Which tool collects context best for an AI agent? Comparing 21 approaches from ripgrep to RAG and LSP

▤ Summary

This article compares 21 approaches for collecting context for AI agents, from simple grep-based methods to AST, LSP, and RAG workflows. It tests the approaches across different tasks and measures performance using metrics such as task success, correctness, minimality, and context quality. The results highlight how different tool choices affect context waste, token usage, and overall agent efficiency. The article also shows that specialized retrieval and symbol-aware tools can outperform broader repository-reading approaches in some scenarios. It is useful for teams building coding agents or software development tooling because it directly evaluates the tradeoffs between search, semantic retrieval, and structured code analysis.

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