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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Talk Python Courses

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 #507 deep-dive: talkpython.fm/507

Episode transcripts: talkpython.fm

Subscribe to Talk Python on YouTube: youtube.com

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