Jeffrey Emanuel Launches 'cass' Tool, Offering Unified Search for AI Coding Agent Sessions in Under a Week

Jeffrey Emanuel, known online as @doodlestein, has introduced "cass" (coding agent session search), a new utility designed to centralize and expedite the search of session histories across diverse AI coding agent platforms. Developed in less than a week, this tool addresses a significant pain point for developers managing numerous coding agent interactions from tools like Claude Code, Codex, Cursor, and Gemini-cli. Emanuel stated, "This tool solves a direct pain point I’ve been experiencing for months as a heavy user of coding agents."

The primary motivation behind "cass" was the difficulty in recalling and locating specific past conversations within a multitude of agent sessions and projects. Emanuel sought an instantly available terminal-based solution that could perform rich, low-latency "search as you type" filtering and ranking across all installed coding tools without requiring extensive configuration. The tool is designed to automatically detect and integrate with various agents, including future ones like opencode and aider.

Built using the Rust programming language, "cass" prioritizes high performance, optimization, and user-friendly ergonomics. Emanuel expressed satisfaction with the tool's development, highlighting the meticulous attention paid to its design and functionality. This robust technical foundation aims to provide a seamless experience for both human developers and the AI agents themselves.

A key innovation of "cass" is its dedicated "robot mode," specifically engineered for use by AI coding agents. This feature enables agents to access and search their own operational notes and those of their peers across different platforms, effectively creating a shared, searchable knowledge base. Emanuel emphasized that he "went through countless iterations of improving the tool so that agents really love to use it."

The launch of "cass" serves as a tangible demonstration of results from Emanuel's recent AI agent workflows, directly responding to criticisms about the practical output of complex agent-based development. The tool indexes conversations from a wide array of agents into a unified, searchable index, fostering cross-agent knowledge sharing and enhancing problem-solving efficiency. Further details and installation instructions are available via its GitHub repository.