What You Can Build

Agents and Automation

• Autonomous AI agents that reason, plan, and execute multi-step tasks using a growing catalog of built-in tools or your custom APIs
• Multi-agent systems with pipeline, parallel, router, and supervisor orchestration patterns
• Research assistants that search the web, analyze results, and synthesize findings using ReAct planning
• Task automation workflows with agent delegation, resilience policies, and comprehensive observability

Document and Knowledge Workflows

• RAG-powered knowledge assistants over local documents, databases, and enterprise data sources
• PDF chat and document Q&A with retrieval, reranking, and grounded generation
• OCR and extraction pipelines for invoices, forms, IDs, emails, and scanned documents
• Intelligent document splitting that detects logical boundaries in multi-page PDFs using vision models

Multimodal and Compliance

• Voice-driven assistants with speech-to-text, reasoning, and function calling
• Compliance-focused text intelligence with PII extraction, NER, classification, and sentiment analysis

Core Capabilities

🤖 AI Agents and Orchestration

Build autonomous AI agents that reason, plan, and execute complex workflows within your applications.

• Agent Framework - Complete agent infrastructure with Agent, AgentBuilder, AgentExecutor, and AgentRegistry
• Multi-Agent Orchestration - Coordinate multiple agents with PipelineOrchestrator, ParallelOrchestrator, RouterOrchestrator, and SupervisorOrchestrator
• Planning Strategies - ReAct, Chain-of-Thought, Tree-of-Thought, Plan-and-Execute, and Reflection
• Agent-to-Agent Delegation - Delegate tasks to specialized sub-agents with DelegationManager and DelegateTool
• Agent Templates - Pre-built templates including Chat, Assistant, Code, Research, Analyst, Planner, and more

• Extensive Built-in Tools - A growing catalog of ready-to-use tools across eight categories (Data, Text, Numeric, Security, Utility, Document, IO, Net), each following the 1 tool = 1 feature atomic design
• MCP Client Support - Connect to Model Context Protocol servers for extended tool access, resources, and prompts
• Function Calling - Let models dynamically invoke your application's methods with structured parameters

• Resilience Policies - Retry, Circuit Breaker, Timeout, Rate Limit, Bulkhead, and Fallback
• Streaming Support - Real-time response streaming with buffered, multicast, and delegate handlers
• Agent Observability - Full tracing and metrics with AgentTracer, AgentMetrics, and JSON export
• Agent Memory - Persistent memory across conversation sessions with RAG-based recall
• Reasoning Control - Adjust reasoning depth for models that support extended thinking

📄 Document Intelligence

Process, extract, and transform documents across PDF, DOCX, XLSX, PPTX, EML, MBOX, HTML, and image formats.

• VLM-Powered OCR - High-accuracy text extraction from images and scanned content using vision language models
• Structured Extraction - Define extraction targets with JSON schemas, custom elements, and pattern constraints
• Confidence Scoring and Validation - Per-element confidence scores, entity auto-detection, format validation, and human verification flags
• Named Entity Recognition (NER) - Extract people, organizations, locations, and custom entity types
• PII Detection - Identify and classify personal identifiers for privacy compliance

• Document Splitting - Detect logical document boundaries in multi-page files using vision-based analysis
• PDF Manipulation - Split, merge, search, extract pages, render to image, and unlock password-protected files
• Format Conversion - Convert between Markdown, HTML, and DOCX
• Layout-Aware Processing - Detect paragraphs and lines, support region-based workflows

📚 Retrieval-Augmented Generation (RAG)

Ground AI responses in your organization's knowledge with a flexible, extensible RAG framework.

• Modular RAG Architecture - Use built-in pipelines or implement custom retrieval strategies
• Built-in Vector Database - Store and search embeddings without external dependencies
• PDF Chat and Document RAG - Chat and retrieve over documents with dedicated workflows
• Multimodal RAG - Retrieve relevant content from both text and images
• Advanced Chunking - Markdown-aware, HTML-aware, semantic, and layout-based chunking strategies
• Reranking - Improve retrieval precision with semantic reranking
• External Vector Store Integration - Connect to Qdrant and other vector databases

🔍 Vision, Speech, and Content Analysis

Process visual, audio, and textual content.

• Vision Language Models (VLM) - Analyze images, extract information, answer questions about visual content
• Image Embeddings - Generate semantic representations of images for similarity search
• Speech-to-Text - Transcribe audio with voice activity detection and multi-language support

• Sentiment and Emotion Analysis - Detect emotional tone from text and images
• Custom Classification - Categorize text and images into your defined classes
• Language Detection - Identify languages from text, images, or audio
• Summarization - Condense long content with configurable strategies
• Keyword Extraction - Identify key terms and phrases

✍️ Text Generation and Transformation

Generate and refine content with precise control.

• Conversational AI - Build context-aware chatbots with multi-turn memory
• Constrained Generation - Guide model outputs using JSON schemas, templates, or custom grammar rules
• Translation - Convert text between languages with confidence scoring
• Text Enhancement - Improve clarity, fix grammar, adapt tone

🛠️ Model Customization

Tailor models to your specific domain.

• Fine-Tuning - Train models on your data with LoRA support
• Dynamic LoRA Loading - Switch adapters at runtime without reloading base models
• Quantization - Optimize models for your deployment constraints
• Training Dataset Tools - Prepare and export datasets in standard formats

Supported Models

LM-Kit.NET ships domain-tuned models optimized for real-world tasks and supports a wide range of open models across four modalities:

• Text - Chat, reasoning, code generation, and tool calling
• Vision - Image understanding, visual Q&A, and VLM-powered OCR
• Embeddings - Semantic search and retrieval
• Speech - Transcription with voice activity detection

[!NOTE] New model families are added continuously. Browse the full list in the Model Catalog, or load any compatible model directly from Hugging Face.

Performance and Hardware

Hardware Acceleration

LM-Kit.NET automatically leverages the best available acceleration on any hardware:

• NVIDIA GPUs - CUDA backends with optimized kernels
• AMD/Intel GPUs - Vulkan backend for cross-vendor GPU support
• Apple Silicon - Metal acceleration for M-series chips
• Multi-GPU - Distribute models across multiple GPUs
• CPU Fallback - Optimized CPU inference when GPU unavailable

Dual Backend Architecture

Choose the optimal inference engine for your use case:

• llama.cpp Backend - Broad model compatibility, memory efficiency
• ONNX Runtime - Optimized inference for supported model formats

Observability

Gain full visibility into AI operations with comprehensive instrumentation:

• OpenTelemetry Integration - GenAI semantic conventions for distributed tracing and metrics
• Inference Metrics - Token counts, processing rates, generation speeds, context utilization, perplexity scores, and sampling statistics
• Event Callbacks - Fine-grained hooks for token sampling, tool invocations, and generation lifecycle

Platform Support

Operating Systems

• Windows - Windows 7 through Windows 11
• macOS - macOS 11+ (Intel and Apple Silicon)
• Linux - glibc 2.27+ (x64 and ARM64)

.NET Frameworks

Compatible from .NET Framework 4.6.2 through the latest .NET releases, with optimized binaries for each version.

Integration

Zero Dependencies

LM-Kit.NET ships as a single NuGet package with absolutely no external dependencies:

[!TIP]

dotnet add package LM-Kit.NET

No Python runtime. No containers. No external services. No native libraries to manage separately. Everything runs in-process.

Ecosystem Connections

• Microsoft Semantic Kernel - Use LM-Kit.NET as a local inference provider for Microsoft Semantic Kernel via the open-source LM-Kit.NET.SemanticKernel bridge package
• Microsoft.Extensions.AI - Plug LM-Kit.NET into any .NET application that targets the standard IChatClient and IEmbeddingGenerator abstractions via the open-source LM-Kit.NET.ExtensionsAI bridge package
• Vector Databases - Integrate with Qdrant via open-source connectors
• MCP Servers - Connect to Model Context Protocol servers for extended tool access

Data Privacy and Security

Running inference locally provides inherent security advantages:

• No data transmission - Content never leaves your network
• No third-party access - No external services process your data
• Audit-friendly - Complete visibility into AI operations
• Air-gapped deployment - Works in disconnected environments

This architecture simplifies compliance with GDPR, HIPAA, SOC 2, and other regulatory frameworks.

Getting Started

Basic Chat

AI Agent with Tools