Course Overview

Modern generative AI development has moved far beyond simple chat completions and brittle shell scripts. Today's enterprise AI systems require resilient orchestration, type-safe structured data contracts, high-precision retrieval mechanisms, and cyclic multi-agent graphs capable of autonomous reasoning and human-in-the-loop validation.

Mastering LangChain & LangGraph: Production AI Agents & LLMOps is an intensive, developer-first curriculum engineered specifically for software engineers, backend architects, and AI developers who want to construct battle-tested production systems. Rather than relying on outdated legacy abstractions, this entire course is built from the ground up on the modern LangChain v0.3+ architecture, LangChain Expression Language (LCEL), LangGraph state graphs, and enterprise LLMOps observability via LangSmith.

What You Will Master

The Modern LCEL Protocol: Master declarative Runnable sequences, parallel routing, streaming backpressure, dynamic fallbacks, and multi-provider failover matrices.
Advanced Production RAG: Go beyond naive vector search with semantic chunking, PGVector HNSW indexes, Qdrant payload filters, multi-query expansion, and cross-encoder rerankers.
Tools & Autonomous ReAct Agents: Design strict Pydantic schemas, dynamic parameter injection, autonomous think-act-observe loops, and self-correcting error recovery handlers.
LangGraph Multi-Agent Systems: Architect cyclic state graphs, thread-safe checkpointing memory, human-in-the-loop approval gates, and hierarchical supervisor agent networks.
Production LLMOps & Observability: Instrument end-to-end tracing, latency profiling, golden evaluation datasets, and automated regression suites with LangSmith.

Course Structure

Section 1: LangChain & LCEL Fundamentals

Foundational architecture, declarative pipelines, multi-provider model switching, and deterministic Pydantic extraction.

Section 2: Retrieval-Augmented Generation & Memory

High-performance document ingestion, vector stores with PGVector and Qdrant, advanced query translation, and stateful memory management.

Section 3: Tools, Tool-Calling & ReAct Agents

Standardized tool interfaces, autonomous reasoning loops, human review breakpoints, and fault-tolerant execution fallbacks.

Section 4: LangGraph & Multi-Agent Orchestration

Cyclic state graphs, checkpointing persistence, multi-agent supervisor patterns, and complete enterprise observability with LangSmith.

Prerequisites

Familiarity with modern Python (type annotations, async/await) or TypeScript/Node.js, basic REST API concepts, and familiarity with Large Language Model fundamentals (tokens, temperature, system prompts).

Conversational Memory & State Management