Magpie Search FAQs

Question 1

What information sources can Magpie Search query?

Accepted Answer

It can query six diverse sources: AI agent transcripts, local files, knowledge graphs, vector stores, and the live web. It employs multiple search modes, including literal/regex, lexical, semantic, hybrid, and cross-encoder reranking, optimized for each source.

Question 2

How does Magpie Search differ from traditional RAG (Retrieval-Augmented Generation)?

Accepted Answer

Magpie Search focuses purely on advanced 'Retrieval' (the 'R' in RAG). It finds and ranks truth without generating any natural language answers. It's designed to be the robust retrieval component that an AI agent or LLM can then use for its own generation (the 'G').

Question 3

How does Magpie Search optimize token costs for deep web research?

Accepted Answer

Magpie's `deepweb` feature conducts broad, multi-angle web research and page-reading entirely through retrieval (zero LLM tokens). Your AI agent only pays for a single synthesis pass over the trimmed, corroborated result set, saving up to 2,000x on tokens compared to multi-agent swarm approaches.

Question 4

What is Magpie Search?

Accepted Answer

Magpie Search is a federated, local-first search engine designed for AI agents and LLMs. It retrieves and fuses information from diverse sources like transcripts, files, knowledge graphs, vector stores, and the web into a single, trust-weighted result set.

Question 5

Is Magpie Search private and secure?

Accepted Answer

Yes, Magpie Search features a local-first architecture, meaning all indexing and processing happen on your machine. It includes proactive data redaction to strip sensitive information at ingest and has no telemetry by default, ensuring your data remains private.

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