ChainClaw让AI代理安全执行链上操作，解决状态、对抗、不可逆三大难题。
ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution
- 分层架构：事件驱动+模拟安全+链上监控，跨层共享记忆
- 在7类任务中，安全性和完成率均超越基线模型
- 适合需要高可靠性的智能合约自动化场景
通用大语言模型代理在工具增强任务中表现强劲，但在区块链环境中因假设失效而面临挑战。链上执行具有状态性、对抗性和经济不可逆性，暴露反应性、不可逆性和可观测性三大根本缺陷。我们提出ChainClaw，一个基于OpenClaw的原生区块链代理框架，通过三层架构——事件驱动编排层、基于模拟的安全智能层、链上监控运行时层——并由跨层记忆系统统一整合，分别解决三类问题：通过事件摄入与模拟反馈修复反应性，通过预执行安全流水线（交易模拟+动作守卫）应对不可逆性，通过链上读取适配器和交易监控实现可观测性。我们在自建基准上评估了七项任务，覆盖四类场景与五个维度，ChainClaw在安全性和任务完成度上均持续优于代表性基线。
原文摘要 · Abstract (English)
General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: Reactivity, Irreversibility, and Observability. We propose ChainClaw, a blockchain-native agent framework built on OpenClaw, that addresses all three gaps through a layered architecture comprising an event-driven orchestration layer, a simulation-based safety intelligence layer, and an on-chain monitoring runtime layer, unified by a cross-layer memory subsystem. ChainClaw closes the Reactivity gap via event ingestion and simulation feedback, the Irreversibility gap via a pre-execution safety pipeline with transaction simulation and action guard, and the Observability gap via an on-chain read adapter and transaction monitor. We evaluate ChainClaw on a purpose-built benchmark covering seven tasks across four categories and five dimensions. ChainClaw consistently outperforms representative baselines on both safety and task completion.
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