Introducing our work on general-purpose LLM Agents | GoodAI

LLM agents rely on their Long-Term Memory (LTM) to store and retrieve crucial memories. Conversations, thoughts, plans, actions, observations, skills, and behaviors are all stored within a vector database. The LTM enables pre-processing and post-processing of memories, ensuring optimal retrieval. By considering factors such as context, recency, imp... See more

LLM’s responses can be volatile and unreliable, as slight changes in the prompt can lead to significant variations in the output. Occasionally, the LLM may generate irrelevant or nonsensical information, necessitating ongoing improvements. Additionally, the context window has limited capacity, which can impact our agents’ understanding of complex s... See more

Thanks to the cognitive architecture design, goal-oriented behavior is at the core of our LLM agents. When presented with a goal, they employ a planning and execution process to achieve it. If the goal can be accomplished using atomic game actions, we generate a plan that outlines how to solve the objective. For longer-term goals, we decompose the ... See more