Por fin entendí cómo estructurar un grafo de estados con LangGraph en vez de encadenar prompts a lo loco.
LangChain and LangGraph: Building Stateful AI Agents
Transition from basic AI chains to dynamic workflows by learning how to design and build stateful agent applications.
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About this course
As AI applications become more complex, simple prompt chains are no longer enough. Developers need ways to build intelligent agents that can remember past interactions, make decisions, and correct their own mistakes. This course guides you through the evolution of AI development, bridging the gap between traditional LangChain concepts and modern LangGraph architectures. You will learn how to transition from linear chains to dynamic, stateful agent graphs, enabling you to build applications that handle complex, multi-step reasoning. What you will learn: • Understand the core differences between LangChain and LangGraph architectures. • Learn foundational agentic AI concepts, including tool calling and dynamic routing. • Design stateful workflows that maintain memory across multiple conversational turns. • Apply modern retrieval-augmented generation (RAG) patterns within a graph structure. • Build and structure your own agent graph using written coding exercises. • Practice fundamental prompt engineering techniques tailored for autonomous agents. The journey begins with a clear breakdown of essential AI terminology and foundational concepts before moving into practical code implementations. You will systematically explore how to define state, create nodes, and route logic to form a complete agentic system. This course is designed for beginners and developers who are new to agentic AI, requiring no prior experience with complex AI frameworks. Start reading today to modernize your AI development skills and build smarter, stateful applications.
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Short & focused
2h 42m of practical content
Reviews (9)
Explicação excelente sobre grafos de estado.
शुरुआत में LangChain की बेसिक चेन्स से LangGraph के डायनामिक वर्कफ़्लो में जाना थोड़ा मुश्किल लगा, लेकिन कोर्स ने हर स्टेप को अच्छे उदाहरणों से समझाया। स्टेट मैनेजमेंट वाला हिस्सा सबसे उपयोगी लगा। बस कुछ जगह वीडियो थोड़े लंबे खिंच गए।
Переход от простых цепочек к полноценным агентам с состоянием объясняется логично, и наконец стало понятно, как LangGraph хранит и обновляет состояние между шагами. Примеры рабочие, я собрал свой воркфлоу почти сразу. Хотелось бы чуть больше про обработку ошибок в графах, но в целом материал очень толковый.
คอร์สนี้อธิบายการเปลี่ยนจาก chain ธรรมดาไปเป็น workflow แบบ dynamic ได้เข้าใจง่ายมาก ตัวอย่างการสร้าง state graph ช่วยให้เห็นภาพชัดเจนกว่าที่เคยอ่านจากเอกสารเอง ลองทำตามแล้วเอเจนต์ของเราเริ่มมีความจำระหว่างขั้นตอนได้จริง
Llevaba meses tratando de entender cómo pasar de simples cadenas de prompts a algo más parecido a un agente real con memoria y decisiones, y este curso lo dejó todo mucho más claro. La parte donde explican cómo diseñar los nodos y las transiciones del grafo fue justo lo que necesitaba para dejar de improvisar con mi código. También me gustó que no se quedan solo en la teoría, sino que van construyendo un agente paso a paso hasta que realmente responde según el estado anterior. El ritmo es bueno, ni muy lento ni atropellado. Terminé el curso con un proyecto funcionando que antes no sabía ni por dónde empezar.
Хороший переход от простых цепочек LangChain к более сложным графам состояний в LangGraph, хотя пара примеров кода была устаревшей.
Khóa học giúp mình hiểu rõ cách chuyển từ chain đơn giản sang workflow linh hoạt hơn với LangGraph. Phần thiết kế state và các node chuyển tiếp được giải thích khá chi tiết, dễ áp dụng vào dự án thực tế. Chỉ tiếc là một số ví dụ code hơi cũ so với phiên bản thư viện hiện tại.
स्टेटफुल एजेंट्स का कॉन्सेप्ट पहले उलझन भरा लगता था, पर अब LangGraph से डायनामिक वर्कफ़्लो बनाना बिल्कुल साफ़ हो गया।
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