Build LLM Agents From Scratch

Frustrated by the complexity of existing agent frameworks like LangChain, we decided to build our own LLM agents from the ground up. Our implementation captures the core functionalities while stripping away unnecessary code, making it both easy to understand and use.

🚀 Features

🔹 Simple Agent Implementations

• Single-Agent System
  A streamlined solution for straightforward tasks, offering a clear and minimalistic interface for basic LLM interactions.
• Multi-Agent System
  Enables collaborative interactions among multiple agents, paving the way for solving more complex and multifaceted problems.

🔹 Workflow Patterns to Build AI Systems

• Overview
  In this tutorial, we’ll cover everything you need to start building AI agents in pure Python. We’ll start with the essential building blocks and then dive into workflow patterns for more reliable systems. Basic Python skills, familiarity with the OpenAI SDK, and an API key are recommended. Cloning the GitHub repository will help you follow along step by step.

• Part 1: Building Block – The Augmented LLM

  • Basic LLM Calls
  • Structured Output
  • Tool Use
  • Retrieval

• Part 2: Workflow Patterns to Build AI Systems

  • Prompt Chaining
  • Routing
  • Parallelization

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Embrace the simplicity and power of our from-scratch approach to LLM agents and unlock new possibilities in automation, intelligent system design, and beyond.