MCP server that delegates simple Claude Code / Codex / Wibey tasks to a local LLM via langchain4j and langgraph4j.
An MCP server that lets Claude Code / Codex / Wibey (or any MCP-compliant coding agent) delegate simple, well-scoped tasks to your local LLM — code snippets, isolated refactors, documentation, code reviews, basic Q&A — so you spend premium tokens on coordination and architecture, not on work a local model can already do.
This is a complete rewrite in Java on langchain4j (local LLM client), langgraph4j (orchestration), and the official MCP Java SDK (stdio server).
Credit for the original idea and the Python/FastMCP prototype goes to csabakecskemeti/cctokensavermcp — this project builds on that concept with a graph-based classify/delegate/escalate flow, server-side file reading, timeouts, and actionable errors.
The server exposes two MCP tools — querylocalllm and querylocalllmwithcontext — that Claude Code is instructed to try first for simple subtasks. Every call runs through a small langgraph4j graph instead of just forwarding straight to the model:
classify makes a cheap, low-temperature call to the local model to judge whether the task is actually simple enough for it. If not, escalate returns a structured ESCALATE: ... message telling Claude Code to handle the task itself — so a task that's too complex for the local model comes back as a clear signal, not a low-quality answer.
- Server-side file reading. Both tools accept an optional filepaths array. The server reads the files itself and passes their content straight to the local LLM — Claude Code passes a path instead of Read()-ing the file into its own context first. This is where the real token savings on large f
From the project README.
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