ratel: Ratel is a context engineering layer for AI agents that optimizes tool usage by selecting only relevant tools for each task. It aims to reduce token costs and improve accuracy for LLMs by preventing 'tool overload' without relying on vector databases or embeddings.; agentic-cursorrules: This tool helps AI agents navigate large codebases by partitioning them into domain-specific contexts. It generates isolated markdown files that define explicit file-tree boundaries, preventing agents from interfering with unrelated modules and improving workflow focus.
Developing AI agents in Python or TypeScript that efficiently manage external tools
Managing multi-agent AI workflows on large codebases