Date of Submission

Spring 2024

Academic Program

Computer Science

Project Advisor 1

Rose Sloan

Abstract/Artist's Statement

This work presents LociGraph, an artificial intelligence agent that can autonomously search information on the non-public web, such as email inboxes, online communities, social media, or web applications not searchable on a public search engine. With a given query, the agent will browse the web using the keyboard and mouse to find a webpage containing the relevant information and extract the information in a structured format. For example, if the agent is given the query [Alex, studied at, ?] on your email inbox, the agent will start by typing “Alex” into the search bar, click on email related to Alex, read the content “Alex went to Bard College” and return [Alex, studied at, Bard College]. The framework consists of two parts: an agent pipeline, where a group of agents analyze the webpage content and suggest the next action, and a browser extension, where the user can enter the query and execute the suggested action. Preliminary evaluations show that Large Language Model (LLM) agents can navigate and extract information from real-world websites, but struggle to extract information from indirectly relevant content. All code, benchmarks, and results are available at https://github.com/ntcho/LociGraph.

Open Access Agreement

Open Access

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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