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Ask and You Shall Retrieve

One-line RAG for your agent.
No vector database. No embedding pipeline. No indexing.
Paste this line to your agent and ask questions.
Retrievr provides relevant information for your agent to work with.

Fetch https://retrievr.io/install and follow it.

Works with the agent you already have:

Claude Code
Codex
Cursor
pi
opencode
Kiro
Hermes
and others

USAGE

WHAT YOUR AGENT ASKED

WHAT CAME BACK FROM RETRIEVR

The Divine Comedy

Alighieri, Dante

INFERNO > CANTO II.

“

YOUR AGENTS ANSWER USING THIS INFORMATION

BENEFITS

Lowered token cost: Your agent gets the passages that answer the question, not the document they came from. The same answer costs a fraction of the tokens it would have taken to find it.
Document aware RAG: The stack fits the document. The embedding model and the index, the chunk size, how each file type is read and which language the text is in — everything is optimized for the specific document.
Reduced hallucination: Every passage arrives with its source, author, section, and character range so the agent quotes what it found instead of reconstructing it from memory. A made-up answer becomes visible instead of plausible.
Explainable AI: Every answer is a short list of passages you can open: what was retrieved, from which document, at which character range, and in what order. When an answer is wrong you can see whether the search missed or the model misread, instead of guessing at a black box.