Mastering LangChain & LangGraph: Production AI Agents & LLMOps
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 Rather than relying on outdated legacy abstractions, this entire course is built from the ground up on the modern LangChain v0.3+ architecture, LangChain Expression Language (LCEL), LangGraph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).
Course content
4 sections · 16 curated lessonsSection 1: LangChain & LCEL Fundamentals
Master modern LangChain architecture, declarative LCEL Runnables, prompt pipelines, multi-provider model gateways, and deterministic Pydantic structured extraction.
+
Section 1: LangChain & LCEL Fundamentals
Master modern LangChain architecture, declarative LCEL Runnables, prompt pipelines, multi-provider model gateways, and deterministic Pydantic structured extraction.
- Architecture & LCEL: The Declarative Runnable Protocol Preview35m
- Prompt Templates & Composable Pipelines30m
- Model I/O & Multi-Provider Architecture35m
- Structured Outputs with Pydantic & Type Safety40m
Section 2: Retrieval-Augmented Generation & Memory
Build production-grade RAG systems using semantic chunking, PGVector HNSW indexes, Qdrant vector databases, multi-query reranking, and persistent conversational memory.
+
Section 2: Retrieval-Augmented Generation & Memory
Build production-grade RAG systems using semantic chunking, PGVector HNSW indexes, Qdrant vector databases, multi-query reranking, and persistent conversational memory.
- Document Ingestion & Semantic Chunking Strategies40m
- Embeddings & Vector Stores with PGVector and Qdrant45m
- Advanced RAG: Multi-Query, Reranking & Context Compression45m
- Conversational Memory & State Management35m
Section 3: Tools, Tool-Calling & ReAct Agents
Construct resilient autonomous ReAct agents, dynamic tool schemas with Pydantic validation, human-in-the-loop review interrupts, and self-correcting error handling loops.
+
Section 3: Tools, Tool-Calling & ReAct Agents
Construct resilient autonomous ReAct agents, dynamic tool schemas with Pydantic validation, human-in-the-loop review interrupts, and self-correcting error handling loops.
- Defining Tools & Dynamic Pydantic Schemas Preview35m
- Building Autonomous ReAct Agents45m
- Human-in-the-Loop Approval & Interruption Workflows40m
- Tool Error Handling, Self-Correction & Fallbacks40m
Section 4: LangGraph & Multi-Agent Orchestration
Architect stateful cyclic graphs with LangGraph, thread-safe checkpointing persistence, collaborative multi-agent supervisor systems, and end-to-end LLMOps tracing with LangSmith.
+
Section 4: LangGraph & Multi-Agent Orchestration
Architect stateful cyclic graphs with LangGraph, thread-safe checkpointing persistence, collaborative multi-agent supervisor systems, and end-to-end LLMOps tracing with LangSmith.
- StateGraph Core: Cyclic Graphs, State & Reducers50m
- Stateful Persistence & Checkpointing Architecture45m
- Multi-Agent Collaboration: Supervisors & Hierarchical Teams50m
- Production LLMOps: Tracing & Evaluation with LangSmith45m