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OpenAgents: An Open Platform for Language Agents in the Wild

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arxiv 2310.10634 v1 pith:IAITZR3C submitted 2023-10-16 cs.CL cs.AI

classification cs.CLcs.AI
keywords languageagentsagentopenagentsdatafoundationopenplatform
verification ladder T0 review T1 audit T2 compute T3 formal
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Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs). Current language agent frameworks aim to facilitate the construction of proof-of-concept language agents while neglecting the non-expert user access to agents and paying little attention to application-level designs. We present OpenAgents, an open platform for using and hosting language agents in the wild of everyday life. OpenAgents includes three agents: (1) Data Agent for data analysis with Python/SQL and data tools; (2) Plugins Agent with 200+ daily API tools; (3) Web Agent for autonomous web browsing. OpenAgents enables general users to interact with agent functionalities through a web user interface optimized for swift responses and common failures while offering developers and researchers a seamless deployment experience on local setups, providing a foundation for crafting innovative language agents and facilitating real-world evaluations. We elucidate the challenges and opportunities, aspiring to set a foundation for future research and development of real-world language agents.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 11 citations worldwide. Full citation record

  1. When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

    cs.CR 2026-07 conditional novelty 6.5 of 10

    GhostWriter poisons tool-using personal agents' long-term memory via untrusted emails/calendar invites (~98% injection, ~60% activation); AM-Sentry policies and retrieval screens sharply reduce success while preservin...

  2. DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    DataClaw0 introduces an agentic data-tailoring paradigm, a 9B model trained on a synthetically generated dataset, and a new benchmark, claiming improved downstream adaptation in video generation, VQA, and GUI navigati...

  3. WebGuard: Building a Generalizable Guardrail for Web Agents

    cs.AI 2025-07 conditional novelty 6.0 of 10

    WebGuard introduces an action-level risk dataset for web agents and shows that a fine-tuned 7B model improves risk-prediction accuracy from about 38% to 80% and high-risk recall from 20% to 76%, still below deployment...

  4. S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner

    cs.AI 2025-08 conditional novelty 5.0 of 10

    S3LoRA prunes LoRA layers with the sharpest spectral update concentration to improve safety in fine-tuned LLM agents without needing base models or extra data.

  5. OAgents: An Empirical Study of Building Effective Agents

    cs.AI 2025-06 reject novelty 5.0 of 10

    OAgents reports a framework and ablation study claiming open-source state-of-the-art on GAIA, but the empirical evidence is weakened by benchmark fitting and missing statistics.

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