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Free Download Advanced Langgraph Workflows, Multi-Agents, Deep Agents
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 10.07 GB | Duration: 13h 12m
Practical LangGraph: Build Real-World Agents and Workflows with Memory, Tools and Deep thinking
What you'll learn
Master LangGraph Fundamentals: Build a solid foundation in state, nodes, and edges to transition from linear chains to cyclic, agentic workflows.
Design Advanced Workflows: Architect sophisticated state machine patterns to handle complex business logic and non-linear AI reasoning paths.
Implement Short-Term Memory: Utilize thread-level persistence to maintain context and state within a single user session or conversation.
Manage Long-Term Persistence: Enable agents to recall information across multiple sessions by integrating robust, long-term memory storage solutions.
Orchestrate Multi-Agent Systems: Build collaborative ecosystems where specialized agents interact via sub-graphs to solve high-level enterprise tasks.
Engineer Deep Agents: Develop recursive, long-horizon agents capable of autonomous planning, self-correction, and complex task execution.
Requirements
Intermediate Python Proficiency: You must be comfortable with Type Annotations, TypedDicts, and asynchronous programming in Python.
LangChain Fundamentals: Pre-existing knowledge of LLMs, Prompt Templates, and the LangChain Expression Language (LCEL) is essential.
Basic Agent Understanding: Familiarity with the basic "ReAct" agent pattern and how LLM tool-calling works will be necessary to follow the advanced modules.
Environment Setup: A local development environment with Python 3.10+ installed and the ability to manage API keys for providers like OpenAI or Anthropic.
Description
Welcome to Advanced LangGraph: Workflows, Multi-Agents, Deep Agents, the definitive guide for developers ready to transcend the limitations of linear AI chains and enter the era of Agentic Workflows. While basic LLM applications often suffer from brittleness and a lack of state management, this course equips you with the architectural patterns required to build resilient, self-healing, and highly sophisticated autonomous systems. We move past simple prompt-response cycles into the world of cyclic graphs, where AI doesn't just respond-it reasons, remembers, and collaborates.In this course, we dive deep into the core of LangGraph, beginning with a rigorous primer on its fundamental mechanics before quickly scaling into complex Workflow Architectures. You will master the art of state management, learning to implement both Short-Term and Long-Term Persistence to give your agents "human-like" memory across sessions. We won't just build agents; we will engineer Multi-Agent Systems that utilize Sub-Graphs to compartmentalize logic and solve enterprise-scale problems. Finally, we explore the frontier of Deep Agents, teaching you how to build recursive, long-horizon systems that can plan, self-correct, and execute multi-stage tasks with minimal human intervention.Whether you are an AI engineer, a backend developer, or a technical architect, this course provides the production-grade blueprints needed to orchestrate multiple LLMs into a unified, high-performing workforce. By the end of this journey, you will have the skills to design and deploy stateful, reliable, and intelligent agentic systems that can handle the unpredictability of real-world data and user interactions. Elevate your AI development from simple scripts to enterprise-grade agentic architectures.
AI Engineers who have mastered basic chains and are ready to build complex, stateful agentic architectures.,Backend Developers looking to implement reliable, multi-step AI workflows with robust persistence and memory.,Technical Architects needing to design collaborative multi-agent systems that can scale across enterprise-level tasks.
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