Autonomous Agents & Agent Simulations

BabyAGI differs in that it explicitly plans out a sequence of actions. It then executes on the first one, and then uses the result of that to do another planning step and update it’s task list. Our intuition is that this enables it to execute better on more complex and involved tasks, by using the planning steps essentially as a state tracking syst... See more

Because AutoGPT is more long running, passing the full list of agent steps to the LLM call is no longer feasible. Instead, AutoGPT added a retrieval-based memory over the intermediate agent steps.

The main differences between the AutoGPT project and traditional LangChain agents can be attributed to different objectives. In AutoGPT, the goals are often more open ended and long running. This means that AutoGPT has a different AgentExecutor and different way of doing memory (both of which are more optimized for long running tasks).

In the traditional LangChain Agent framework (and the AutoGPT framework), the agent thinks one step ahead at a time. For a given state of the world it think about what its next immediate action should be, and then does that action.