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Route coding tasks across local and remote LLMs with benchmarking and code search.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "LocalLama MCP Server" yet — see the docs or source repo.
Design a routing strategy that sends coding requests to local or remote models based on task complexity, cost, and latency, and provide example rules.
A clear model routing strategy with decision criteria, priorities, and example use cases.
Create a benchmark plan for local and remote LLMs on coding tasks, comparing code quality, latency, cost, and reliability, and list the evaluation metrics.
An executable benchmarking framework with test tasks, scoring criteria, and comparison dimensions.
Find code files related to user authentication, token refresh, and permission checks in the repository, and group them by function.
A categorized list of relevant code files or snippets for easier analysis and modification.
Run Llama models locally for private, offline AI assistance.
Delegate low-risk tasks to a cheaper model with main-agent review.
Search code and technical docs privately with local-first RAG for developers.
Delegate summarization, classification, extraction, and drafting tasks to a local LLM.
Securely let AI read, search, and edit local files with local LLMs.
Index local codebases for fast AI-powered cross-repository code search.