  The adaptation of Large Language Model (LLM)-based agents to execute tasks
via natural language prompts represents a significant advancement, notably
eliminating the need for explicit retraining or fine tuning, but are
constrained by the comprehensiveness and diversity of the provided examples,
leading to outputs that often diverge significantly from expected results,
especially when it comes to the open-ended questions. This paper introduces the
Memory Sharing, a framework which integrates the real-time memory filter,
storage and retrieval to enhance the In-Context Learning process. This
framework allows for the sharing of memories among multiple agents, whereby the
interactions and shared memories between different agents effectively enhance
the diversity of the memories. The collective self-enhancement through
interactive learning among multiple agents facilitates the evolution from
individual intelligence to collective intelligence. Besides, the dynamically
growing memory pool is utilized not only to improve the quality of responses
but also to train and enhance the retriever. We evaluated our framework across
three distinct domains involving specialized tasks of agents. The experimental
results demonstrate that the MS framework significantly improves the agents'
performance in addressing open-ended questions.
