If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph.

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Links from the show

Sydney Runkle: linkedin.com
LangGraph: github.com
LangChain: langchain.com
LangGraph Studio: github.com
LangGraph (Web): langchain.com
LangGraph Tutorials Introduction: langchain-ai.github.io
How to Think About Agent Frameworks: blog.langchain.dev
Human in the Loop Concept: langchain-ai.github.io
GPT-4 Prompting Guide: cookbook.openai.com
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm

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#507: Agentic AI Workflows with LangGraph

Talk Python To Me

#507: Agentic AI Workflows with LangGraph