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Techniques for reusing a single base model instance across multiple agent personas or configurations to reduce memory overhead.
Distinct from Singleton Instance Sharing: None of the candidates cover the specific pattern of sharing a base LLM weights instance via different prompt templates for multiple agents.
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LazyLLM is a multi-agent framework and orchestration engine designed for building complex AI applications. It provides a system for chaining large language models into sequential or parallel pipelines, utilizing a tool registry to convert standard functions into discoverable tools that models can invoke via reasoning. The project features an application deployment kit that enables hosting model workflows as web services with integrated chat interfaces and API gateways. It includes an infrastructure abstraction layer that allows users to switch between bare-metal servers, clusters, and public
Creates multiple specialized agent configurations using a single base model to save memory by applying different prompt templates.