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AgentCPM-Explore 标志

中文 | English】

Latest News

  • [2026-01-12] 🚀🚀🚀 We have open-sourced AgentCPM-Explore, the first open-source agent model with 4B parameters to appear on 8 widely used long-horizon agent benchmarks.

Overview

AgentCPM is a series of open-source large language model agents jointly developed by the THUNLP, Renmin University of China, ModelBest, and OpenBMB.

Model List

Model Download Links Resources Technical Report
AgentCPM-Explore 🤗 Hugging Face
🤖 ModelScope
AgentDock: An unified tool sandbox management and scheduling platform
AgentRL: An asynchronous agent reinforcement learning training framework
AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilities
Coming Soon

AgentCPM-Explore

The AgentCPM team has focused on systematically building agents’ deep research capabilities and released AgentCPM-Explore, a deep-search LLM agent. AgentCPM-Explore is the first open-source agent model with 4B parameters to appear on eight widely used long-horizon agent benchmarks such as GAIA, XBench, etc.

Key highlights:

  • SOTA at 4B Scale: Best-in-class among same-size models, matches or surpasses 8B models, rivals some 30B+ and closed-source LLMs.

  • Deep Exploration: 100+ turns of continuous interaction with multi-source cross-validation and dynamic strategy adjustment.

  • End-to-End Open Source: Complete training and evaluation infrastructure for community development and custom extensions.

Demo

Demo examples (speed up):

demo_en.mp4

License

  • The code in this repository is released under the Apache-2.0 license.

Citation

If AgentCPM-Explore is useful for your research, please cite the codebase:

@software{AgentCPMExplore2026,
  title  = {AgentCPM-Explore: An End-to-End Infrastructure for Training and Evaluating LLM Agents},
  author = {Haotian Chen and Xin Cong and Shengda Fan and Yuyang Fu and Ziqin Gong and Yaxi Lu and Yishan Li and Boye Niu and Chengjun Pan and Zijun Song and Huadong Wang and Yesai Wu and Yueying Wu and Zihao Xie and Yukun Yan and Zhong Zhang and Yankai Lin and Zhiyuan Liu and Maosong Sun},
  year   = {2026},
  url    = {https://github.com/OpenBMB/AgentCPM}
}

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An End-to-End Infrastructure for Training and Evaluating Various LLM Agents

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