arxiv:2510.21618

DeepAgent: A General Reasoning Agent with Scalable Toolsets

Published on Oct 24

· Submitted by

Xiaoxi Li on Oct 27

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Authors:

Wenxiang Jiao ,

Guanting Dong ,

Abstract

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.

AI-generated summary

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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lixiaoxi45

Paper submitter about 15 hours ago

DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. This paradigm shifts away from traditional, predefined workflows, allowing the agent to maintain a global perspective on the entire task and dynamically discover tools on an as-needed basis.

To handle long-horizon interactions and prevent getting stuck in incorrect exploration paths, we introduce an Autonomous Memory Folding mechanism. This allows DeepAgent to "take a breath" by compressing its interaction history into a structured, brain-inspired memory schema, enabling it to reconsider its strategy and proceed efficiently.

Furthermore, we propose ToolPO, an end-to-end reinforcement learning (RL) training method tailored for general tool use, which enhances the agent's proficiency in mastering these complex mechanisms.

We conduct extensive experiments on a wide range of benchmarks:

(1) General Tool-Use Tasks: We evaluate DeepAgent on ToolBench, API-Bank, TMDB, Spotify, and ToolHop, which feature toolsets scaling from tens to over ten thousand distinct tools.
(2) Downstream Applications: We test its performance on ALFWorld, WebShop, GAIA, and Humanity's Last Exam (HLE), which require the use of domain-specific toolsets. The overall results in Figure show that DeepAgent achieves superior performance across all scenarios.

lixiaoxi45

Paper submitter about 15 hours ago

🎬 Demo

General Agent Task with 16,000+ RapidAPIs

DeepAgent is a reasoning agent with scalable toolsets, capable of tackling general tasks by searching for and using the appropriate tools from over 16,000 RapidAPIs in an end-to-end agentic reasoning process. (Note: Due to some APIs in ToolBench being unavailable, API responses are LLM-simulated in this demo to show the system's normal functionality.)

Embodied AI Agent Task in ALFWorld Env.

DeepAgent also excels at navigation-based tasks (e.g., web browsing, OS interaction, and embodied AI) by using a versatile set of pluggable actions such as moving, looking, and taking.

Deep Research Task with Specialized Tools

DeepAgent can also serve as a powerful research assistant, equipped with specialized tools for web search, browsing, code execution, visual QA, and file processing.