This guide explains how to build a serverless AI agent using Langbase to automate the process of generating personalized cold emails for job applications. It details how to integrate memory agents into a Node.js project to enhance LLMs with long-term memory, making AI applications smarter and more capable. The tutorial covers step-by-step instructions from setting up the project to generating and using memory embeddings, ensuring the AI agent can produce context-aware responses based on the user's résumé and job descriptions.
Table of contents
Large Language Models (LLMs) Are Stateless by NatureWhat Are Memory Agents?PrerequisitesReference ArchitectureStep 1: Create a Directory and Initialize npmStep 2: Create a Serverless Pipe AgentStep 3: Add a .env FileStep 4: Create a Serverless Memory AgentStep 5: Add Documents to the Memory AgentStep 6: Generate Memory EmbeddingsStep 7: Integrate Memory in Pipe AgentStep 8: Integrate the Memory Agent in Node.jsStep 9: Start the BaseAI ServerStep 10: Run the Memory AgentThe ResultSort: