What it does
A data and document-agent framework for connecting LLM apps to files, structured data, retrieval systems, and agent workflows.
Why it belongs here
Good retrieval is tokenmaxxing in disguise: send the model the useful context, not a suitcase full of maybe-relevant text.
Best use case
Applications that need to ground prompts in documents, databases, search results, or tool-accessible knowledge instead of giant static context.
How to use it
Build retrieval pipelines that select narrow context for each task, then measure answer quality and token usage before and after the change.
Limits
Retrieval quality depends on chunking, metadata, ranking, and evaluation. Bad retrieval can simply make prompts smaller and worse.
Tags
ragagentscontext
