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Scalekit

Scalekit

ScaleKit Auth

Scalekit

ScaleKit Auth

BShared byByteMonk
From video: Stop Confusing LangChain, LangGraph, and LangSmith | Full Breakdown
Published: March 23, 2026

Video Description

One thing worth calling out separately: once your agent starts acting inside real systems: booking meetings, updating CRMs, filing tickets – you hit a wall that LangChain, LangGraph, and LangSmith don't solve on their own: auth. Especially, in multi-tenant enterprise systems, you need answers for: Whose credentials does the agent use? What is the agent actually allowed to touch? And how do you prove a specific user authorized an agent? Scalekit is a drop-in auth and tool-calling layer built for exactly this. Try it free: https://tinyurl.com/5h9vssuv Resources: - System Design Course: https://academy.bytemonk.io/courses - ByteMonk Blog: https://blog.bytemonk.io/ - LinkedIn: https://www.linkedin.com/in/bytemonk/ - Github: https://github.com/bytemonk-academy Timestamps 00:00 Why Everyone Is Talking About LangChain, LangGraph & LangSmith 00:25 The Problem: LLM Apps Are More Than API Calls 00:49 From Single Prompt to AI Pipelines 01:36 What LangChain Solves (Core Idea) 01:54 Prompt Templates Explained 02:21 Chains: Building Multi-Step Workflows 02:44 Tools: Giving LLMs Real Actions 03:20 Retrieval Augmented Generation (RAG) Basics 03:54 The OAuth & Authentication Problem in Agents 05:05 Tool Authentication Layer (ScaleKit Overview) 05:52 From Chains to Agents (Reason + Act Loop) 06:33 Why Agent Workflows Become Complex 06:52 Why LangGraph Exists 07:03 Graph-Based AI Architecture (Nodes & Edges) 07:25 Research Agent Example (Loops & Decisions) 07:54 Stateful AI Systems Explained 08:22 Debugging Problem in AI Systems 08:52 LangSmith: Observability for AI Applications 09:16 Evaluation & Measuring AI Quality 09:43 Prompt Experiments and A/B Testing 09:58 Production Monitoring & Metrics 10:07 End-to-End Architecture (How All Three Work Together) 10:34 LangChain vs LangGraph vs LangSmith Summary 10:50 Limitations & Criticism of LangChain 11:22 Why LangGraph Was Created 11:36 When You Actually Need This Stack 11:58 The Shift: From Prompting to System Design 12:20 Final Takeaways & What to Learn Next https://www.youtube.com/playlist?list=PLJq-63ZRPdBt423WbyAD1YZO0Ljo1pzvY https://www.youtube.com/playlist?list=PLJq-63ZRPdBssWTtcUlbngD_O5HaxXu6k https://www.youtube.com/playlist?list=PLJq-63ZRPdBu38EjXRXzyPat3sYMHbIWU https://www.youtube.com/playlist?list=PLJq-63ZRPdBuo5zjv9bPNLIks4tfd0Pui https://www.youtube.com/playlist?list=PLJq-63ZRPdBsPWE24vdpmgeRFMRQyjvvj https://www.youtube.com/playlist?list=PLJq-63ZRPdBslxJd-ZT12BNBDqGZgFo58 #LangChain #aiarchitecture #bytemonk