Build Serverless AI Agents with Langbase

Why It Matters

LangBase’s serverless, memory‑enabled architecture lets businesses launch production‑grade AI agents quickly and at scale, cutting infrastructure costs and accelerating time‑to‑value for AI‑driven products.

Summary

The video introduces a hands‑on course on building serverless AI agents using LangBase, a cloud platform that abstracts away infrastructure and lets developers focus on AI logic. Instructor Maham Koth explains that LangBase is not a traditional framework but a primitives‑based, serverless AI cloud that supports memory‑enabled agents, agentic Retrieval‑Augmented Generation (RAG), and one‑click deployment. The curriculum assumes familiarity with JavaScript/TypeScript and guides learners through setting up environment variables, creating a memory store, uploading documents, and wiring the memory to a pipe‑based agent that can answer queries with context‑aware precision.

Key technical insights include the distinction between “pipes” (serverless API endpoints that execute agent logic) and “memory agents” (long‑term semantic stores that handle terabytes of data without manual vector‑store management). The course demonstrates how to instantiate a LangBase client via its SDK, call langbase.memories.create to provision a knowledge base, and use OpenAI’s text‑embedding‑3‑large model for embedding generation. Subsequent lessons cover reading local files, uploading them to the memory, and constructing a retrieval step that feeds relevant chunks into the agentic pipe for generation, illustrating a full end‑to‑end agentic RAG workflow.

Throughout the tutorial, Maham highlights practical shortcuts such as the command.new feature that scaffolds boilerplate code, and emphasizes the platform’s ability to scale from hobby projects to production without code changes. Real‑world examples—like a “chat‑with‑PDF” assistant—show how the memory‑pipe pattern can be applied to document‑centric use cases. The instructor also provides guidance on managing API keys via Scrimba’s environment system and points learners to complementary courses on JavaScript, AI engineering, and RAG fundamentals.

The broader implication is that LangBase lowers the barrier to deploying autonomous, context‑aware AI services, potentially accelerating adoption of agentic AI in enterprises that lack deep DevOps resources. By offering a serverless, primitives‑first approach, the platform promises faster iteration cycles, reduced operational overhead, and seamless scaling, positioning it as a strategic tool for companies looking to embed AI agents into products, internal tools, or customer‑facing applications.

Original Description

Learn to build AI agents with Langbase, one of the most powerful serverless AI clouds. This hands-on course will teach you how to create context-engineered agents that use memory and AI primitives to take action and deliver accurate, production-ready results using Langbase. Maham from Scrimba developed this course.

✏️ Study this course interactively on Scrimba:

https://scrimba.com/build-serverless-ai-agents-with-langbase-c0cg73hgmh?utm_source=youtube&utm_medium=video&utm_campaign=fcc-langbase

Scrimba on YouTube: https://www.youtube.com/c/Scrimba

⭐️ Contents ⭐️

- 00:00 Intro to Building Serverless AI Agents

- 03:57 Core Concepts

- 08:11 Setting Up Environment Variables in Scrimba

- 10:00 Create a Memory with Langbase SDK

- 15:07 Upload Documents to AI Memory

- 21:43 Memory Agents on Langbase

- 25:32 Perform RAG Retrieval

- 30:48 Create an AI Agent Pipe

- 35:14 Generate RAG Responses

- 40:43 AI Primitives for Context Engineering

- 44:23 Vibe Coding AI Agents with Command.new

🎉 Thanks to our Champion and Sponsor supporters:

👾 Drake Milly

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👾 David MG

👾 Matthew Springman

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👾 jedi-or-sith

👾 Nattira Maneerat

👾 Justin Hual

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