Introducing Filesystem Search for Mastra Workspaces

You can now search stored content with filesystem search. Index files from disk, databases, or APIs. Query by keyword, semantic-similarity, or both.

Knowledge is stored everywhere, usually across different and unconnected services. Mastra workspaces let you unify data sources and create searchable indexes that agents can use to find relevant information and answer user queries more accurately.

Before filesystem search, finding content meant reading every file, running grep for literal matches, or standing up a RAG pipeline. Now, RAG is part of the workspace setup and can work alongside BM25 keyword search.

BM25 is a keyword-ranking algorithm that scores documents by term frequency and length. Indexes are stored in-memory and don’t require external network calls.

Vector search is a semantic-similarity approach that matches documents by their content. Indexes are stored in an external database and require network calls to embed and retrieve results.