What You Can Build with LM-Kit.NET

Standalone .NET samples across multiple capability areas. Every sample runs entirely on your machine with no cloud API calls, no internet dependency, and no data leaving your infrastructure. Clone the repo, open a project, and run.

Browse all samples on GitHub →

Tip

New to LM-Kit.NET? Start with Your First AI Agent to set up your environment and build a working agent in minutes.

1. Get Running in 60 Seconds

git clone https://github.com/LM-Kit/lm-kit-net-samples.git
cd lm-kit-net-samples

Open any .csproj in your IDE and run. Models download automatically on first launch. No API keys. No configuration files. No cloud accounts.

Platform	GPU Support
Windows x64	CUDA 12/13, Vulkan, CPU
Linux x64 / ARM64	CUDA 12/13, Vulkan, CPU
macOS (Apple Silicon)	Metal, CPU

2. Solve a Real Problem

Find the right sample for your use case:

I need to...	Start with
Have a conversation with a local LLM	Multi-Turn Chat
Build an autonomous agent that reasons and acts	Research Assistant
Ask questions about my documents	Conversational RAG
Chat with a specific PDF	Chat with PDF
Give an agent web search, calculators, and file access	Tool Calling Assistant
Analyze and navigate a codebase with AI	Code Analysis Assistant
Generate and modify code files locally	Code Writing Assistant
Extract structured data from unstructured text	Structured Data Extraction
Parse resumes (text, PDF, or scanned images) into structured profiles	Resume Parser
Extract expense data from receipts (text, PDF, or photos)	Receipt & Expense Scanner
Extract key terms from contracts (text, PDF, or scanned images)	Contract Key Terms Extractor
Detect and redact PII from documents	PII Extraction
Permanently remove PII from PDFs (detect + redact)	Smart Redaction
Classify documents by category	Document Classification
Analyze customer sentiment at scale	Sentiment Analysis
Transcribe audio locally	Speech to Text
Convert scanned documents to searchable Markdown	Document to Markdown
Parse PDFs and scans into typed elements (JSON, HTML, DocLang)	Structured Document Parser
Export the data behind every chart as CSV	Chart Data Harvester
Convert PDFs to archival PDF/A (ISO 19005)	PDF to PDF/A Converter
Extract text and coordinates from images with OCR	VLM OCR with Coordinates
Search text in a PDF or image and highlight matches	Search and Highlight
Build a help desk knowledge base	Help Desk Knowledge Base
Translate content between languages	Text Translator
Turn meeting recordings into action items	Smart Meeting Assistant
Analyze CSV or JSON data locally	Data Analyst Agent
Classify and draft responses to emails	Email Triage Agent
Monitor agent performance with telemetry	Telemetry & Observability

3. Industry Solutions

Healthcare & Life Sciences

Patient data cannot leave your network. Local AI keeps PHI on-premises.

Capability	Sample
Redact patient PII before sharing records	Smart Redaction, PII Extraction, Batch PII Extraction
Transcribe clinical dictations locally	Speech to Text
Generate meeting notes with action items	Smart Meeting Assistant
Extract structured data from lab reports	Structured Data Extraction
Classify medical documents by type	Document Classification

Legal & Compliance

Attorney-client privilege requires that documents stay within your infrastructure.

Capability	Sample
Review contracts and flag compliance issues	Multi-Agent Document Review
Extract named entities from legal filings	Named Entity Recognition
Search case files with natural language	Conversational RAG
Summarize lengthy documents	Document Summarizer
Search contracts for specific terms and highlight matches	Search and Highlight
Extract key terms and risk flags from contracts	Contract Key Terms Extractor

Also see the how-to guide: Process Email Archives for Compliance and Legal Discovery

Finance & Accounting

Regulatory requirements demand auditability and data sovereignty.

Capability	Sample
Extract invoice data from PDFs and images	Invoice Data Extraction
Scan receipts and extract expense data	Receipt & Expense Scanner
Analyze market sentiment from reports	Sentiment Analysis
Classify financial documents by type	Batch Document Classification
Analyze financial data locally with AI	Data Analyst Agent
Detect emotions in customer communications	Emotion Detection

Manufacturing & Field Operations

Field devices and factory floors often have no internet access.

Capability	Sample
Transcribe inspection notes offline	Speech to Text
Extract text from equipment manuals (OCR)	VLM OCR
Build a searchable knowledge base for procedures	Help Desk Knowledge Base
Detect language in multilingual documents	Language Detection

Also see the how-to guide: Build and Deploy an Offline AI Application for Edge Environments

Customer Service

Respond faster with AI that understands your product documentation.

Capability	Sample
Build a support chatbot grounded in your docs	Conversational RAG
Route tickets to the right team	Custom Classification
Detect sarcasm and escalation cues	Sarcasm Detection
Triage and draft email responses	Email Triage Agent
Extract structured info from web content	Web Content Extractor to JSON

4. Architecture Patterns

Each sample demonstrates a proven pattern you can adapt to your application:

Single Agent

One model, one task. The agent reasons, calls tools, and returns a result.

→ Research Assistant · Tool Calling Assistant · Web Search Assistant

Multi-Agent Pipeline

Multiple agents execute in sequence, each transforming the output for the next stage.

→ Content Creation Pipeline · Multi-Agent Document Review · Smart Meeting Assistant

Supervisor Delegation

A supervisor agent routes tasks to specialized worker agents based on the request.

→ Smart Task Router · Email Triage Agent

RAG (Retrieval-Augmented Generation)

Embed documents into vectors, retrieve relevant chunks, and generate grounded answers.

→ Conversational RAG · Single-Turn RAG · Help Desk Knowledge Base · Qdrant Integration · pgvector Integration

Document Processing Pipeline

Ingest documents, extract text and structure, convert formats, and index for search.

→ Structured Document Parser · Document to Markdown · VLM OCR · Search and Highlight · Document Splitting · Document Processing Agent · PDF Digital Signatures

Code Analysis & Generation

Equip a conversation with file system and web search tools to read, analyze, and write code through natural language.

→ Code Analysis Assistant · Code Writing Assistant

External Tool Servers (MCP)

Connect agents to external tool servers via the Model Context Protocol.

→ MCP Integration · MCP Stdio Integration

5. Learning Paths

Beginner: Your First Week

Build confidence with core capabilities.

Single-Turn Chat: send one prompt, get one response.
Multi-Turn Chat: hold a conversation with history.
Sentiment Analysis: classify text by sentiment.
Text Translator: translate between languages.
Speech to Text: transcribe audio locally.

Intermediate: Production Capabilities

Combine multiple features into real applications.

Tool Calling Assistant: give the model tools to call.
Code Analysis Assistant: read files and explore codebases with built-in tools.
Conversational RAG: ground answers in your documents.
PII Extraction: detect and redact sensitive data.
Document to Markdown: convert any document to searchable text.
Persistent Memory Assistant: remember facts across sessions.

Advanced: Enterprise Patterns

Build multi-agent systems with orchestration, delegation, and observability.

Research Assistant: ReAct planning with web search and tool use.
Code Writing Assistant: read, create, and modify source files with built-in tools.
Smart Task Router: supervisor-based delegation across specialist agents.
Content Creation Pipeline: sequential multi-agent workflows.
Filter Pipeline: add middleware guardrails to agent conversations.
Telemetry & Observability: instrument agents with OpenTelemetry.

6. All Samples at a Glance

The catalogue below mirrors the LM-Kit.NET capability pillars and matches the on-disk layout under console_net/<pillar>/<category>/<sample>/. Pillar order follows the website navigation.

AI Agents

Chatbots

Sample	What It Demonstrates
Persona-Driven Chatbot	`AgentTemplates.Chat(model).WithPersonality(...)` minimal loop

Agent Templates

Sample	What It Demonstrates
Agent Templates Catalogue	Chat / Writer / Analyst / ReAct templates on the same prompt

Permissions

Sample	What It Demonstrates
Tool Permission Policies	Safe-chat vs developer profile via `ToolPermissionPolicy`

Streaming

Sample	What It Demonstrates
Streaming Agent Responses	`StreamingAgentExecutor` + `IAsyncEnumerable<AgentStreamToken>`

Resilience

Sample	What It Demonstrates
Resilience Policies	Retry, circuit breaker, fallback, composite policies

Agent Memory

Sample	What It Demonstrates
Multi-Turn Chat with Memory	Agent memory with semantic recall
Persistent Memory Assistant	Long-term knowledge across sessions

Agent Reasoning

Sample	What It Demonstrates
Research Assistant	ReAct planning with web search

Agent Skills

Sample	What It Demonstrates
Skill-Based Assistant	Load agent skills from SKILL.md files

Delegation

Sample	What It Demonstrates
Email Triage Agent	Email classification and response drafting
Smart Task Router	Supervisor-based task delegation

Filter Pipeline

Sample	What It Demonstrates
Filter Pipeline	Middleware guardrails for agent conversations

Graph Orchestration

Sample	What It Demonstrates
Graph Orchestration Showcase	Six-agent draft / review / revise pipeline using composable graph nodes

MCP

Sample	What It Demonstrates
MCP Integration	Connect to external MCP tool servers
MCP Stdio Integration	Stdio-based MCP server connectivity

Multi-Agent Workflows

Sample	What It Demonstrates
Content Creation Pipeline	Sequential multi-agent workflow
Multi-Agent Document Review	Parallel multi-perspective document analysis
Smart Meeting Assistant	Transcription to meeting notes pipeline

Observability

Sample	What It Demonstrates
Agent Telemetry with ActivitySource	OpenTelemetry-compatible distributed tracing for agents
Telemetry & Observability	OpenTelemetry tracing and metrics

Single Function Call

Sample	What It Demonstrates
Function Calling	Register and invoke custom functions

Tools & Function Calling

Sample	What It Demonstrates
Code Analysis Assistant	Code analysis with built-in file system and web search tools
Code Writing Assistant	Code generation and file modification with built-in read/write tools
Data Analyst Agent	Local data analysis with built-in tools
Document Processing Agent	Agent-driven PDF and image processing
News Monitoring Agent	Automated news tracking with web search
Tool Calling Assistant	Custom tool implementation
Web Search Assistant	Real-time web search integration

Document Intelligence

Document Parsing

Sample	What It Demonstrates
Chart Data Harvester	Every chart of a folder of documents read as data and exported as CSV
Parsed Document RAG	Q&A over parsed documents with page citations; parse once, stored as JSON
Structured Document Parser	AI document parser: typed elements exported as Markdown, JSON, HTML and DocLang

Document RAG

Sample	What It Demonstrates
Document RAG	Index a folder of mixed-format documents, chat with citations

Chat with PDF

Sample	What It Demonstrates
Chat with PDF	Interactive PDF Q&A

Layout Understanding

Sample	What It Demonstrates
Layout Understanding	Lines, paragraphs, bounding boxes via VlmOcr + PageElement

OCR

Sample	What It Demonstrates
OCR	Single-image OCR via VlmOcr.Run

Image Processing

Sample	What It Demonstrates
Image Preprocessing	Resize, rotate, auto-crop with `ImageBuffer`

Document Conversion

Sample	What It Demonstrates
Document Conversion to Markdown	Universal converter for PDF / DOCX / XLSX / EML / HTML

PDF Toolkit

Sample	What It Demonstrates
PDF Metadata Inspector	Title, author, permissions, page sizes, security handler
PDF Merge Batch	`PdfMerger.MergeFiles` to combine a list of PDFs
PDF Splitter by Page Range	`PdfSplitter.SplitToFiles` with multi-range syntax
PDF Pages to Image Thumbnails	`PdfRenderer.RenderPagesToFolder` for one file per page (PNG / JPEG / WebP / BMP / TIFF / TGA / PNM)
PDF to Multi-page TIFF Archive	`PdfRenderer.RenderPagesToMultipageTiff` packs all pages into one `.tif`
PDF Page Rotator	`PdfEditor.ApplyToFile` with `PageEdit` rotations
PDF Text Search with Highlights	`PdfSearch.FindText` returning bounding-box matches
PDF to PDF/A Converter	`PdfAConverter.ConvertToFileAsync` (PDF -> PDF/A-1b/2b/3b archival conversion)
Searchable PDF from Scans	`PdfSearchableMaker.ConvertToFile` (PDF -> PDF/OCR)
Multipage TIFF to PDF/A-1B Archive	`ImageToSearchablePdf.ConvertAsync` + `PdfGenerationOptions.Version = PdfA1b` (TIFF -> PDF/A-1B)
Encrypted PDF Workflows	Inspect, render, search, split, edit password-protected PDFs end to end

Smart Redaction

Sample	What It Demonstrates
Smart Redaction	`PiiExtraction` + `PdfRedactor`: detect PII, review it with a human in the loop, then permanently remove it

Email Processing

Sample	What It Demonstrates
Email Processing	Convert `.eml` archives to Markdown for downstream pipelines

Document Classification

Sample	What It Demonstrates
Batch Document Classification	Classify documents at scale
Document Classification	Automatic document type detection

Document Search

Sample	What It Demonstrates
Search and Highlight	Search text in PDFs/images and highlight matches

Document Splitting

Sample	What It Demonstrates
Document Splitting	Intelligent document segmentation

Document Summarization

Sample	What It Demonstrates
Document Summarizer	Automatic document summarization

Document to Markdown

Sample	What It Demonstrates
Document to Markdown	Universal document-to-Markdown engine (PDF, DOCX, PPTX, XLSX, EML, MSG, MBOX, HTML, images)

Structured Data Extraction

Sample	What It Demonstrates
Contract Key Terms Extractor	Extract clauses, parties, dates, and risk flags from contracts and scanned images using VLMs
Invoice Data Extraction	Extract fields from invoices
Receipt & Expense Scanner	Extract expense data from receipts and photos using VLMs
Resume Parser	Extract candidate profiles from resumes and scanned images using VLMs

Vision & Multimodal

Image Understanding

Sample	What It Demonstrates
Image Understanding	Caption, describe, count, and reason about an image with a VLM

Image Classification

Sample	What It Demonstrates
Zero-Shot Image Classification	Classify into a fixed list of categories with no training data

Image Labeling

Sample	What It Demonstrates
Multi-Label Image Tagging	Generate 5-10 short tags per image with a VLM

Chat with Image

Sample	What It Demonstrates
Multi-Turn Chat with Vision	Image understanding in conversations

Image Embeddings

Sample	What It Demonstrates
Image Similarity Search	Find visually similar images

VLM OCR

Sample	What It Demonstrates
VLM OCR	Vision-based OCR
VLM OCR with Coordinates	OCR with bounding box positions

Background Removal

Sample	What It Demonstrates
Background Removal	On-device transparent-background PNG output with the bundled U2-Net

RAG & Knowledge

Embeddings

Sample	What It Demonstrates
Text Embeddings	Vectorise a query and corpus, rank by cosine similarity

Query Expansion

Sample	What It Demonstrates
Query Expansion (Multi-Query and HyDE)	Compare `Original`, `MultiQuery`, and `HypotheticalAnswer` retrieval modes

Reranker

Sample	What It Demonstrates
Reranker	Cross-encoder rerank on top of embedding retrieval

Data Extraction

Sample	What It Demonstrates
Structured Data Extraction	Schema-driven field extraction
Web Content Extractor to JSON	Parse web content into structured JSON

RAG Chat

Sample	What It Demonstrates
Conversational RAG	Multi-turn retrieval-augmented generation
Help Desk Knowledge Base	Build a searchable support system
Retrieval Quality Tuning	Optimize retrieval precision and recall
Single-Turn RAG	Simple question-answering over documents

Vector Database

Sample	What It Demonstrates
Single-Turn RAG with Qdrant	External vector database integration
Single-Turn RAG with pgvector	PostgreSQL (pgvector) vector database integration

Text Analysis

Content Classification

Sample	What It Demonstrates
Custom Classification	Define your own categories

Keyword Extraction

Sample	What It Demonstrates
Keyword Extraction	Pull key terms from documents and images using VLMs

Language Detection

Sample	What It Demonstrates
Language Detection from Document	Detect language from PDFs, documents, and images using VLMs

Named Entity Recognition

Sample	What It Demonstrates
Named Entity Recognition	Identify people, places, organizations

PII Extraction

Sample	What It Demonstrates
Batch PII Extraction	PII detection across many documents
PII Extraction	Detect personal information

Sentiment Analysis

Sample	What It Demonstrates
Emotion Detection	Identify emotions in text
Sarcasm Detection	Detect sarcasm and irony
Sentiment Analysis	Positive/negative/neutral classification

Multimodal Embeddings

Sample	What It Demonstrates
Multimodal Embeddings	Embed text and images into one aligned space (Nomic) for cross-modal search

Speech & Audio

Real-Time Transcription

Sample	What It Demonstrates
Audio Transcription App (MAUI)	Cross-platform transcription desktop app
Speech to Text	Local Whisper transcription

Voice Activity Detection

Sample	What It Demonstrates
Voice Activity Detection	Gate Whisper with Silero VAD: faster, no silence hallucinations

Audio Language Detection

Sample	What It Demonstrates
Audio Language Detection	Identify spoken language before transcription, then pin it for accuracy

Also see the how-to guide: Build a Voice-Commanded Agent That Executes Tools

Text Generation

Content Summarization

Sample	What It Demonstrates
Text Summarizer	Summarize plain text

Conversations

Sample	What It Demonstrates
Chat Playground (MAUI)	Cross-platform desktop chat app
Multi-Turn Chat	Basic multi-turn conversation
Multi-Turn Chat with Chat History Guidance	History management strategies
Multi-Turn Chat with Custom Sampling	Temperature, top-p, and sampler control
Multi-Turn Chat with Persistent Session	Save and restore conversation state
Single-Turn Chat	Simplest possible interaction

Grammar & Spell Correction

Sample	What It Demonstrates
Text Corrector	Grammar and spelling correction

Prompt Templates

Sample	What It Demonstrates
Prompt Templates with Logic	Variables, conditionals, loops, and filters

Structured Content Creation

Sample	What It Demonstrates
Multi-Turn Chat with Yes/No Assistant	Constrained output formatting

Text Enhancement

Sample	What It Demonstrates
Text Rewriter	Rewrite in different styles

Text Translation

Sample	What It Demonstrates
Text Translator	Multilingual translation

Local Inference

Model Catalog

Sample	What It Demonstrates
Model Catalog Browser	Filter the predefined catalog by capability and load by `ModelID`

Hardware Backends

Sample	What It Demonstrates
Hardware Backends Inspector	Detect CPU/CUDA/Vulkan/Metal, enumerate GPUs, report layer placement

Multi-GPU

Sample	What It Demonstrates
Multi-GPU and Tensor Overrides	Run 30B MoE models across CPU + multi-GPU with regex placement rules

Context Hibernation

Sample	What It Demonstrates
Context Hibernation	Serialize the KV-cache to disk and rehydrate on next inference call

Sampling Controls

Sample	What It Demonstrates
Sampling and Generation Controls	Greedy, nucleus, and Mirostat v2 side by side

Encrypted Models

Sample	What It Demonstrates
Encrypted Model Loading	Load and run encrypted GGUF/LMK models

Model Optimization

Fine-Tuning

Sample	What It Demonstrates
LoRA Fine-Tuning	Train a local model on your own data and produce a GGUF adapter
Text-to-SQL Fine-Tuning	Teach a model your database schema from a dataset file, then merge one deployable model
Vision Display Fine-Tuning	Train a vision-language model to read seven-segment displays from labeled images

Model Quantization

Sample	What It Demonstrates
Quantization	Reduce model size for edge deployment

LoRA Integration

Sample	What It Demonstrates
LoRA Adapter Hot-Swap	Apply, list, and remove LoRA adapters on a loaded base model at inference time

Integrations

Microsoft.Extensions.AI

Sample	What It Demonstrates
Microsoft.Extensions.AI Integration	Use LM-Kit as an `IChatClient` drop-in

Semantic Kernel

Sample	What It Demonstrates
Semantic Kernel Integration Memory Demo	LM-Kit as a Semantic Kernel backend

Also see the how-to guide: Migrate from Cloud AI APIs to Local Inference with Microsoft.Extensions.AI

7. Hardware Quick Reference

All samples run on CPU. GPU acceleration is optional but recommended for larger models.

Sample Category	Minimum VRAM	Recommended Model
Chat & Conversation	6 GB	`gemma4:e4b`
AI Agents (with tools)	4 GB	`qwen3.5:9b`
Classification & Analysis	2 GB	`qwen3.5:2b`
Data Extraction (NER, PII)	2 GB	`qwen3.5:2b`
Document Processing (OCR)	6 GB	`gemma4:e4b` + vision model
Embeddings & RAG	2 GB	`qwen3-embedding:0.6b` + chat model
Speech & Audio	< 1 GB	`whisper-large-turbo3`
Fine-Tuning	8+ GB	Depends on base model
Multi-Agent Orchestration	4 GB	`qwen3.5:9b`

No GPU? Every sample works on CPU. Use smaller models (qwen3.5:0.8b, qwen3.5:2b, phi4-mini:3.8b) for faster CPU inference. See Choosing the Right Model.

8. Platform & Integration Support

LM-Kit.NET fits into your existing .NET stack:

Integration	What It Provides	Sample
Microsoft.Extensions.AI	`IChatClient` and `IEmbeddingGenerator` interfaces	Extensions.AI Integration
Semantic Kernel	Use LM-Kit as a Semantic Kernel AI backend	Semantic Kernel Memory Demo
Model Context Protocol (MCP)	Connect to external tool servers	MCP Integration
Qdrant	External vector database for production RAG	Single-Turn RAG with Qdrant
PostgreSQL (pgvector)	External vector database for production RAG	Single-Turn RAG with pgvector
OpenTelemetry	Distributed tracing and metrics	Telemetry & Observability
.NET MAUI	Cross-platform desktop and mobile apps	Chat Playground

Found a sample useful? Use the share buttons below this article (copy link, X, LinkedIn, Reddit) to share it with your team.

⭐ Star the samples repo on GitHub · Report an issue · Contribute a sample

Table of Contents