A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, with a focus on causal discovery tools, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader’s understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, with a focus on causal discovery tools, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader’s understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it. Keywords: structure learning, parameter learning, causal discovery algorithms, causal discovery, bayesian networks (BNs) status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2025 May 20; Accepted 2025 Aug 8; Collection date 2025. 1 Introduction Bayesian networks (BNs) have established themselves over the years as a powerful framework for modeling and analyzing complex systems under conditions of uncertainty. A causal network is a specific type of Bayesian network where the edges reflect actual causal influences among variables, and their interpretation relies on assumptions such as causal sufficiency, faithfulness, and the absence of unmeasured confounding. Throughout this paper, we include structure learning algorithms developed for both probabilistic modeling and causal discovery. For a detailed discussion of the assumptions underlying causal discovery, we refer the reader to ( Vonk et al., 2023 ). To speed up or improve structure learning, prior knowledge can be incorporated to constrain or guide the search for the network structure. Users may specify relationships that are known to exist, permitted, or prohibited, thereby reducing the search space and enhancing both the accuracy and efficiency of learning algorithms. An overview of structure learning approaches is beyond the scope of this document; a comprehensive assessment of state-of-the-art methodologies can be found in ( Nogueira et al., 2022 ; Kitson et al., 2023 ; Glymour et al., 2019 ; Scanagatta et al., 2019 ). This document assumes that the reader is equipped with the necessary foundational knowledge and is ready to engage in practical hands-on work. Over the past 5 years, the field of causality and BNs development has seen an influx of numerous packages with no single solution being able to cater to all requirements and scenarios; this abundance of options is often challenging for individuals trying to gain hands-on experience with BNs. This document simplifies structure and parameter learning in BNs by providing a comprehensive overview of available software packages with a focus on causal discovery. In addition, we offer our subjective recommendations on selecting the best tools based on the reader’s specific objectives. The remainder of this paper is structured as follows: Section 2 provides a systematic review of both open-source and commercial software. Section 3 offers guidance on selecting tools suitable for beginners. Section 4 summarizes the key contributions of this work. A concise summary of all reviewed tools is provided in Supplementary Table S1 ( Supplementary Material ). 2 Software tools and packages 2.1 gCastle gCastle ( Zhang et al., 2021 ) is an end-to-end Python toolbox created by Huawei Noah’s Ark Lab for causal structure learning. The first subsection focuses on causal discovery tools, while the second presents tools that support functionalities for both parameter learning and structure learning for the Bayesian network framework. Finally, the last subsection discusses commercial software that This might not apply to BayesiaLab, as users cannot try the software on the website before purchasing it. Additionally, BayesiaLab can only be used with Java. 4 Conclusion This paper provides an overview of recent tools and software packages for Bayesian network structure and parameter learning, as well as methods specifically developed for causal discovery. The tools were reviewed from the perspective of a beginner seeking to gain hands-on experience in the field, and subjective recommendations were given about which tools are deemed more suitable. At the same time, it is important to acknowledge that the current landscape of BN tools remains fragmented.