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LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. Deploy to the cloud or on-premise.

...For more information about dnrDALMAS, see the following technical report: Hjelmblom, M. (2008). Deontic action-logic multi-agent systems in Prolog. University of Gävle, Division of Computer Science; University of Gävle. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-1475 See also: Odelstad, J., & Boman, M. (2004). Algebras for Agent Norm-Regulation. Annals of Mathematics and Artificial Intelligence, 42(1), 141–166. http://doi.org/10.1023/B:AMAI.0000034525.49481.4a

Jason [http://jason.sourceforge.net/] is an AgentSpeak interpreter for multi-agent system development. The agents are describbed in AgentSpeak, but the environment requires a Java description of how the actions and perceptions happen. We aim to close the gap between the descriptions with an AgentSpeak-like description of the environment for new users and simpler examples.