Webhell: A Unified Interface for Agentic Web Access
On the map: LLM agents and tool use → Web, search, and research agents also fits: General LLM agent frameworks and benchmarks
Abstract (arXiv)
Large language model (LLM) agents are becoming direct users of the open web. As they take on increasingly complex and information-intensive tasks, effective web access is becoming a core capability for agentic problem solving. Existing approaches have advanced agentic web access through different access models, including search-augmented agents, browser-based agents, and, more recently, filesystem-based agents. Nevertheless, as users expect agents to complete long-horizon, end-to-end tasks, they increasingly need to conduct broad information gathering, perform browser actions, and revisit acquired evidence within a single trajectory. Yet our preliminary analyses reveal that existing access models struggle to meet these demands. They primarily support either information gathering or browser interaction, while combining their capabilities can involve substantial costs or performance trade-offs. To this end, we present Webhell, an interface that unifies web access under a single access model. Using a common schema defined by WebContract, WebCompiler compiles live webpages into addressable information and action objects. These objects are maintained in WebCache and handled through a shared set of operations, providing agents with consistent, operable, and persistent web access. Through extensive experiments across web search, web navigation, and deep research benchmarks, we show that Webhell achieves strong task performance at lower cost. Our results further indicate the importance of interface design for effective web access, with Webhell opening new possibilities for more capable and efficient LLM agents on the web.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 31 of its 35 references that are in the map. First posted on arXiv 9 Oct 2026.
Builds on
31 of 35 references.
Closest ancestors · 18
Same direction, newest first
- 2026Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion12 Jun
- 2026WebChallenger: A Reliable and Efficient Generalist Web Agent9 Jun
- 2026Region4Web: Rethinking Observation Space Granularity for Web Agents8 May
- 2026Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction3 May
- 2026AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web13 Apr
- 2025MCP vs RAG vs NLWeb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report)28 Nov
- 2025Prune4Web: DOM Tree Pruning Programming for Web Agent26 Nov
- 2025WideSearch: Benchmarking Agentic Broad Info-Seeking11 Aug
- 2025DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents13 Jun
- 2025Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents12 Mar
- 2025Search-o1: Agentic Search-Enhanced Large Reasoning Models9 Jan
- 2024AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents17 Oct
6 more
- 2024Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation17 Oct
- 2024Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems17 Jul
- 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models25 Jan
- 2024GPT-4V(ision) is a Generalist Web Agent, if Grounded3 Jan
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agents25 Jul
- 2023Mind2Web: Towards a Generalist Agent for the Web9 Jun
Other references · 13
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
- 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
- 2025WebThinker: Empowering Large Reasoning Models with Deep Research Capabilitycited by 335
- 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 309
- 2025DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environmentscited by 286
- 2025WebWalker: Benchmarking LLMs in Web Traversalcited by 175
- 2025SealQA: Raising the Bar for Reasoning in Search-Augmented Language Modelscited by 75
- 2026Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnessescited by 10
- 2026CLI-Anything: Towards Agent-Native Computer Usecited by 9
- 2026AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shoppingcited by 7
- 2026HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harnesscited by 3
- 2026DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivationcited by 1
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