13 production microservices that prevent wasteful AI API calls through semantic search, caching, and team learning - 85% cost reduction
Smart Context Optimization for LLMs - Reduce tokens by 66%, save 40% on API costs. Intelligent ranking and selection of relevant context using embeddings, keywords, and semantic analysis.
GitHub Action that analyzes codebases and generates AI agent context documentation (CLAUDE.md/AGENTS.md) to optimize AI coding assistant efficiency. Reduces token waste and improves development velocity through intelligent recommendations.
CodeGrok MCP is a Model Context Protocol (MCP) server that enables AI assistants to intelligently search and understand codebases using semantic embeddings and Tree-sitter parsing.
Graph-style library for LLM agents: plan → fetch context → synthesize → verify.
Real-time MCP token tracking for Claude Code, Codex CLI, and Gemini CLI
🎯 Optimize LLM token usage by 70-90% with smart context ranking, reducing costs while maintaining quality and performance.
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