Building an Article Research Agent with Mastra & Upstash

In the rapidly evolving field of academic research, keeping up with new papers can be overwhelming. The guide outlines the creation of an AI research assistant using Mastra, an open-source TypeScript framework, and Upstash for memory and vector storage, designed to assist researchers by understanding natural-language questions, finding relevant papers in a vector database of arXiv abstracts, summarizing key insights, and providing direct PDF links for deeper reading. The project utilizes a tech stack comprising Mastra for building AI agents, Upstash Redis for conversation memory, Upstash Vector for storing article embeddings, and Next.js with Vercel for deploying the web application, while also implementing Upstash Ratelimit to manage request loads. The implementation process involves setting up a Mastra server and a web application, configuring agents and tools, enabling agent memory via Upstash Redis, and embedding research articles from arXiv into Upstash Vector for efficient querying. The AI assistant is deployed on Vercel and can be explored through the Mastra Playground, with scheduled updates to the article database managed via Upstash QStash to ensure data freshness.

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