Open source Large Language Models like DeepSeek offer two key advantages: enhanced privacy by running models within your infrastructure, and cost-effective scaling without being tied to proprietary API pricing.

In this tutorial, we'll build a Python agent using DeepSeek that demonstrates the core mechanics of LLM-powered automation. While frameworks like LangChain or Llamaindex provide comprehensive agent toolkits, building one from scratch helps understand the fundamental components and provides flexibility for custom use cases.

What is an Agent?

At its core, an agent combines two essential capabilities:

  1. Generating contextual outputs using an LLM
  2. Executing those outputs to interact with the world

The execution layer is particularly powerful as it enables the agent to:

  • Generate task-specific code based on user requirements
  • Interact with external systems through APIs, databases, and services
  • Process and analyze data using specialized libraries

Our agent will use DeepSeek to generate Python code, but the same principles apply to other languages like:

  • SQL for database operations
  • JavaScript for web automation
  • Terraform for infrastructure management
  • Shell scripts for system tasks

What We'll Build

We'll create a pure Python implementation that can:

  1. Accept natural language queries
  2. Generate appropriate code using DeepSeek
  3. Execute the code safely in a controlled environment
  4. Handle various data science and analytics tasks

You'll see the agent tackle real-world examples like:

  • Data analysis with Pandas
  • Machine learning with scikit-learn
  • Statistical computations with NumPy
  • Visualization using matplotlib/seaborn
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By the end of the tutorial, you will have a solid foundation for designing and building AI agents from scratch with open source models like DeepSeek. Furthermore, you can extend the code included for various real-world use cases.

The Colab Notebook

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