Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
As language agents tackle increasingly complex tasks, they struggle with
effective error correction and experience reuse across domains. We introduce
Agent KB, a hierarchical experience framework that enables complex agentic
problem solving via a novel Reason-Retrieve-Refine pipeline. Agent KB addresses
a core limitation: agents traditionally cannot learn from each other's
experiences. By capturing both high-level strategies and detailed execution
logs, Agent KB creates a shared knowledge base that enables cross-agent
knowledge transfer. Evaluated on the GAIA benchmark, Agent KB improves success
rates by up to 16.28 percentage points. On the most challenging tasks, Claude-3
improves from 38.46% to 57.69%, while GPT-4 improves from 53.49% to 73.26% on
intermediate tasks. On SWE-bench code repair, Agent KB enables Claude-3 to
improve from 41.33% to 53.33%. Our results suggest that Agent KB provides a
modular, framework-agnostic infrastructure for enabling agents to learn from
past experiences and generalize successful strategies to new tasks.
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Xiangru Tang
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2025-07-09T08:50:46.438Z
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Xiangru Tang
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RTT1
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Tianrui Qin
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Tianhao Peng
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Ziyang Zhou
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2025-07-09T08:50:48.624Z
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ZiyangZhou
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AzHouangy
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Daniel Shao
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Tingting Du
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Xinming Wei
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Peng Xia
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Peng Xia
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Fang Wu
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2025-07-23T08:37:25.015Z
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Fang Wu
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fangwu97
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He Zhu
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Ge Zhang
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2025-07-09T08:50:41.208Z
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Ge Zhang
user
zhangysk
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Jiaheng Liu
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Xingyao Wang
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Sirui Hong
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Chenglin Wu
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Hao Cheng
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Chi Wang
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Wangchunshu Zhou
claimed_verified
2025-07-29T12:51:17.995Z
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Zhou
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Wangchunshu
Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
As language agents tackle increasingly complex tasks, they struggle with
effective error correction and experience reuse across domains. We introduce
Agent KB, a hierarchical experience framework that enables complex agentic
problem solving via a novel Reason-Retrieve-Refine pipeline. Agent KB addresses
a core limitation: agents traditionally cannot learn from each other's
experiences. By capturing both high-level strategies and detailed execution
logs, Agent KB creates a shared knowledge base that enables cross-agent
knowledge transfer. Evaluated on the GAIA benchmark, Agent KB improves success
rates by up to 16.28 percentage points. On the most challenging tasks, Claude-3
improves from 38.46% to 57.69%, while GPT-4 improves from 53.49% to 73.26% on
intermediate tasks. On SWE-bench code repair, Agent KB enables Claude-3 to
improve from 41.33% to 53.33%. Our results suggest that Agent KB provides a
modular, framework-agnostic infrastructure for enabling agents to learn from
past experiences and generalize successful strategies to new tasks.
Agent KB, a hierarchical experience framework, enhances problem-solving success rates across different agents by enabling cross-agent knowledge transfer through a Reason-Retrieve-Refine pipeline.
Agent KB hierarchical experience framework Reason-Retrieve-Refine pipeline cross-agent knowledge transfer GAIA benchmark SWE-bench code repair