#507: Agentic AI Workflows with LangGraph

Talk Python To Me

Michael Kennedy

Technology

4.8 • 635 Ratings

🗓️ 2 June 2025

⏱️ 64 minutes

🔗️ Recording | iTunes | RSS

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Summary

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.

Transcript

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0:00.0	If you want to leverage the power of LLMs in your Python apps, you would be wise to consider
0:04.3	an Agenic framework.
0:06.1	Agenic empowers the LLMs to use tools and take further actions based on what it's learned
0:11.3	at that point.
0:12.8	And frameworks provide all the necessary building blocks to weave these into your apps
0:16.8	with features like long-term memory and durable resumability.
0:21.3	I'm excited to have Sidney Rinkle back on the podcast to dive into building Python apps with LangChane and Langraph.
0:27.3	This is Talk Python and Me.
0:28.6	Episode 507 recorded Tuesday, May 6th, 2025.
0:34.0	Are you ready for your host?
0:36.5	You're listening to Michael Kennedy on Talk Python to Me.
0:40.3	Life from Portland, Oregon, and this segment was made with Python.
0:46.2	Welcome to Talk Python to Me, a weekly podcast on Python.
0:49.9	This is your host, Michael Kennedy.
0:52.0	Follow me on Macedon, where I'm at M. Kennedy, and follow the podcast using at TalkPython, both accounts over at faucidon.org.
1:00.4	And keep up with the show and listen to over nine years of episodes at TalkPython.fm.
1:05.2	If you want to be part of our live episodes, you can find the live streams over on YouTube.
1:09.9	Subscribe to our YouTube channel
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1:16.9	sponsored by Posit Connect from the makers of Shiny. Publish, share, and deploy all of your data
1:22.2	projects that you're creating using Python. Streamlet, Dash, Shiny, Boca, fast API, flask, quarto, reports, dashboards,
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	...

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