Pruner: Give AI coding agents the right context in one shot (github.com)

The newly announced Pruner tool revolutionizes how AI coding agents like Claude Code, Codex, and Copilot access and process codebases, achieving significant cost and time savings. By pre-indexing entire repositories through structural code analysis, Pruner allows agents to bypass lengthy navigational tasks, reducing tool calls by up to 86% and cutting costs by between 15-62% depending on the task type. This localized processing eliminates the need for LLMs, embeddings, and network calls, enabling agents to begin their work instantly with the right context. This innovation is crucial for the AI/ML community as it enhances the efficiency and speed of coding agents, making them more effective in both one-shot tasks and interactive sessions. The quick, deterministic tree-sitter parsing facilitates immediate access to relevant context while minimizing resource consumption. By decreasing the time agents spend exploring code, Pruner not only accelerates development processes but also makes AI coding more accessible and affordable for users, underscoring the potential for improved productivity in software development environments.