String: An Agentic OS Where Every App Is a Markdown File
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Abstract (arXiv)
LLM agents have become a new class of software user, but every surface they work through was designed for someone else. Pages are built for human eyes, which can skim and ignore; tool schemas for programs, which pay nothing to carry definitions they never call. An agent has neither luxury: it re-reads, and pays again for, everything it is shown on every turn. We present String, an open-source runtime that gives this user an interface of its own and treats the job as an operating-systems problem. Tool knowledge moves out of the agent's context and into a common layer that renders it back one view at a time as Markdown. A single SFMD (String-Flavored Markdown) document declares an application's views, typed actions, navigation, and credentials, and the runtime handles discovery, validation, execution, state, and secrets behind two core verbs: /open to see and /act to do. Web and app turn out to be two renderings of one architecture: an SFMD site serves styled HTML to browsers and the raw document to agents, so one grammar reaches apps, files, shells, and the web, even legacy HTML, with no per-site integration. Views stay partial by design, and the staging is causal: disclosing one tier of detail a single turn too early costs up to 23 accuracy points, while proper staging drops wrong-action selection from 28% to 2%. Privilege follows provenance: a remote page may call HTTP but never the shell, and caller-supplied text never expands a stored secret. On an 87-task benchmark that pairs each task with curated skills, operationalizing those procedures as on-demand String apps yields comparable aggregate success across six models from frontier to small (+1.3pp) while using 33.5% fewer tokens among completed episodes, and the resident interface stays a constant 53 tokens at any catalog size. We report the design, the evaluation, and what three months of production use taught us.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 28 Aug 2026.
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18 of 31 references.
References · 18
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
- 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,302
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
- 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
- 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 574
6 more
- 2024Executable Code Actions Elicit Better LLM Agentscited by 537
- 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Taskscited by 289
- 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Usecited by 209
- 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 150
- 2024Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrievalcited by 50
- 2025Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Modelscited by 46
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- 2025Coding Agents with Multimodal Browsing are Generalist Problem Solvers
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