Advanced International Journal for Research

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Openclaw Echo

Author(s) Ms. Roqaiah Anjum E, Ms. Mokshita N, Ms. Ambika Ambika
Country India
Abstract Agents based on large language models no longer answer a prompt. They plan, call tools, and keep memory across steps. That creates engineering problems that a plain text generator never faces. For example, how much context should be kept? What happens if a provider fails halfway through a task? How can a failed step be recovered from crashing the run? SALLMA is one of the papers that treats this at the software-architecture level, but its validation is only preliminary and qualitative. This paper describes OpenClaw Echo, a local-first agent framework that uses a waterfall router across model providers, a hybrid memory layer that includes relational records, semantic vectors, and a small knowledge graph, role-specialized sub-agents and a verification stage that triggers bounded recovery. SALLMA is used as an architectural reference; none of the individual mechanisms is claimed as new. The aim is to see how these mechanisms behave when built and measured together. Project-defined tests gave 96.2 percent intent-classification accuracy, 91.4 percent precision at five, and 88.7 percent recall at five for retrieval. Recovery from injected provider failures was 99.8 percent. The average latency was 0.85 seconds for requests and 8.4 seconds for delegated tasks. These results show that one coordinated pipeline is better than a set of features, but it has a clear latency cost when deeper processing is used. Limitations, dependence on external providers, and the small self-built evaluation sets are discussed. OpenClaw Echo demonstrates this approach.
Keywords Autonomous Agents, Large Language Models, Multi-Agent Systems, Hybrid Memory, Failover Routing
Field Engineering
Published In Volume 7, Issue 6, November-December 2026
Published On 2026-11-13
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E-ISSN 3048-7641
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