iterative-retrieval

Iterative context retrieval progressively searches and evaluates files to build relevant context, preventing subagents from missing information or exceeding token limits during multi-agent workflows.

Progressive context refinement applies iterative searching, relevance scoring, and keyword refinement based on evaluated content to dynamically gather the most relevant files for problem-solving.

Subagents lack precise context because initial needs are unpredictable. Iterative retrieval solves this by progressively evaluating files and refining search patterns to gather exact requirements.

Yes, iterative retrieval intelligently identifies and gathers relevant files by refining keywords and patterns, even when exact file names or directory patterns are not initially known.

You need progressive context refinement for RAG-like pipelines when handling complex tasks like codebase exploration where initial context requirements are unknown and must be dynamically evaluated.

Iterative retrieval prevents exceeding token limits by progressively searching and evaluating only the most relevant files, ensuring subagents receive precise context without overwhelming them.