking system included in the server to compare local LLM models against paid API models, measuring various performance metrics.