Agent Development: A Beginner’s Guide

Disclaimer

As a complete novice, my journey begins with source code exploration. Although I am not yet fully familiar with frameworks like autogen and langchain, I find that learning from the code provides a better understanding and allows for custom solutions. This content shares my personal insights and should be viewed as a starting point for others in their learning process.

Your Learning Path: Step-by-Step

1. Start from Scratch: Begin by watching Mcp’s tutorials on Bilibili, which cover the basics of technical exploration into agent development. Pay attention to techniques for integrating MCP tools into your agent projects.
2. Explore RAG Techniques: Check out Nir Diamant’s techniques on RAG. This resource is notably beginner-friendly and suggests that RAG can serve as an agent’s long-term memory.
3. Single Agent Fundamentals: Dive into the Openmanus source code and familiarize yourself with the structure in main.py. Track the flow from base.py to react.py, then to toolcall.py and finally manus.py. Understanding the definition and execution of agents will take about three hours, but it’s an enlightening process!
4. Multi-Agent Systems: Explore Evoagentx, which may not be the most classic multi-agent framework, but it presents a fascinating take on agent complexity. While the source code poses additional challenges, grasping the basic automation workflow can inspire innovative solutions. Agents will divide high-level goals into subtasks and communicate through saved environment states to build a cohesive workflow.

Beyond Basics: Advanced Topics

While I have not yet had the time to delve into agent-reinforcement learning (RL) paradigms explored in Evoagentx, these concepts seem promising and merit exploration in the future when I have more time. Engaging with these frameworks will cultivate a deeper understanding and enable more creative applications in agent development.

Join the Discussion!

If you have any insights, suggestions, or questions regarding this guide, please feel free to share in the comments! Collaborative learning can greatly enhance our understanding of these complex topics.