LangGraph & DSPy: Build Controllable AI Agents with Tools
LangGraph & DSPy: Build Controllable AI Agents with Tools
Published 9/2025
Duration: 2h 3m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 1.34 GB
Genre: eLearning | Language: English
Published 9/2025
Duration: 2h 3m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 1.34 GB
Genre: eLearning | Language: English
Learn to design and build smarter AI agents, optimize tool use, and control tool arguments query with LangGraph and DSPy
What you'll learn
- Understand the stateful architecture of LangGraph workflows
- Build Stateful LangGraph agents with tools and memory
- Understand DSPy and its role in prompt optimizations
- Build DSPy-augmented LangGraph agents with controllable tool-calling arguments and queries
Requirements
- Basic understanding of python and a working idea of what AI agents mean
Description
Unlock the power ofLangGraphto build controllable,stateful AI agentsthat go beyond basic chatbots. In this course, you’ll learn how to designlow-level agent workflowswith precise control over tools and arguments, while extending capabilities usingDSPyfor prompt optimization. Perfect for developers seeking to master the next generation of agent frameworks.
We’ll start by exploringLangGraph fundamentals, understanding how to structure agents, manage memory, and create step-by-step execution flows. You’ll integrateLangChainfor tool use and retrieval, giving your agents access to external knowledge. By the end of this section, you’ll know how to design AI agents that are both powerful and controllable in real-world applications.
The course also coversDSPy optimizationsto make your agents smarter when constructing tool arguments and queries. You’ll see how to extend LangGraph’s controllability by applying structured prompt optimizations, reducing errors, and improving accuracy. These techniques allow you to fine-tune agent behavior without manual trial-and-error, accelerating your development process.
Finally, we’ll useLangSmith for observability, enabling detailedtracing and debuggingof agent workflows. This ensures you can monitor, analyze, and refine your agents effectively. By combining LangGraph, LangChain, DSPy, and LangSmith, you’ll be equipped with a cutting-edge toolkit to design, build, and deploysmarter AI agentswith confidence.
Who this course is for:
- AI Agents developers looking for ways to build controllable and smarter AI agents with tools
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