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Agents in Lang graph utilize LLMs to determine sequences of actions, forming agentic workflows. The framework’s core concepts focus on nodes, edges, and states, allowing for versatile task management. Lang graph differentiates itself by enabling a deep understanding of agent operations and providing granular control, despite a steeper learning curve compared to frameworks like autogen or crew AI. This video explains basic concepts through hands-on examples, progressing to more complex real-world implementations, including interaction with large language models and conditional workflows.
LLMs serve as reasoning engines for sequencing actions in agents.
Lang graph offers greater control over agents’ behavior and workflows.
Conditional edges allow different actions based on input.
Cycles in Lang graph facilitate iterative workflows not allowed in LCL.
The video highlights significant advancements in agentic workflows using LLMs, prompting discussions on AI governance. As agents become integral to operational workflows, ethical considerations arise regarding decision-making processes and accountability. Establishing robust governance frameworks will be essential to address these concerns and ensure responsible AI integration.
The detailed exploration of Lang graph in the video emphasizes the importance of understanding the underlying mechanics of AI models in practical applications. By leveraging nodes and edges, data scientists can innovate within workflows, enhancing efficiency and adaptability in AI implementations. This can drive significant advancements in how AI systems interact and evolve in real-time.
In this context, LLMs guide the sequence of tasks in agent workflows.
These workflows leverage the capabilities of LLMs for efficiency.
This mechanism enables routing actions depending on input states.
OpenAI's models are integrated within the Lang graph framework for enhanced capabilities.
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It offers various components to create agents within Lang graph.
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Gao Dalie (高達烈) 15month
Naresh i Technologies 15month
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