Eddie's startup voyage
Last month, I started diving deep into the world of AI Agents. While exploring this field, I had a revelation: I was going to work on AI Agents from now on. In fact I was so enthralled with AI Agents that I did two things. First, I wrote a blog post about it, where I shared my enthusiasm for the field. Second, I began developing my own AI Agent library in Python.
Lately, I’ve been pondering on the second point. Why develop my own AI Agent library when there are plenty of available options out there?
After some reflection, I believe I am now able to articulate the reasons why I’m building my own AI Agent library. This post will cover the 3 main reasons why I’ve gone down this route.
There are countless libraries and AI Agent frameworks out there. The most popular ones include BabyAGI, CrewAI, LangChain (including LangGraph), and AutoGPT. Yet, there is no standard on how agents should be developed. The field is quite new and there is no “leader” who sets the pace. Therefore, more innovation and attempts to implement agents should be encouraged.
Among the libraries and frameworks I cited, LangChain is probably the most well known out there. But it also has its fair share of problems and detractors. It feels bloated: when installing LangChain you also install a lot of other dependencies. Additionally, developers have also complained about its slowness when running in production. I’ve also read, and experienced the frustration of going through the myriad of abstraction layers that must be peeled away to understand what the code does under the hood. Finally, others have highlighted security concerns with using LangChain.
