Developers Stephan and Thomas open-sourced Semble, a code search tool designed for AI agents that dramatically reduces token consumption compared to traditional grep-based searches on large codebases.
Semble addresses a recurring problem in AI-assisted development: when agents like Claude Code can't locate code directly, they resort to grep, consuming substantial tokens while often missing relevant files.
Existing code search solutions fell short due to slow indexing, API key requirements, or poor retrieval accuracy. Semble combines efficient indexing with high-quality retrieval, enabling agents to find code faster and with minimal token overhead.
The tool achieves 98% fewer tokens than grep-based approaches, making it practical for real-time use during development workflows. By reducing token consumption, developers can maintain longer context windows for more complex tasks and lower API costs.
Semble is now available as open source, targeting developers working with large codebases and AI-powered coding assistants. The project directly addresses scaling challenges in agent-based development tools.
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