MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback
Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or from previous agent-environment interactions, LLM agents are required to use memory mechanisms to store and retrieve experiences. While sophisticated memory systems exist for dialogue agents, few studies have empirically examined how to improve agents' tool-using capabilities through past user-agent conversations. We propose MemToolAgent, a framework that improves tool use through memory management. Our approach contains a memory extraction module that processes past experiences into structured memory entries, an...
| Authors | Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar et al. |
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| Primary category | cs.AI |
| Published | 6 June 2026 |
| Last updated at source | 10 June 2026 |
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