DeepResearchGym: A Free, Transparent, and Reproducible Evaluation
  Sandbox for Deep Research
Authors: name: João Coelho, name: Jingjie Ning, name: Jingyuan He, name: Kangrui Mao, name: Abhijay Paladugu, name: Pranav Setlur, name: Jiahe Jin, name: Jamie Callan, name: João Magalhães, name: Bruno Martins, name: Chenyan Xiong

Deep research systems represent an emerging class of agentic information
retrieval methods that generate comprehensive and well-supported reports to
complex queries. However, most existing frameworks rely on dynamic commercial
search APIs, which pose reproducibility and transparency challenges in addition
to their cost. To address these limitations, we introduce DeepResearchGym, an
open-source sandbox that combines a reproducible search API with a rigorous
evaluation protocol for benchmarking deep research systems. The API indexes
large-scale public web corpora, namely ClueWeb22 and FineWeb, using a
state-of-the-art dense retriever and approximate nearest neighbor search via
DiskANN. It achieves lower latency than popular commercial APIs while ensuring
stable document rankings across runs, and is freely available for research use.
To evaluate deep research systems' outputs, we extend the Researchy Questions
benchmark with automatic metrics through LLM-as-a-judge assessments to measure
alignment with users' information needs, retrieval faithfulness, and report
quality. Experimental results show that systems integrated with DeepResearchGym
achieve performance comparable to those using commercial APIs, with performance
rankings remaining consistent across evaluation metrics. A human evaluation
study further confirms that our automatic protocol aligns with human
preferences, validating the framework's ability to help support controlled
assessment of deep research systems. Our code and API documentation are
available at https://www.deepresearchgym.ai.