Talk Python To Me #507: Agentic AI Workflows with LangGraph
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Jun 2, 2025 Sydney Runkle, an open-source developer at LangChain, dives into the world of agentic AI workflows using LangGraph. She explains how integrating agentic frameworks can supercharge Python applications with large language models (LLMs). The discussion highlights the balance of AI-driven workflows, transparency in software development, and the importance of context and memory in enhancing user interactions. Runkle also shares insights on developing intelligent agents and managing application interrupts, showcasing practical examples that bridge creativity with responsibility in AI.
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Agentic Frameworks Empower LLMs
- Agentic frameworks empower LLMs to use external tools and take further actions based on learned information.
- These frameworks provide essential features like long-term memory and resumability for Python app integration.
Use Agents to Enhance AI
- Use agents as tools combining reasoning, tool-calling, and memory to build powerful AI applications.
- Consider agents as enhanced LLMs that can act within your apps, not just chat models.
Enable Real-Time Tool Use
- Allow LLM agents to access real-time data and tools to overcome training data limitations.
- Enable your LLM to decide which tools to use for dynamic, relevant outcomes.
