🚧 Note: This project is evolving rapidly—join the community by opening issues, submitting PRs, leaving comments, or ⭐ starring the repo to help build a leading resource for agentic search.
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Research Collection: Curate and categorize comprehensive research work in agentic search, including papers, code implementations, and empirical findings
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Interactive Demos: Build demonstration pages to showcase different agentic search methods and allow hands-on exploration of their capabilities
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Evaluation Arena: Develop a Python toolkit for systematic evaluation and benchmarking of agentic search methods across diverse tasks and metrics
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Training Gym: Create a Python framework for training and optimizing agentic search models, including reinforcement learning and other approaches
For each paper, we provide the following information:
👨🎓 First Author · 📧 Corresponding Author (Last Author if not specified) · 🏛️ First Organization · 📊 Dataset
Note: Please submit a PR if we missed anything!
📊 Dataset Types:
General QA: NQ, TriviaQA, PopQA
Multi-Hop QA: HotpotQA, 2wiki, Musique, Bamboogle
Complex Task: GPQA, GAIA, WebWalker QA, Humanity's Last Exam (HLE)
Report Generation: Glaive
Math & Coding: AIME, MATH500, AMC, LiveCodeBench
Search-R1: Training LLMs to Reason and Leverage Search
Engines with Reinforcement Learning
👨🎓 Bowen Jin · 📧 Jiawei Han · 🏛️ UIUC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B / 7B · 🎯 Training: GRPO, PPO
An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM Agents
👨🎓 Bowen Jin · 📧 Jiawei Han · 🏛️ UIUC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B / 7B / 14B· 🎯 Training: GRPO, PPO
Notes: a new version of Search-R1.
WebThinker: Empowering Large Reasoning Models with Deep Research Capability:
👨🎓 Xiaoxi Li · 📧 Zhicheng Dou · 🏛️ GSAI, RUC
📊 Dataset: Complex Task, Report Generation · 🤖 Model: QwQ 32B · 🎯 Training: SFT, DPO
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
![[code]](https://img.shields.io/github/stars/GAIR-NLP/DeepResearcher)
👨🎓 Yuxiang Zheng · 📧 Pengfei Liu · 🏛️ SJTU
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-7B · 🎯 Training: GRPO
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
![[code]](https://img.shields.io/github/stars/RUCAIBox/R1-Searcher)
👨🎓 Huatong Song · 📧 Wayne Xin Zhao · 🏛️ GSAI, RUC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-7B, Llama-3.1-8B · 🎯 Training: SFT, GRPO, REINFORCE++
R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning
![[code]](https://img.shields.io/github/stars/RUCAIBox/R1-Searcher-plus)
👨🎓 Huatong Song · 📧 Wayne Xin Zhao · 🏛️ GSAI, RUC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-7B · 🎯 Training: SFT, GRPO, REINFORCE++
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
![[code]](https://img.shields.io/github/stars/RUCAIBox/SimpleDeepSearcher)
👨🎓 Shuang Sun · 📧 Wayne Xin Zhao · 🏛️ GSAI, RUC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-7B / 32B, QwQ-32B · 🎯 Training: SFT, DPO, REINFORCE++
ZeroSearch: Incentivize the Search Capability of LLMs without Searching
![[code]](https://img.shields.io/github/stars/Alibaba-NLP/ZeroSearch)
👨🎓 Hao Sun · 📧 Zile Qiao, Jiayan Guo, Yan Zhang · 🏛️ Tongyi Lab
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B / 7B, s LLaMA-3.2-3B · 🎯 Training: REINFORCE, GRPO, PPO
Chain-of-Retrieval Augmented Generation
![[code]](https://img.shields.io/github/stars/microsoft/LMOps)
👨🎓 Liang Wang · 📧 Furu Wei · 🏛️ MSRA
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Llama-3.1-8B-Instruct · 🎯 Training: REINFORCE, GRPO, PPO
![[code]](https://img.shields.io/github/stars/hzy312/knowledge-r1)
👨🎓 Ziyang Huang · 📧 Kang Liu · 🏛️ IA, CAS
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B / 7B · 🎯 Training: GRPO
Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging
👨🎓 Hongjin Qian · 📧 Zheng Liu · 🏛️ BAAI
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B / 7B · 🎯 Training: GRPO, PPO
Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
![[code]](https://img.shields.io/github/stars/syr-cn/AutoRefine)
👨🎓 Yaorui Shi · 📧 Xiang Wang · 🏛️ USTC
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-3B · 🎯 Training: GRPO
![[code]](https://img.shields.io/github/stars/BeastyZ/ConvSearch-R1)
👨🎓 Changtai Zhu · 📧 Xipeng Qiu · 🏛️ FDU
📊 Dataset: Conversational QA · 🤖 Model: Qwen-2.5-3B / Llama-3.2-3B · 🎯 Training: SFT, GRPO
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
![[code]](https://img.shields.io/github/stars/wlzhang2020/ReasonRAG)
👨🎓 Wenlin Zhang · 📧 Xiangyu Zhao · 🏛️ CityUHK
📊 Dataset: General QA, Multi-Hop QA · 🤖 Model: Qwen-2.5-7B · 🎯 Training: DPO
WebDancer: Towards Autonomous Information Seeking Agency
![[code]](https://img.shields.io/github/stars/Alibaba-NLP/WebAgent)
👨🎓 Jialong Wu · 📧 Wenbiao Yin, Yong Jiang · 🏛️ Tongyi Lab
📊 Dataset: Complex Task · 🤖 Model: Qwen-2.5-7B / 32B, QwQ-32B · 🎯 Training: DAPO
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
![[code]](https://img.shields.io/github/stars/Agent-RL/ReCall)
👨🎓 Mingyang Chen · 📧 Fan Yang · 🏛️ Baichuan
📊 Dataset: Multi-Hop QA · 🤖 Model: Qwen-2.5-7B / 32B · 🎯 Training: GRPO
Search-o1: Agentic Search-Enhanced Large Reasoning Models:
![[code]](https://img.shields.io/github/stars/sunnynexus/Search-o1?style=social)
👨🎓 Xiaoxi Li · 📧 Zhicheng Dou · 🏛️ GSAI, RUC
📊 Dataset: General QA, Multi-Hop QA, Complex Task, Math & Coding · 🤖 Model: QwQ-32B-Preview
Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
![[code]](https://img.shields.io/github/stars/theworldofagents/Agentic-Reasoning)
👨🎓 Junde Wu · 📧 Yuyuan Liu · 🏛️ Oxford University
📊 Dataset: Complex Task · 🤖 Model: APIs
Coding Agents with Multimodal Browsing are Generalist Problem Solvers
![[code]](https://img.shields.io/github/stars/adityasoni9998/OpenHands-Versa)
👨🎓 Aditya Bharat Soni · 📧 Graham Neubigo · 🏛️ CMU
📊 Dataset: Complex Task · 🤖 Model: claude-3-7-sonnet
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
![[code]](https://img.shields.io/github/stars/dongguanting/Tool-Star)
👨🎓 Guanting Dong · 📧 Zhicheng Dou · 🏛️ GSAI, RUC
📊 Dataset: General QA, Multi-Hop QA, Math & Coding · 🤖 Model: Qwen-2.5-3B· 🎯 Training: SFT,GRPO, PPO
OTC: Optimal Tool Calls via Reinforcement Learning
👨🎓 Hongru Wang · 📧 Heng Ji · 🏛️ CUHK
📊 Dataset: General QA, Multi-Hop QA, Math & Coding · 🤖 Model: Qwen-2.5-3B / 7B· 🎯 Training: GRPO, PPO
Multimodal-Search-R1: Incentivizing LMMs to Search
![[code]](https://img.shields.io/github/stars/EvolvingLMMs-Lab/multimodal-search-r1)
👨🎓 Jinming Wu · 📧 Zejun Ma · 🏛️ BUPT
📊 Dataset: VQA · 🤖 Model: Qwen2.5-VL-Instruct-3B/7B · 🎯 Training: GRPO
👨🎓 Zhaorui Yang · 📧 Bo Zhang · 🏛️ ZJU
📊 Dataset: Report Generation
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
![[code]](https://img.shields.io/github/stars/YunjiaXi/InfoDeepSeek)
👨🎓 Yunjia Xi · 📧 Jianghao Lin · 🏛️ SJTU
📊 Dataset: General QA, Multi-Hop QA
BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents
![[code]](https://img.shields.io/github/stars/openai/simple-evals)
👨🎓 Jason Wei · 📧 Amelia Glaese · 🏛️ OpenAI
📊 Dataset: Web Browsing
HealthBench: Evaluating Large Language Models Towards Improved Human Health
👨🎓 Rahul K. Arora · 📧 Karan Singhal · 🏛️ OpenAI
📊 Dataset: Multi-turn Medical QA
👨🎓 Lisheng Huang · 📧 Wayne Xin Zhao · 🏛️ GSAI, RUC
📊 Dataset: Web Browsing
WebWalker: Benchmarking LLMs in Web Traversal
👨🎓 Jialong Wu · 📧 Deyu Zhou, Yong Jiang · 🏛️ SEU, Tongyi Lab
📊 Dataset: Web Browsing
👨🎓 Weinan Zhang · 🏛️ SJTU
OpenAI's Deep Research: https://openai.com/index/introducing-deep-research/
Google's Gemini Pro: https://www.google.com/search/about/
X's Grok 3: https://x.ai/news/grok-3
Perplexity: https://www.perplexity.ai/
Jina AI: https://jina.ai/deepsearch/
Metasota: https://metaso.cn/
We are building a demo page to showcase different agentic search methods and allow hands-on exploration of their capabilities. Each demo will be integrated into a standardized retrieval and web browser interface with comparable settings, enabling comprehensive and fair comparisons across various approaches. This systematic evaluation will help identify strengths and limitations of different methods and advance the state-of-the-art in agentic search.
Currently, it looks like this:
You can run the demo by serving the models via vllm:
vllm serve path_to_your_model --port 25900 --host 127.0.0.1Then, build a search server, for example, use this:
bash retrieval_launch.shConfig your serve address in config/demo_config.json, modify model list here.
Run the demo by:
streamlit run demo/app.pyWe maintain a collection of 📊 paper presentation slides on Overleaf to facilitate learning and knowledge sharing in the agentic search community. Each presentation consists of 3-5 slides that concisely introduce key aspects of a paper, including motivation, methodology, and main results. These slides serve as quick references for understanding important works in the field and can be used for self-study, teaching, or research presentations.
🔗 Check out our slides collection: Agentic Search Paper Slides
We are building an arena page to benchmark different agentic search methods in a unified evaluation framework. All methods will be integrated into standardized retrieval and web browser interfaces with comparable settings, enabling comprehensive and fair comparisons across various approaches. This systematic evaluation will help identify strengths and limitations of different methods and advance the state-of-the-art in agentic search.
We are organizing a collection of optimization frameworks and training approaches used in agentic search, including reinforcement learning methods like GRPO and PPO, as well as supervised fine-tuning techniques. This will help researchers understand and implement effective training strategies for their agentic search models.
Stay tuned for detailed tutorials and code examples on training agentic search systems!
We welcome contributions to this repository! If you have any suggestions or feedback, please feel free to open an issue or submit a pull request.
If you find this repository useful, please consider citing it as follows:
@misc{awesome-agentic-search,
author = {Hongjin Qian, Zheng Liu},
title = {Awesome Agentic Search},
year = {2025},
publisher = {GitHub},