Go to OpenReview Archive homepageOpenClaw Research: A Systematic Survey of Large Language Model Agents in Open Deployment
Abstract: Autonomous agents powered by large language models are moving from curated demos to persistent, open-world deployment.
The rapid rise of OpenClaw, an open-source project that became one of the most starred in GitHub history, makes this transition concrete:
agents can now run continuously, operate across heterogeneous platforms, and use community-contributed skills outside fully curated environments.
This shift breaks the sandbox assumptions that have dominated
prior agent research, including developer-controlled model updates, trusted tools, constrained environments, and short-lived execution.
We present the first systematic survey of OpenClaw Research, defined as the study of agent systems after they enter open deployment.
We formalize this setting through an agent-system tuple $\mathcal{A}=\langle \pi,\mathit{env},\mathit{pop},\mathit{substrate}\rangle$ and derive four principles of openness: Open Policy, Open Environment, Open Population, and Open Substrate.
These principles structure the taxonomy around five research areas:
Learning \& Evolving, Safety \& Security, Claw Society, Infrastructure \& Systems, and Applications.
Across these areas, we review representative work, identify emerging risks such as malicious skill supply chains and autonomy--accountability gaps, and highlight open challenges that arise in open, continuously deployed agent systems.
This survey provides a roadmap for understanding and governing LLM agents as they move beyond laboratory settings into large-scale open deployment, ultimately laying the groundwork for a trustworthy and sustainable agent ecosystem.
To support ongoing research in this field, we maintain https://github.com/shuolucs/Awesome-OpenClaw-Research.
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