Introducing our work on general-purpose LLM Agents | GoodAI

3 days reading through the code of the paper that solved this, here are some notes:

- Long term interactions: the promise of LLM agents is to potentially run over weeks. A lot of the ideas in the paper tackle exactly this problem

- Memory: It’s easy to write down interactions of agents. But how do we track these over time? How do these change with ti... See more

An agent can be thought of as a logical wrapper around an LLM, allowing us to add several features to our AI systems, primarily:

• Tool usage, such as calling APIs for info, executing code,


• Internal thoughts over multiple generation steps


• Ability to use various tools and reasoning steps to answer more complex queries.


• Parallel agents can go and complete

... See more

An LLM’s abilities are amplified when combined using emerging agent frameworks like BabyAGI, AutoGPT and ChatArena. These frameworks break down workflows into a series of discrete roles each handled by AI agents, which are given a defined objective and work together to automate a complex task.

Initially, this will take the form of human-AI collabora... See more

AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as... See more

Even if LLMs don’t get smarter (though I suspect they will, and soon) new capabilities and modes of interacting with AIs, like agents and massive context windows, will help LLMs do dramatic new feats.

First off, a primer on autonomous agents: - Give them a task - Give them memory - Give them access to tools - They decide on-the-fly how to accomplish your task and remove you from this process because... it's autonomous - Watch them attempt to do the work