ASTral

AI agents often incur high costs and latency by indiscriminately processing entire code files to find specific information. ASTral solves this by indexing code repositories once using tree-sitter, allowing agents to precisely query and retrieve only the necessary code symbols—like functions, classes, or methods—with byte-level accuracy. This structured retrieval approach drastically reduces token consumption, leading to significant cost savings and improved agent efficiency, transforming code exploration into targeted navigation rather than brute-force scanning.

Key Features

Use Cases