LM-Kit.NET vs Semantic KernelComplementary, Not Competing
Semantic Kernel is Microsoft's AI orchestration SDK that connects to external model providers. LM-Kit.NET is a self-contained AI platform with its own inference engine. They actually work together: LM-Kit.NET can serve as the local model provider for Semantic Kernel.
Quick Comparison
Product Positioning
Before We Compare: They Work Together
This comparison is unique because LM-Kit.NET and Semantic Kernel are not just alternatives. They are complementary products that can be used together. LM-Kit.NET provides a Semantic Kernel bridge, letting you use local LM-Kit models as the inference provider inside Semantic Kernel workflows.
Microsoft Semantic Kernel
Semantic Kernel is Microsoft's open-source AI orchestration middleware. It connects your application to external LLM providers (Azure OpenAI, OpenAI, etc.) and coordinates the flow between plugins, prompts, and AI services. It does not run models itself.
- Connects to Azure OpenAI, OpenAI, and other providers
- Agent framework with multi-agent patterns
- Prompt template engines (SK syntax, Handlebars, Liquid)
- Available in C#, Python, and Java
LM-Kit.NET
LM-Kit.NET is an enterprise-grade .NET SDK that bundles a local inference engine with a complete AI application platform: agent orchestration, RAG, document intelligence, text analysis, speech processing, and vision. No external services required.
- In-process inference with GPU acceleration
- Built-in RAG engine with hybrid retrieval
- Document processing, OCR, and format conversion
- NER, PII detection, sentiment, classification
Think of it this way: Semantic Kernel is like an orchestra conductor. It coordinates the musicians (AI services) but does not play any instrument itself. LM-Kit.NET is a complete band: it plays the instruments (runs models locally) and coordinates the performance (orchestrates agents, RAG, tools). If you already use Semantic Kernel and want local inference, you can use LM-Kit.NET as one of SK's musicians.
They work together: LM-Kit.NET ships a Semantic Kernel bridge (LM-Kit.NET.Integrations.SemanticKernel) that implements IChatCompletionService, ITextGenerationService, and IEmbeddingGenerator. Register LM-Kit.NET with builder.AddLMKitChatCompletion(model) and your existing Semantic Kernel code runs on local models with zero cloud calls.
Where Semantic Kernel Genuinely Shines
Semantic Kernel is a serious, well-engineered orchestration SDK backed by Microsoft. Here are the areas where it excels and where it may be the better fit.
Multi-Language (C#, Python, Java)
SK is available in three major languages, making it accessible to teams that work across the .NET, Python, and Java ecosystems.
Cloud Provider Ecosystem
First-class GA connectors for Azure OpenAI and OpenAI, plus preview connectors for Google, Amazon Bedrock, Mistral, HuggingFace, and more.
Advanced Prompt Templates
Three template engines (SK syntax, Handlebars, Liquid), YAML-based prompt configuration, and the newer Prompty format for version-controlled prompt assets.
10+ Vector Store Connectors
Prebuilt connectors for Azure AI Search, Qdrant, Redis, Pinecone, Weaviate, Chroma, SQL Server, SQLite, Milvus, and more (most in preview).
MIT Licensed, Microsoft Backed
Fully open-source under MIT license with strong Microsoft backing. Free for commercial use with no licensing tiers or restrictions.
Process Framework
Event-driven workflow orchestration with stateful steps, parallel processing, and Dapr runtime integration for distributed, scalable execution.
Where LM-Kit.NET Goes Further
While Semantic Kernel orchestrates calls to external services, LM-Kit.NET packages everything into a single SDK: the inference engine, RAG pipeline, document processing, NLP, and more. No cloud accounts, no external servers, no assembly required.
Built-in Inference Engine
LM-Kit.NET loads and runs models in-process. No external server, no cloud API, no network calls. Semantic Kernel requires an external LLM provider for every AI interaction.
- CUDA 12/13, Vulkan, Metal, AVX2 acceleration
- 60+ curated models from the model catalog
- Zero per-token cost, unlimited inference
- Fully offline and air-gapped capable
Complete RAG Pipeline
SK provides vector store search primitives but delegates document ingestion, chunking, and extraction to the separate Kernel Memory project. LM-Kit.NET ships the entire RAG pipeline in one package.
- Hybrid retrieval: vector + BM25 with RRF
- Built-in vector store and Qdrant connector
- Semantic, Markdown, HTML, layout chunking
- Multi-query, HyDE, query contextualization
Document Intelligence
SK has no document processing capabilities. PDF parsing, OCR, and format conversion require external services like Azure Document Intelligence. LM-Kit.NET handles these natively.
- PDF chat, search, split, merge, convert
- VLM-powered OCR with multi-intent modes
- DOCX, EML, MBOX, HTML, Markdown support
- AI-powered document splitting with vision
NLP and Text Analysis
SK has no built-in NLP. Named entity recognition, PII detection, sentiment analysis, and classification all require wrapping external libraries. LM-Kit.NET ships these as first-class features.
- NER with 102 entity types and validators
- PII detection and compliance-ready redaction
- Fine-tuned sentiment and sarcasm models
- Structured extraction with JSON schema
Rich Built-in Tool Catalog
SK ships 7 basic core plugins (math, text, time, file I/O, HTTP, wait). LM-Kit.NET provides a constantly growing catalog of atomic tools across eight categories with enterprise-grade permission policies.
- Data, Document, Text, Numeric, Security, Utility, IO, Net
- Risk-level metadata and approval workflows
- Web search: DuckDuckGo, Brave, Tavily, Serper, SearXNG
- Wildcard permission patterns for domain control
Speech, Vision, Fine-tuning
SK's audio support is cloud-only (OpenAI/Azure Whisper API). It has no vision processing, no fine-tuning, and no quantization. LM-Kit.NET handles all of these locally.
- On-device Whisper (tiny through large-v3-turbo)
- Vision language models for image understanding
- LoRA fine-tuning and adapter management
- Built-in model quantization tools
Detailed Feature Comparison
A comprehensive, honest breakdown. Both products have real strengths. We only claim what ships in the product today.
| Feature | LM-Kit.NET | Semantic Kernel |
|---|---|---|
| Core Architecture | ||
| Built-in inference engine | In-process, GPU-accelerated | Not included. Requires external LLM provider |
| Cloud LLM connectors | Local-first (no cloud connectors) | Azure OpenAI, OpenAI (GA) + 7 preview |
| Local model support | Native, first-class | Via ONNX (alpha), Ollama (alpha), or OpenAI-compat endpoints |
| Offline / air-gapped operation | Fully offline | Requires network access to LLM providers |
| GPU acceleration | CUDA 12/13, Vulkan, Metal, AVX2 | N/A (inference runs on provider side) |
| Structured outputs | Grammar-constrained decoding | Via provider support (JSON mode) |
| Agent Orchestration | ||
| Agent framework | Agent, AgentBuilder, AgentExecutor | ChatCompletionAgent, OpenAIAssistantAgent |
| Multi-agent patterns | Pipeline, Parallel, Router, Supervisor | Sequential, Concurrent, Conditional, Group Chat, Handoff |
| Planning strategies | ReAct, CoT, ToT, Plan-and-Execute, Reflection | Auto function calling (planners partially deprecated) |
| Agent memory / persistence | Time-decay, consolidation, user-scoped | Basic memory plugin. Full pipeline via Kernel Memory (separate project) |
| Agent skills (SKILL.md) | Reusable skill definitions | Not available (plugins serve a similar role) |
| Built-in tool catalog | Growing catalog across 8 categories | 7 basic core plugins (math, text, time, file, HTTP, wait, summary) |
| Tool permission policies | Risk-level, category, wildcard patterns | Not available |
| RAG & Knowledge Retrieval | ||
| Built-in RAG engine | RagEngine, RagChat, PdfChat | Search primitives only. Ingestion via Kernel Memory (separate) |
| Document chunking | Semantic, Markdown, HTML, layout | Not included. Available in Kernel Memory |
| Vector store connectors | Built-in + Qdrant | 10+ connectors (most in preview) |
| Hybrid retrieval (Vector + BM25) | With Reciprocal Rank Fusion | Depends on vector store provider capabilities |
| Reranking | BGE M3 Reranker | Not available |
| Document Processing & Vision | ||
| PDF processing | Chat, search, split, merge, convert | Not included. Use Azure Document Intelligence or third-party |
| OCR | VLM-powered, multi-intent | Not included. Requires external cloud service |
| Vision / VLM | Local multi-model, multi-image | Via cloud vision models (GPT-4o, Gemini) |
| Image embeddings | Nomic Embed Vision | Not available |
| NLP & Text Analysis | ||
| Named Entity Recognition | 102 entity types | Not included. Requires custom plugins |
| PII detection & redaction | Compliance-ready | Not included. Presidio available separately |
| Sentiment / emotion analysis | Fine-tuned models included | Not included |
| Translation | 100+ languages with confidence | Not included (prompt-based via LLM) |
| Speech & Audio | ||
| Speech-to-text | On-device Whisper models | Cloud-only via OpenAI/Azure Whisper API |
| Voice Activity Detection | Not available | |
| Prompt Management & Templates | ||
| Template engines | Mustache with conditionals, loops, filters | SK syntax, Handlebars, Liquid (3 engines) |
| YAML prompt configuration | Not available | Version-controlled prompt assets |
| Semantic / native plugins | Uses tool registration pattern | Prompt files + [KernelFunction] annotations |
| Model Customization | ||
| LoRA fine-tuning | Train and manage adapters | Not included. Use provider fine-tuning APIs |
| Quantization | Built-in quantization tools | Not included |
| Production & Enterprise | ||
| Observability (OpenTelemetry) | GenAI semantic conventions | Tracing, logging, metrics (experimental) |
| Filter / middleware pipeline | Prompt, completion, tool filters | Function, prompt, auto-invocation filters |
| Resilience patterns | Retry, circuit breaker, bulkhead, rate limit | Not built-in. Use Polly or Microsoft.Extensions.Http.Resilience |
| MCP support | Native MCP client | Supported in Microsoft Agent Framework (MAF) |
| Process framework (workflows) | Uses agent orchestration patterns | Event-driven steps with Dapr runtime |
| Dependency injection | Via Extensions.AI bridge | Native IServiceCollection integration |
| Platform & Licensing | ||
| .NET support | .NET Standard 2.0, .NET 8/9/10 | netstandard2.0, net8.0 |
| Python support | Not available (.NET only) | GA |
| Java support | Not available (.NET only) | GA |
| License | Commercial (free tier available) | MIT (fully open source) |
Which One Is Right for You?
Remember: these products can be used together. But if you're choosing a primary platform, here's how to decide.
Choose Semantic Kernel if you...
SK is the right choice when you need a cloud-first orchestration layer with multi-language support.
- Primarily use Azure OpenAI or OpenAI APIs and want an orchestration layer
- Work in Python or Java (not just .NET)
- Need to connect multiple cloud LLM providers in the same application
- Want advanced prompt template management (Handlebars, Liquid, YAML)
- Need the Process Framework for event-driven distributed workflows
- Require a fully open-source (MIT), free solution with no commercial licensing
Choose LM-Kit.NET if you...
LM-Kit.NET is the right choice when you need a self-contained AI platform that runs entirely on your infrastructure.
- Need local inference with zero cloud dependency or per-token costs
- Want RAG, document processing, OCR, and NLP in a single package
- Require data privacy, offline operation, or air-gapped deployment
- Need on-device speech-to-text, vision, or fine-tuning capabilities
- Want built-in tool permission policies for enterprise security
- Are building a .NET application and want everything in one NuGet package
- Already use SK and want to add local inference via the LM-Kit bridge
One SDK. Everything Built In.
LM-Kit.NET gives you local inference, agent orchestration, RAG, document intelligence, NLP, speech, and vision in a single .NET package. No cloud accounts. No assembly required.