Building AI Agents in Pure Python - Beginner Course

About this Video

• Video Title: Building AI Agents in Pure Python - Beginner Course
• Channel: Dave Ebbelaar
• Speakers: Dave Ebbelaar
• Duration: 00:46:44

Introduction

This video provides a beginner's course on building AI agents using only Python and the LLMs API, without relying on pre-built tools or frameworks. The instructor, Dave Ebbelaar, guides viewers through core patterns and building blocks for creating effective AI systems, drawing from an Entropic blog post.

Key Takeaways

1. Direct API Calls: Building AI agents can be achieved through direct API calls to LLMs, using Python's OpenAI SDK. This approach provides a deep understanding of underlying principles, often bypassing the need for complex tools.
2. Structured Output: Using structured output with the OpenAI API allows developers to specify key-value pairs for more controlled and programmatically usable responses from the LLM. The Pydantic library aids in defining these data models.
3. Tool Use: The OpenAI API enables the use of external tools (like weather APIs) within the AI agent. The LLM suggests tool use and parameters; the developer integrates and calls the external tools.
4. Memory and Retrieval: Memory in AI agents is represented by the conversation history (messages). Retrieval can be dynamically integrated through tool use, allowing the AI to access and use information from a knowledge base.
5. Workflow Patterns: The video details three core workflow patterns: prompt chaining (sequencing LLM calls), routing (conditional branching based on LLM output), and parallelization (running independent LLM calls concurrently). These are combined to build robust, reliable AI systems.