DeepAgent: A General Reasoning Agent with Scalable Toolsets
Large reasoning models have demonstrated strong problem-solving abilities,
yet real-world tasks often require external tools and long-horizon
interactions. Existing agent frameworks typically follow predefined workflows,
which limit autonomous and global task completion. In this paper, we introduce
DeepAgent, an end-to-end deep reasoning agent that performs autonomous
thinking, tool discovery, and action execution within a single, coherent
reasoning process. To address the challenges of long-horizon interactions,
particularly the context length explosion from multiple tool calls and the
accumulation of interaction history, we introduce an autonomous memory folding
mechanism that compresses past interactions into structured episodic, working,
and tool memories, reducing error accumulation while preserving critical
information. To teach general-purpose tool use efficiently and stably, we
develop an end-to-end reinforcement learning strategy, namely ToolPO, that
leverages LLM-simulated APIs and applies tool-call advantage attribution to
assign fine-grained credit to the tool invocation tokens. Extensive experiments
on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank,
TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA,
HLE), demonstrate that DeepAgent consistently outperforms baselines across both
labeled-tool and open-set tool retrieval scenarios. This work takes a step
toward more general and capable agents for real-world applications. The code
and demo are available at https://github.com/RUC-NLPIR/DeepAgent.
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Xiaoxi Li
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Wenxiang Jiao
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Wenxiang Jiao
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Jiarui Jin
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Jiarui Jin
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Guanting Dong
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KABI
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Jiajie Jin
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Yinuo Wang
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Hao Wang
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Yutao Zhu
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Ji-Rong Wen
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Yuan Lu
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Zhicheng Dou
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2025-11-06T11:48:57.439Z
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Zhicheng Dou
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DeepAgent: A General Reasoning Agent with Scalable Toolsets
Large reasoning models have demonstrated strong problem-solving abilities,
yet real-world tasks often require external tools and long-horizon
interactions. Existing agent frameworks typically follow predefined workflows,
which limit autonomous and global task completion. In this paper, we introduce
DeepAgent, an end-to-end deep reasoning agent that performs autonomous
thinking, tool discovery, and action execution within a single, coherent
reasoning process. To address the challenges of long-horizon interactions,
particularly the context length explosion from multiple tool calls and the
accumulation of interaction history, we introduce an autonomous memory folding
mechanism that compresses past interactions into structured episodic, working,
and tool memories, reducing error accumulation while preserving critical
information. To teach general-purpose tool use efficiently and stably, we
develop an end-to-end reinforcement learning strategy, namely ToolPO, that
leverages LLM-simulated APIs and applies tool-call advantage attribution to
assign fine-grained credit to the tool invocation tokens. Extensive experiments
on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank,
TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA,
HLE), demonstrate that DeepAgent consistently outperforms baselines across both
labeled-tool and open-set tool retrieval scenarios. This work takes a step
toward more general and capable agents for real-world applications. The code
and demo are available at https://github.com/RUC-NLPIR/DeepAgent.
DeepAgent, an end-to-end deep reasoning agent, autonomously performs thinking, tool discovery, and action execution using memory folding and reinforcement learning, outperforming baselines in various tool-use and application tasks.
DeepAgent autonomous thinking tool discovery action execution autonomous memory folding episodic memory working memory tool memory reinforcement learning ToolPO LLM-simulated APIs tool-call advantage attribution ToolBench API-Bank TMDB Spotify ToolHop ALFWorld WebShop GAIA HLE