Compare LM-Kit.NET vs. LangGraph

LM-Kit.NET introduces sophisticated artificial intelligence capabilities for C# and VB.NET, featuring a robust enterprise architecture and a user-friendly AI Agent Builder. This tool empowers developers to create flexible agents for tasks such as text generation, translation, and context-sensitive decision-making. With integrated runtime support that simplifies the complexities of AI implementation, teams can rapidly prototype, deploy, and expand intelligent solutions, ensuring their software remains responsive to changing data and user requirements.

The AI agents component within LM-Kit.NET empowers developers to design, tailor, and implement agents for various applications such as text creation, translation, code evaluation, and more, all with minimal alterations to the existing codebase. A streamlined runtime and API framework facilitate seamless collaboration among multiple agents, enabling them to share information, allocate tasks, and execute simultaneously. Additionally, the option for on-device processing reduces delays and maintains data privacy, while extensive hardware compatibility ensures that these agents can operate effectively on laptops, edge devices, or cloud GPUs, optimizing performance, cost efficiency, and security.

Developers can seamlessly integrate sophisticated generative AI capabilities into their .NET applications with minimal configuration. This enables functionalities such as chatbots, text creation, content discovery, natural language understanding, translation, and extracting structured information. The on-device inference leverages a combination of CPU and GPU acceleration for swift local processing, ensuring data security. Regular updates incorporate the latest advancements in research, allowing teams to create secure, high-performance AI solutions while enjoying an efficient development process and comprehensive oversight.

LM-Kit.NET empowers .NET developers to enhance large language models using various parameters such as LoraAlpha, LoraRank, AdamAlpha, and AdamBeta1. It integrates efficient optimization techniques and dynamic sample batching to achieve quick convergence. The tool automates quantization, allowing models to be compressed into lower-precision formats, which accelerates inference on devices with limited resources while maintaining accuracy. Additionally, it facilitates the effortless merging of LoRA adapters, enabling the incorporation of new skills in just minutes, avoiding the need for complete retraining. With straightforward APIs, comprehensive guides, and support for on-device processing, LM-Kit.NET ensures a secure and user-friendly optimization process within your existing code framework.

LM-Kit.NET integrates cutting-edge artificial intelligence into C# and VB.NET, enabling you to design and implement context-sensitive agents that execute compact language models on edge devices. This approach reduces latency, enhances data security, and ensures immediate performance, even in environments with limited resources. As a result, both enterprise-level solutions and quick prototypes can be developed and launched more efficiently, intelligently, and dependably.

LM-Kit.NET now empowers your .NET applications to operate the most recent open models directly on your device. This includes advanced models such as Meta Llama 4, DeepSeek V3-0324, Microsoft Phi 4 (along with its mini and multimodal versions), Mistral Mixtral 8x22B, Google Gemma 3, and Alibaba Qwen 2.5 VL. By doing this, you can achieve state-of-the-art capabilities in language processing, vision, and audio without relying on any external services. For easy integration of new models, a regularly updated catalog complete with setup guides and quantized versions is accessible at docs.lm-kit.com/lm-kit-net/guides/getting-started/model-catalog.html. This ensures that you can quickly adopt the latest releases while maintaining low latency and ensuring the complete privacy of your data.

The text generator from LM-Kit.NET operates on both CPU and GPU, enabling fast and secure content generation, summarization, grammar improvement, and style enhancement. With its dynamic sampling capabilities and adjustable grammar rules, it can produce organized outputs like JSON schemas, formatted documents, or code fragments that require minimal editing. Additionally, its efficient resource management ensures low latency and uniform results throughout various workflows.

This .NET chatbot library designed for on-device use introduces advanced multi-turn conversational AI that maintains context while ensuring quick response times and complete privacy. With lightweight models, it eliminates the need for cloud connectivity. You can customize the responses using techniques like RandomSampling or MirostatSampling, and manage token usage with LogitBias and RepetitionPenalty to achieve diverse and non-repetitive responses. Moreover, it features event-driven hooks that allow you to implement personalized logic before or after each message, as well as facilitating human-in-the-loop evaluations when necessary.

LM-Kit.NET empowers C# and VB.NET applications to integrate conversational AI using simplified APIs. It facilitates engaging multi-turn conversations and context-sensitive replies for chatbots, virtual assistants, and customer support agents, providing users with interactions that mimic human dialogue and adjust seamlessly in real-time.

LM-Kit.NET transforms unstructured text and image content into organized data tailored for your .NET applications. Its advanced extraction engine employs dynamic sampling techniques to accurately analyze various formats such as documents, emails, logs, and beyond. You can specify custom fields along with metadata and adaptable formats to suit your needs. Choose between the Parse method for synchronous processing or ParseAsync for asynchronous execution, accommodating any workflow requirements. Retrieval-Augmented Generation connects relevant segments for enhanced search capabilities. The entire process operates locally, ensuring quick performance, robust security, and complete data confidentiality—no registration required.

LM-Kit.NET empowers developers working with C# and VB.NET to seamlessly incorporate both extensive and compact language models for tasks such as natural language comprehension, text creation, engaging in multi-turn conversations, and facilitating rapid on-device inference. Additionally, its vision language models enhance functionality by providing image analysis and captioning capabilities. The embedding models transform text into vector representations, enabling swift semantic searches. Furthermore, the LM-Lit catalog offers a comprehensive list of cutting-edge models, continuously updated, all within a streamlined toolkit that integrates effortlessly into your codebase without disclosing any AI origins to the end user.

The .NET-based on-device Natural Language Generation (NLG) module leverages streamlined local language models to efficiently produce contextually relevant text in a secure manner. It allows for the creation of code snippets, summaries, grammar corrections, and style enhancements directly within your system, ensuring that your data remains confidential. This tool is perfect for automating documentation, maintaining a consistent brand voice, and generating content in multiple languages. With its adaptable controls, you can specify various formats and styles, making it exceptionally suitable for tasks such as reporting, programming code creation, and generating succinct summaries.

The on-device NLP Toolkit designed for .NET efficiently handles extensive text data in a secure and instantaneous manner, ensuring that no information is transmitted to the cloud. Key functionalities encompass multilingual sentiment analysis, detection of emotions and sarcasm, personalized text classification, keyword extraction, and semantic embeddings that provide in-depth contextual understanding. Its dynamic sampling mechanism optimally utilizes both CPU and GPU capabilities to deliver enhanced speed and performance.

With LM-Kit RAG, you can implement context-aware search and provide answers in C# and VB.NET through a single NuGet installation, complemented by an instant free trial that requires no registration. Its hybrid approach combines keyword and vector retrieval, operating on your local CPU or GPU, ensuring only the most relevant data is sent to the language model, significantly reducing inaccuracies, while maintaining complete data integrity for privacy compliance. The RagEngine manages various modular components: the DataSource integrates documents and web pages, TextChunking divides files into overlapping segments, and the Embedder transforms these segments into vectors for rapid similarity searching. The system supports both synchronous and asynchronous workflows, capable of scaling to handle millions of documents and refreshing indexes in real-time. Leverage RAG to enhance knowledge chatbots, enterprise search capabilities, legal document review, and research assistance. Adjusting chunk sizes, metadata tags, and embedding models allows you to optimize the balance between recall and speed, while on-device processing ensures predictable expenses and safeguards against data leakage.

The on-device sentiment analysis tool for .NET provides immediate and confidential insights. It categorizes text into positive, negative, or neutral sentiments, identifies emotions such as happiness, anger, sorrow, and fear, and recognizes sarcasm for more detailed evaluation. Transform unrefined text into valuable intelligence for customer support, social monitoring, marketing initiatives, and product development strategies.