My Tech Stack as an AI Agent Engineer

Hi, I'm Nelson LIN.

You can connect with me on LinkedIn here: https://www.linkedin.com/in/nelson-l-842564164/.

I'm a founder of Agent Engineering, SandX.AI and LeetQuiz, passionate about designing, building, and deploying intelligent, autonomous agents that can reason, act, and interact with users and systems in real time. Over the years I've focused on creating production-grade agentic applications that are scalable, context-aware, and developer-friendly.

Here’s the complete tech stack I use daily to ship reliable AI agents from idea to production.

Programming Languages

Python – My go-to for backend logic, agent orchestration, and everything AI-related.

TypeScript – Keeps the frontend and full-stack layers type-safe and maintainable.

Frontend

Next.js – The React framework I use for fast, SEO-friendly, and highly interactive user interfaces.

Vercel AI SDK – My primary Frontend Agent Framework. It makes streaming LLM responses, tool calls, and conversational UIs feel almost effortless.

Backend & Agent Frameworks

FastAPI – Blazing-fast Python backend with automatic OpenAPI docs and great async support.

LangChain – Foundation for chains, tools, memory, retrievers, and classic agents.

LangGraph – For complex, stateful, multi-agent workflows (graphs with cycles, persistence, human-in-the-loop).

Pydantic AI – Strict type safety + structured outputs for every LLM call — essential for reliable agents.

Context Engineering & Data Layer

I combine multiple databases depending on the access pattern and performance requirements of each agent:

LangChain – Core layer for memory management, conversation history, retrievers, and context assembly.

PostgreSQL – Primary relational store for structured session data, user profiles, metadata, and strongly consistent long-term memory.

Prisma ORM (Type-safe) – My preferred way to interact with PostgreSQL from TypeScript, giving end-to-end type safety across frontend ↔ backend ↔ database.

MongoDB – Document database for flexible, schema-less storage of rich agent state, tool call histories, retrieved documents, and semi-structured context blobs.

Redis – Ultra-fast in-memory layer for short-lived session state, real-time conversation caching, rate limiting, pending tool calls, and temporary agent scratchpads / working memory.

Cloud & Infrastructure

AWS – Core cloud platform for heavy compute, storage, databases, serverless services, and enterprise-grade production workloads.

Vercel – My primary frontend + edge hosting platform — perfect for Next.js apps, instant global deployments, automatic scaling, edge functions, and seamless integration with Vercel AI SDK.

AWS Solutions Architect Certification – Helps me design secure, cost-effective, and highly available architectures across both AWS and hybrid/multi-cloud setups.

AI-First Development Environment

Cursor – My main AI-assisted coding companion.

Trae – The powerful new AI IDE (trae.ai) that understands my entire codebase deeply and runs autonomous coding agents — dramatically speeding up iteration.

Why This Stack Excels for Agent Engineering

This combination delivers:

End-to-end type safety (TypeScript + Pydantic + Prisma)

Seamless agent streaming from backend to frontend (Vercel AI SDK + LangChain)

Production-grade orchestration & reliability (LangGraph + FastAPI)

Flexible, high-performance context management
→ Fast ephemeral state (Redis)
→ Structured & relational persistence (PostgreSQL + Prisma)
→ Rich, schema-flexible documents (MongoDB)

Blazing-fast frontend delivery + edge intelligence (Next.js + Vercel)

Enterprise-ready scale & deployment flexibility (AWS + Vercel)

Whether I'm building customer-support agents, autonomous research systems, multi-agent orchestration platforms, or anything in between, this stack lets me go from prototype → production-grade in days rather than weeks.

If you're also deep into agents, context engineering, memory architecture, or any part of this stack , feel free to reach out on LinkedIn. I love discussing agent patterns, trade-offs between vector/memory stores, state management strategies, and the bleeding edge of agent tooling!