LM-Kit.NET

LM-Kit: C# LLM integration for .NET apps is a comprehensive set of C#/.NET toolkits and SDKs designed to integrate language models and generative AI capabilities into your applications. It focuses on edge and on-device processing, multimodal support, AI agent orchestration, and high performance across diverse hardware. The solution helps developers build AI agents, Q&A systems, text generation, translation, summarization, and more with native .NET tooling, without heavy server dependencies when not needed. It emphasizes privacy by enabling local data processing and offers tools for model optimization, fine tuning, and seamless integration into existing C# and VB.NET projects. It also provides ready-to-use demos, documentation, and a robust ecosystem of plugins, connectors, and model repositories to accelerate development.

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How to Use LM-Kit

1. Install LM-Kit packages into your .NET project via NuGet or download the community edition.
2. Choose a model or load a local/edge capable GenAI model suitable for your app.
3. Integrate AI agents using the LM-Kit runtime to create multi-turn Q&A, text generation, translation, embeddings, and more.
4. Enable on device processing to minimize latency and improve privacy while optionally leveraging cloud resources when needed.
5. Deploy and test with native SDKs and hardware optimizations for your target platform.

Core Capabilities

• Multimodal Generative AI systems for .NET: text and image analysis and generation
• AI Agents orchestration: create intelligent, adaptable agents with memory, retrieval augmented generation, and function calling
• Rich text processing: generation, rewriting, translation, summarization, grammar and spell correction, sentiment and emotion analysis, keyword extraction
• Data extraction and structured output: extraction schemes for precise data from sources
• Embeddings and semantic tools: convert text to vectors for similarity, search, and clustering
• Custom classification and sentiment analysis: tailored to your domain
• Code and data tooling: code analysis, data connectors, and model optimization
• Model optimization: quantization, LoRA integration, fine tuning for efficiency
• On device and edge inference: native SDKs with Metal, AVX, CUDA, and GPU acceleration options
• Privacy and security: local data processing by default, with cloud options as needed

Features by Category

• Native SDKs for seamless AI integration in .NET apps
• On‑device GenAI processing for low latency and privacy
• Multimodal support: text and image analysis and generation
• Agent memory and context management for richer interactions
• Retrieval-augmented generation for informed responses
• Function calling to interact with your app's APIs
• Embeddings, structured data extraction, and keyword analysis
• Text generation, rewriting, translation, and summarization
• Language detection and sentiment/emotion analysis
• Code analysis and developer tooling integration
• Model quantization, fine tuning, and LoRA integration for efficiency
• Cross platform: optimized for ARM, x86, and GPUs with CPU/GPU hybrid inference
• Zero dependency approach with native .NET tooling
• Community resources: demos, blogs, and tutorials

How It Works

• Integrate LM-Kit into your .NET project
• Load or connect to a suitable LLM or small language model
• Build AI agents and orchestrate tasks across multiple components
• Use on-device inference to minimize latency and protect data
• Extend capabilities with embeddings, RAG, and custom extraction pipelines

Safety and Legal Considerations

• Use responsibly for legitimate AI tasks
• Respect privacy and data handling guidelines in your region
• Ensure proper user consent when processing sensitive data

Core Components and Resources

• LM-Kit.NET Demo: sample implementations of AI agent orchestration
• LM-Kit Maestro: advanced orchestration framework for multi-agent scenarios
• Semantic Kernel Plugin: integration with semantic kernel workflows
• Data Connectors and Hugging Face model repository: model sources and adapters
• NuGet packages: plantable building blocks for rapid development
• Documentation: getting started guides, API references, and tutorials
• Community and blog: insights and updates on GenAI with C#

Why LM-Kit

• Edge Gen-AI processing with native SDKs results in reduced latency and better security
• Seamless integration with existing .NET apps and familiar languages
• Broad feature coverage for building robust AI enhanced applications
• Flexible deployment options across devices, on-prem, and cloud
• Active community and ongoing improvements to stay at the forefront of GenAI in .NET

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Example Use Cases

• AI powered chatbots and Q&A assistants with context memory
• Content generation and editing tools inside an IDE or editor
• Document understanding and extraction pipelines
• Multimodal apps that analyze and generate both text and images
• Enterprise workflows that automate knowledge work with AI agents