Moreover, in chemistry -like in almost all scientific disciplines -the vast majority of the knowledge that has been generated over the past centuries is only available in the form of unstructured natural language in books or scientific publications rather than in structured, machinereadable and readily interoperable data. For these reasons, natural language processing (NLP) is an obvious choice for generating automated synthesis workflows on robotic platforms such as SDLs and MAPs, either directly from user input, or from previously published experimental procedures. There are several examples, some as early as 2011 , in which rule-based algorithms, part-ofspeech (POS) tagging, and named entity recognition (NER) are used to create structured output from unstructured experimental procedures. While POS and NER can also be done using neural network architectures like convolutional neural networks (CNN) and recurrent neural networks (RNN) such as long-short-term-memory (LSTM) or gated recurrent unit (GRU) networks instead of purely rule-based approaches, the introduction of the transformer architecture and with it the advent of LLMs such as the GPT model family revolutionized the task of NLP. In general, transformer-based LLMs are more robust against grammatical or typographical errors than rule-based approaches and make the challenge of formulating precise yet generic rules that encompass all possible ways language can be used to convey the same meaning obsolete due to their high dimensional latent embeddings and their attention mechanisms. Consequently, most recent efforts of integrating NLP with MAPs or SDLs are focused on using LLMs. Following the approach of generating action graphs from experimental procedures that can be executed on robotic hardware, Vaucher et al. demonstrated the usefulness of LLMs for this task with a custom encoder-decoder transformer model with eight attention heads that can generate executable actions for a proprietary hardware backend. Yoshikawa et al. used automated iterative prompting between a generator that queries proprietary OpenAI LLMs and a rule-based verifier for generating executable XDL code on robotic hardware in a MAP. In both cases, the weights of the fully trained neural networks are proprietary and not publicly available, and the networks are hosted on third party servers. However, there are certainly cases in which it is beneficial to have full control over the data in one's own IT infrastructure, for example when considering especially sensitive data, intellectual property data, or when running the software on PC systems that are not or cannot be connected to the internet for security reasons to query online resources. Moreover, making the fully trained LLMs publicly available gives other researchers the possibility to fine-tune the models according to their specific needs or domain-specific language. With the recently published Llama series of LLMs, 19 very large, fully trained networks are made publicly available, however, these are prohibitively memory-consuming and slow when attempting to run them on standard, consumergrade hardware typically used in edge computing and commonly used laboratory IT infrastructure. Hence, there is a need for fully open, publicly available LLMs that strike a balance between performance, generality, and fitness for purpose for the task of automatically generating workflows and executable code from natural language inputs. For this reason, the fine-tuning and comparison of two different, pre-trained encoder-decoder transformer "surrogate" LLMs for the task of generating action graphs from experimental procedures written in unstructured, natural language are investigated and described in detail here. The training data for these models was compiled from over 1.5 million publicly available experimental procedures from the patent literature and annotated using a rule-based approach, as well as more general, but also much larger LLMs. The performance of the fully trained surrogate models is evaluated and compared using different metrics, and the fully trained models are made freely publicly available. Additionally, the ability of the surrogate models to adapt to experimental procedures from the domains of materials science, organic chemistry, inorganic chemistry, and patents that were not part of the training or evaluation dataset are discussed. The action graphs generated by these models follow a simple markup language, i.e., they represent structured output with a clear vocabulary and syntactic rules. In principle, such intermediate output can readily be turned into executable code by either a rule-based custom "compiler" or by another LLM. Here, the first approach is followed, albeit with an intermediate step. Rather than turning the action graphs generated from the natural language input directly into executable python code, a node setup in a node editor of a graphical user interface is generated first. Such node editors are often used for creating or representing workflows in a more accessible or "visual" way compared to classical, textbased programming. Representing the action graphs this way makes it easier for users without in-depth programming knowledge to modify or adjust the automatically generated action graphs in an intuitive way. Moreover, the node editor can also be used in a stand-alone way to build entirely new workflows from scratch. Finally, the nature of the node-based setup with different input and output fields that are grouped hierarchically under one node, in combination with the underlying inheritance structure of the entities corresponding to nodes and input and output fields in an object-oriented programming language and the software architecture of the SDL backend, lend themselves excellently to the generation of knowledge graphs from these intermediate node graphs, which is also investigated in this work. It should be noted that these automatically generated knowledge graphs follow an ontology that is (currently) imposed by the underlying architecture of the software backend of the SDL. However, in the future, a common underlying ontology used for this type of representation could help to further harmonize and enhance sharing synthesis workflows even across different MAP or SDL platforms.