Agentic AI Workflows with LangGraph - Talk Python to Me Ep. 507
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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This video is the uncut, live recording of the Talk Python To Me podcast ( https://talkpython.fm ). We cover Python-focused topics every week and publish the edited and polished version in audio form. Subscribe in your podcast player of choice (100% free) at https://talkpython.fm/subscribe.
▬▬▬▬ Guests ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Sydney Runkle
▬▬▬▬ Links and resources from the show ▬▬▬▬▬▬▬▬▬▬▬▬
Sydney Runkle: / sydney-runkle-105a35190
LangGraph: https://github.com/langchain-ai/langg...
LangChain: https://www.langchain.com/
LangGraph Studio: https://github.com/langchain-ai/langg...
LangGraph (Web): https://www.langchain.com/langgraph
LangGraph Tutorials Introduction: https://langchain-ai.github.io/langgr...
How to Think About Agent Frameworks: https://blog.langchain.dev/how-to-thi...
Human in the Loop Concept: https://langchain-ai.github.io/langgr...
GPT-4 Prompting Guide: https://cookbook.openai.com/examples/...
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