Revolutionizing AI Development for C# and .NET
Empowering developers with advanced AI technologies to design and orchestrate AI workflows seamlessly.
Our vision is to lead the industry in integrating AI with enterprise-level applications, driving innovation and efficiency across various sectors. We aim to be at the forefront of AI technology, continually pushing the boundaries of what is possible.
At the heart of Linguistic Agents is a suite of powerful libraries and frameworks designed to make AI development seamless and efficient:
In addition to our core libraries, we offer a range of advanced AI tools that enhance the capabilities of your applications:
Seamlessly access state-of-the-art language models for a variety of tasks such as text completion, translation, and sentiment analysis. This integration allows you to leverage the full power of OpenAI's capabilities directly within your applications, making it easier to implement advanced natural language processing features without needing extensive expertise in AI.
Our comprehensive Graph Library enables the creation and management of complex data structures. It supports structured nodes and edges, with advanced features for vector management and TOML serialization. This allows you to efficiently organize and manipulate large datasets, facilitating advanced analytics and machine learning tasks.
Designed to support non-sequential workflows and conditional branching, our Modular Workflow System streamlines AI application development. This flexibility allows developers to create sophisticated AI workflows that can adapt to varying conditions and inputs, ensuring that your AI applications are both robust and adaptable.
Leverage the power of PyTorch within the .NET ecosystem with TorchSharp. This integration enables advanced deep learning capabilities, allowing you to build and train sophisticated neural networks directly in C#. Whether you're working on image recognition, natural language processing, or any other machine learning task, TorchSharp provides the tools you need to succeed.
Our implementation of Proximal Policy Optimization (PPO) provides a robust framework for reinforcement learning. This allows your applications to learn and optimize decision-making processes in complex environments, improving performance over time through trial and error. PPO is particularly useful for applications in robotics, gaming, and automated trading systems.
Our suite of tools for constructing, training, and running inference on graph-based data sets you up for success in tackling problems involving complex relationships and interdependencies. GNNs are particularly effective in areas such as social network analysis, molecular chemistry, and recommendation systems, where the relationships between data points are just as important as the data points themselves.