Breaking

Hands-on course for building production-grade LLM agents with LangGraph published

July 7, 2026 at 23:13 EDT

AI Agents
Software Dev & Coding
Open Source

A hands-on tutorial for building "production-grade" LLM agents with LangGraph—featuring Agentic RAG, ReAct, and reflection workflows—is drawing attention after being shared on GitHub. The material in question is the langgraph-course repository published by Eden Marco (emarco177), offered as a companion to a paid Udemy course.

July 2026 · langgraph-course

Building Production-Grade LLM Agents with LangGraph

A hands-on, open-source repository teaching real-world agent development — Agentic RAG, ReAct, and Reflection workflows modeled as stateful graphs with cycles.

609

GitHub stars

297

Forks

Steps in Agentic RAG track

Apache-2.0

Open-source license

Community traction: stars vs forks

Roughly one fork for every two stars — a strong signal of hands-on reuse.

609

Stars

297

Forks

Three agent patterns, one branch each

Agentic RAG

Document grading, Tavily web search, adaptive routing & Self-RAG. The LLM judges retrieval need and quality itself.

ReAct

A classic agent interleaving reasoning and tool execution on LangGraph.

Reflection

Reflection / Reflexion workflows for self-correction across multiple branches.

The Agentic RAG loop

Retrieve → Grade documents → Route / Web search → Self-critique

Modeled as a stateful graph with cycles — enabling self-correction, human-in-the-loop, and persistence beyond plain sequential chains.

Valued for

Production-readiness focus with LangSmith tracing
Step-by-step, hands-on structure
Practical next step after LangChain basics

Noted caveats

Beginners may prefer LangChain Academy / DeepLearning.AI
Feature requests such as a SQL Agent
Tool integration matters in real deployments

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