
Discover Pedantic AI, a Python agent framework that makes generative AI apps production-ready, offering model-agnostic design, type-safe Pydantic validation, streaming responses, and testing-focused dependency injection under the MIT license.
Demonstrate a hello world in the pydantic ai agent framework in Python, creating the agent with model Gemini 1.5 flash, using a concise system prompt and Runasync to print results.
Explore tool and dependency injection with pedantic ai by building a bank support agent, defining support dependencies and results, and configuring an OpenAI GPT model for risk assessment.
Define add_customer_name with run_context and support_dependency to return a string, fetching the customer name from db by id. Define customer_balance to return balance, via a recursive call with include_pending.
Explore configuring and running a pydantic ai agent with dependencies in python to fetch account balance and support responses, including card blocking guidance from database results.
Observe the Pydantic AI agent framework in Python by instrumenting with a pedantic log file, set up logging, and view execution traces, spans, and a live dashboard.
Explore pedantic log file instrumentation through agent run prompts and balance queries, tracing asynchronous PostgreSQL tasks and OpenAI GPT-4 powered responses in a bank support context.
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Course Description: "Introduction to Pydantic AI Agent Framework in Python / How to Pydantic with LLMs ?"
Discover the power of PydanticAI, a Python-based agent framework designed to simplify the development of intelligent applications with large language models (LLMs). This course is your beginner-friendly introduction to PydanticAI, ideal for developers and AI enthusiasts eager to explore how to integrate LLMs into real-world projects efficiently.
We’ll start by explaining the core concepts of PydanticAI, its advantages over other frameworks, and why it’s a game-changer for building type-safe, production-grade AI applications. You’ll learn how to create intelligent agents with structured response validation, leverage Pydantic’s novel dependency injection system, and manage control flows seamlessly using Python best practices.
Key highlights of the course:
Overview of PydanticAI’s ecosystem and its integration with popular LLMs like OpenAI, Anthropic, Gemini, and more.
Building and deploying agents with dynamic system prompts and custom tools.
Handling dependency injection for robust and testable AI-driven applications.
Introduction to debugging and monitoring with Pydantic Logfire.
By the end of this course, you’ll have a strong foundation in PydanticAI, understand its unique features, and be ready to use it to prototype intelligent agents. Whether you’re an AI newcomer or an experienced Python developer, this course is your gateway to building smarter applications!