Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2% to 52.0% with Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with TTS. The code is available at https://anonymous.4open.science/r/SWE-World-2D1E.

Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures. In this work, we propose ManuSearch, a transparent and modular multi-agent framework designed to democratize deep search for LLMs. ManuSearch decomposes the search and reasoning process into three collaborative agents: (1) a solution planning agent that iteratively formulates sub-queries, (2) an Internet search agent that retrieves relevant documents via real-time web search, and (3) a structured webpage reading agent that extracts key evidence from raw web content. To rigorously evaluate deep reasoning abilities, we introduce ORION, a challenging benchmark focused on open-web reasoning over long-tail entities, covering both English and Chinese. Experimental results show that ManuSearch substantially outperforms prior open-source baselines and even surpasses leading closed-source systems. Our work paves the way for reproducible, extensible research in open deep search systems. We release the data and code in https://github.com/RUCAIBox/ManuSearch.