This study presents the experimental realization of a chemical reservoir computer based on the formose reaction, demonstrating its ability to perform complex information processing tasks. The formose reaction, a self-organizing chemical network, was used to create a system capable of parallel nonlinear classification, predicting complex dynamical systems, and time-series forecasting. The system leverages the inherent nonlinear dynamics of the reaction network to process information without requiring explicit molecular-level design, offering a new approach to biomimetic information processing. The formose reaction network was implemented in a continuous stirred tank reactor (CSTR), where input variables were controlled by adjusting the flow rates of reactants. The system's output was measured using an ion mobility mass spectrometer, which detected the relative abundance of ions. A single linear read-out layer was trained to convert the reservoir's response into computational outputs, enabling the system to perform various tasks, including classification, prediction, and simulation of complex systems. The formose reservoir computer was tested on nonlinear classification tasks, demonstrating its ability to emulate Boolean logic gates and perform advanced classification tasks such as sine and concentric circle classifications. It outperformed Gaussian process classification for certain tasks and matched the performance of support vector classifiers, multilayer perceptrons, and extreme learning machines for others. The system was also shown to predict the behavior of complex metabolic networks, such as the carbon metabolism of Escherichia coli, with high accuracy. Additionally, the formose reservoir was used to forecast environmental dynamics, showing its ability to predict future states based on past inputs. The system's memory properties were evaluated using mutual information, revealing that certain compounds in the reaction network exhibited short-term memory characteristics. These findings highlight the potential of self-organizing chemical reaction networks for information processing and suggest new opportunities for scalable molecular computing. The study demonstrates that complex chemical reaction networks can perform information processing tasks similar to those of biological systems, offering a promising avenue for the development of biomimetic information processing systems. The results suggest that the formose reaction network could be used to create new types of chemical computers with applications in various fields, including biocomputing and synthetic chemistry.This study presents the experimental realization of a chemical reservoir computer based on the formose reaction, demonstrating its ability to perform complex information processing tasks. The formose reaction, a self-organizing chemical network, was used to create a system capable of parallel nonlinear classification, predicting complex dynamical systems, and time-series forecasting. The system leverages the inherent nonlinear dynamics of the reaction network to process information without requiring explicit molecular-level design, offering a new approach to biomimetic information processing. The formose reaction network was implemented in a continuous stirred tank reactor (CSTR), where input variables were controlled by adjusting the flow rates of reactants. The system's output was measured using an ion mobility mass spectrometer, which detected the relative abundance of ions. A single linear read-out layer was trained to convert the reservoir's response into computational outputs, enabling the system to perform various tasks, including classification, prediction, and simulation of complex systems. The formose reservoir computer was tested on nonlinear classification tasks, demonstrating its ability to emulate Boolean logic gates and perform advanced classification tasks such as sine and concentric circle classifications. It outperformed Gaussian process classification for certain tasks and matched the performance of support vector classifiers, multilayer perceptrons, and extreme learning machines for others. The system was also shown to predict the behavior of complex metabolic networks, such as the carbon metabolism of Escherichia coli, with high accuracy. Additionally, the formose reservoir was used to forecast environmental dynamics, showing its ability to predict future states based on past inputs. The system's memory properties were evaluated using mutual information, revealing that certain compounds in the reaction network exhibited short-term memory characteristics. These findings highlight the potential of self-organizing chemical reaction networks for information processing and suggest new opportunities for scalable molecular computing. The study demonstrates that complex chemical reaction networks can perform information processing tasks similar to those of biological systems, offering a promising avenue for the development of biomimetic information processing systems. The results suggest that the formose reaction network could be used to create new types of chemical computers with applications in various fields, including biocomputing and synthetic chemistry.