Building an AI Agent with Long-Term Memory: ChromaDB + Ollama + TypeScript
Last Updated on February 17, 2026 by Editorial Team
Author(s): Jageen Shukla
Originally published on Towards AI.
How I Built a Customer Support Agent That Actually Remembers What You Said
I built a prototype AI customer support agent with semantic long-term memory using ChromaDB (vector database), Ollama (local LLM), and TypeScript. The agent remembers conversations across sessions, understands context semantically, and costs $0 to run. This is an MVP demonstrating the architecture full source code included. You can read full blog free using this link.
The article discusses the development of a prototype AI customer support agent that effectively utilizes semantic long-term memory for enhanced user interaction. It outlines the challenges faced by traditional AI systems that often forget past interactions and introduces ChromaDB, Ollama, and TypeScript as key technologies in building an agent capable of retaining context and information across sessions. The author explores the architecture of the agent, its functionalities, and the importance of memory management to enhance user experience, while providing insights on implementation and potential applications in various fields beyond customer support.
Read the full blog for free on Medium.
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