title: "LM-Kit.NET - Local AI Agent Platform for .NET Developers"
Local AI Agent Platform for .NET Developers
Your AI. Your Data. On Your Device.
LM-Kit.NET is a very unique full-stack AI framework for .NET that unifies everything you need to build and deploy AI agents with zero cloud dependency and zero external dependencies. It combines the fastest .NET inference engine, production-ready trained models, agent orchestration, RAG pipelines, and MCP-compatible tool calling in a single in-process SDK for C# and VB.NET. That makes LM-Kit.NET a category of one in the .NET ecosystem.
🔒 100% Local ⚡ No Signup 🌐 Cross-Platform
Why LM-Kit.NET
A complete AI stack with no moving parts. LM-Kit.NET integrates inference, models, orchestration, and RAG into your .NET application as a single NuGet package. No Python runtimes, no containers, no external services, no dependencies to manage. Everything runs in-process.
Built by experts, updated continuously. Our team ships the latest advances in generative AI, symbolic AI, and NLP research directly into the SDK. Check our changelog to see the pace of innovation.
Not every problem requires a massive LLM. Dedicated task agents deliver faster execution, lower costs, and higher accuracy for specific workflows.
Complete data sovereignty - sensitive information stays within your infrastructure
Zero network latency - responses as fast as your hardware allows
No per-token costs - unlimited inference once deployed
Offline operation - works without internet connectivity
Regulatory compliance - meets GDPR, HIPAA, and data residency requirements by design
What You Can Build
Autonomous AI agents that reason, plan, and execute multi-step tasks using 56 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
RAG-powered knowledge assistants over local documents, databases, and enterprise data sources
PDF chat and document Q&A with retrieval, reranking, and grounded generation
Task automation workflows with agent delegation, resilience policies, and comprehensive observability
Voice-driven assistants with speech-to-text, reasoning, and function calling
OCR and extraction pipelines for invoices, forms, IDs, emails, and scanned documents
Compliance-focused text intelligence - PII extraction, NER, classification, sentiment analysis
Core Capabilities
LM-Kit.NET delivers a complete AI stack: the fastest .NET inference engine, domain-tuned models that solve real-world problems out of the box, and a comprehensive orchestration layer for building agents and RAG applications.
🤖 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 for building production-ready AI agents
Multi-Agent Orchestration - Coordinate multiple agents with PipelineOrchestrator, ParallelOrchestrator, RouterOrchestrator, and SupervisorOrchestrator
Planning Strategies - Multiple reasoning approaches including ReAct, Chain-of-Thought, Tree-of-Thought, Plan-and-Execute, and Reflection handlers
56 Built-in Tools - Ready-to-use tools across categories: Data (JSON, XML, CSV, YAML), Text (Diff, Regex, Template), Numeric (Calculator, Stats), Security (Hash, Crypto, JWT), IO (FileSystem, HTTP, Network), and more
Agent-to-Agent Delegation - Enable agents to delegate tasks to specialized sub-agents with DelegationManager and DelegateTool
Agent Templates - 18 pre-built templates including Chat, Assistant, Code, Research, Analyst, Planner, and more for rapid development
Resilience Policies - Production-grade reliability with Retry, Circuit Breaker, Timeout, Rate Limit, Bulkhead, and Fallback policies
Streaming Support - Real-time response streaming with buffered, multicast, and delegate handlers
Agent Observability - Full tracing and metrics with AgentTracer, AgentMetrics, and JSON export capabilities
MCP Client Support - Connect to Model Context Protocol servers for extended capabilities including resources, prompts, and tool discovery
Agent Memory - Persistent memory that survives across conversation sessions with RAG-based recall
Reasoning Control - Adjust reasoning depth for models that support extended thinking
Function Calling - Let models dynamically invoke your application's methods with structured parameters
🔍 Multimodal Intelligence
Process and understand content across text, images, documents, and audio.
Vision Language Models (VLM) - Analyze images, extract information, answer questions about visual content
VLM-Based OCR - High-accuracy text extraction from images and scanned content
Speech-to-Text - Transcribe audio with voice activity detection and multi-language support
Document Processing - Native support for PDF, DOCX, XLSX, PPTX, HTML, and image formats
Image Embeddings - Generate semantic representations of images for similarity search
📚 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, semantic, and layout-based chunking strategies
Reranking - Improve retrieval precision with semantic reranking
External Vector Store Integration - Connect to Qdrant and other vector databases
📊 Structured Data Extraction
Transform unstructured content into structured, actionable data.
Schema-Based Extraction - Define extraction targets using JSON schemas or custom elements
Named Entity Recognition (NER) - Extract people, organizations, locations, and custom entity types
PII Detection - Identify and classify personal identifiers for privacy compliance
Multimodal Extraction - Extract structured data from images and documents
Layout-Aware Processing - Detect paragraphs and lines, support region-based workflows
💡 Content Intelligence
Analyze and understand text and visual content.
Sentiment and Emotion Analysis - Detect emotional tone from text and images
Custom Classification - Categorize text and images into your defined classes
Keyword Extraction - Identify key terms and phrases
Language Detection - Identify languages from text, images, or audio
Summarization - Condense long content with configurable strategies
✍️ 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 includes domain-tuned models optimized for real-world tasks, plus broad compatibility with models from leading providers:
Text Models: LLaMA, Mistral, Mixtral, Qwen, Phi, Gemma, Granite, DeepSeek, Falcon, and more
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:
dotnet add package LM-Kit.NET
No Python runtime. No containers. No external services. No native libraries to manage separately. The entire AI stack runs in-process within your .NET application, making deployment as simple as any other NuGet package.
Ecosystem Connections
Semantic Kernel - Use LM-Kit.NET as a backend for Microsoft Semantic Kernel
Vector Databases - Integrate with Qdrant via open-source connectors
MCP Servers - Connect to Model Context Protocol servers for extended tool access
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
using LMKit.Model;
using LMKit.TextGeneration;
// Load a model
var model = new LM("path/to/model.gguf");
// Create a conversation
var conversation = new MultiTurnConversation(model);
// Chat
var response = await conversation.SubmitAsync("Explain quantum computing briefly.");
Console.WriteLine(response);
AI Agent with Tools
using LMKit.Model;
using LMKit.Agents;
using LMKit.Agents.Tools.BuiltIn;
// Load a model
var model = new LM("path/to/model.gguf");
// Build an agent with built-in tools
var agent = Agent.CreateBuilder(model)
.WithSystemPrompt("You are a helpful research assistant.")
.WithTools(tools =>
{
tools.Register(BuiltInTools.WebSearch);
tools.Register(BuiltInTools.Calculator);
tools.Register(BuiltInTools.DateTime);
})
.WithPlanning(PlanningStrategy.ReAct)
.Build();
// Execute a task
var result = await agent.ExecuteAsync("What is the current population of Tokyo?");
Console.WriteLine(result.Response);