Agents

I finally got around to exploring local models, which is surprisingly simple to set up. I wanted to see how well a local model would perform in ToolAgent compared to the remote models (mostly Gemini) I have been using.

I will likely run out of topics to cover in the “LLMs as Agents” series, eventually. I wanted to explore creating a chat UI and what I could do with embeddings but I didn’t do that. For more context , checkout the previous posts: Minimalist Approach, Taking Action, Planning via Prompting, Tools & Benchmarking, and WebAgent Gets a Refactor. Take a look at the repository to see the code.

My fifth post on LLMs as Agents. I just it’s a series now.

I spent some time refactoring the Tool Agent code, added some additional mock tools and even some basic benchmarking. For more context, checkout the previous posts:

My exploration of how to use LLMs as Agents continues. In this post, I’m looking at ways to improve the outcomes by using better approaches to planning. For more context, checkout the previous posts:

I’m still exploring how to use LLMs to build agents. In the first post, I described some of the motivation and approach. I started working on two agents:

Large Language Models (LLMs) are powerful tools for generating text, answering questions, and coding. We’ve moved beyond generating content, and LLMs are now being used to take actions as agents — independent entities that can act, use tools, and interact with their environment. You probably already know all of this.