The rapid growth of the Internet of Things (IoT) has created vast networks of interconnected devices generating continuous data streams, offering opportunities for real-time analytics across domains like smart cities, healthcare, and transportation. On the one hand, traditional knowledge representation methods provide interpretability, precise semantic modeling, and structured reasoning but often struggle with scalability, making predictions, and handling uncertainty. On the other hand, machine learning models offer powerful predictive capabilities and efficient handling of large, uncertain datasets, but typically lack interpretability, adaptability, and require extensive labeled data. Neuro-symbolic AI tries to use the best of both symbolic reasoning and machine learning—combining interpretability and structured inference from symbolic approaches with the predictive power and generalization capabilities of machine learning. Existing neuro-symbolic approaches, such as DeepProbLog and NeurASP, although successful in general logical programming contexts, they do not natively support streaming data or querying over RDF. This paper introduces Kolibrie, a hybrid stream reasoning engine developed in Rust that integrates both at the language and system level (1) SPARQL for querying, (2) Datalog-style rules for symbolic inference, (3) RSP-QL semantics for RDF stream processing and (4) machine learning predictions. Resource type: Software framework License: Mozilla Public License Version 2.0 DOI: https://doi.org/10.5281/zenodo.17808656 URL: https://github.com/StreamIntelligenceLab/Kolibrie