Pydantic AI + DeepSeek V3 - The BEST AI Agent Combo
Updated: February 24, 2025
Summary
The video delves into the development of AI agents using n8n and Python with Pantic AI for production readiness. It covers essential aspects such as planning, coding the agent, API key setup, utilizing the Deep Seek V3 model, and implementing a CLI for user interaction. The demonstration also shows testing the agent with repositories, customization steps, and best practices for creating a functional AI agent.
Introduction to AI Agent Development
The video starts with the introduction to developing AI agents using n8n and transitioning to Python with Pantic AI for production-ready agents.
Planning the AI Agent
The planning phase is discussed, including creating a code Q&A agent, setting up a workflow, and customizing the agent.
Building AI Agent with Pantic AI
Details on building the AI agent with Pantic AI, including best practices, conversation history, API keys, and using the Deep Seek V3 model.
Coding the AI Agent
The process of coding the AI agent using Python and setting up environment variables, large language models, and access tokens is covered.
CLI Development for AI Agent
Implementation of the command line interface (CLI) for the AI agent, interacting with users, and running the agent in an infinite loop are discussed.
Testing the AI Agent
Testing the AI agent by interacting with repositories, analyzing structure, and requesting information from the agent is demonstrated.
Finalizing the AI Agent Development
Final steps in developing the AI agent, including changing models, handling API keys, and customizing the framework, are highlighted.
FAQ
Q: What is discussed in the planning phase of developing AI agents with n8n and Python using Pantic AI?
A: The planning phase includes creating a code Q&A agent, setting up a workflow, and customizing the agent.
Q: What details are provided for building the AI agent with Pantic AI?
A: Details include best practices, conversation history, API keys, and using the Deep Seek V3 model.
Q: What is covered in the process of coding the AI agent using Python?
A: The process covers setting up environment variables, large language models, and access tokens.
Q: What is the implementation discussed for the AI agent?
A: The implementation includes setting up a command line interface (CLI), interacting with users, and running the agent in an infinite loop.
Q: How is testing of the AI agent demonstrated?
A: Testing is demonstrated by interacting with repositories, analyzing structure, and requesting information from the agent.
Q: What are the final steps highlighted in developing the AI agent?
A: The final steps include changing models, handling API keys, and customizing the framework.
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