A local-first way to search project files from AI agents

Evan Brooks — Tue, 01 Sep 2026 04:05:21 +0000

Most AI agents are good at reasoning, but they still struggle when the context they need is scattered across local PDFs, notes, documents, and project folders. Uploading everything to a cloud knowledge base creates another system to maintain and can be a poor fit for private material.

I have been working on Linkly AI, a local-first document search layer designed for this workflow. It parses files on the user's machine, keeps a searchable private knowledge layer, and exposes built-in tools that an AI agent can use to search, compare, outline, and read the source material.

The MCP angle is especially useful: instead of manually selecting and uploading documents for every conversation, an agent can call the relevant search and reading tools when it needs evidence. Typical use cases include:

finding decisions across meeting notes and project documents;
comparing requirements across several files;
locating the source passage behind an answer;
giving coding or research agents access to a private local corpus.

The product is available at Linkly AI. The goal is not to replace an agent's reasoning, but to give it a dependable way to work with the files you already have.

I would be interested in feedback from people building agent workflows: which local file types and retrieval actions matter most in your day-to-day work?

PromptZone - Leading AI Community for Prompt Engineering and AI Enthusiasts: Evan Brooks

A local-first way to search project files from AI agents