LM-Kit.NET vs LLamaSharpAn Honest, Side-by-Side Look
LLamaSharp is a well-maintained open-source binding of llama.cpp for .NET. LM-Kit.NET is a full AI development platform built for production .NET applications. Both target .NET developers, but at very different levels of the stack. Here is a transparent comparison.
Quick Comparison
Product Positioning
A Word Before We Compare
LLamaSharp and LM-Kit.NET are both .NET libraries for local AI, but they operate at fundamentally different levels. LLamaSharp is a focused inference binding. LM-Kit.NET is a comprehensive development platform. This comparison is an apples-to-oranges exercise in many areas, and we want to be upfront about that.
LLamaSharp
LLamaSharp is a well-maintained, MIT-licensed C#/.NET binding of llama.cpp. It provides clean, modern APIs for loading and running GGUF models locally. It is one of the most popular open-source .NET projects for local LLM inference, with an active community and regular releases.
- Direct llama.cpp binding (P/Invoke)
- GGUF model format support
- Multiple executor patterns
- ChatSession & embedding APIs
- MIT license (fully open source)
LM-Kit.NET
LM-Kit.NET is an enterprise-grade .NET SDK that bundles local inference with agent orchestration, RAG, document intelligence, NLP, speech recognition, vision, structured extraction, fine-tuning, and a growing catalog of built-in tools. It is a single NuGet package that covers the entire AI application lifecycle.
- Local inference + full SDK capabilities
- Agent orchestration (ReAct, pipeline, supervisor)
- Built-in RAG, document & speech processing
- Enterprise tooling & resilience patterns
- Commercial license (free tier available)
Think of it this way: LLamaSharp is like a high-quality engine block you can drop into your project. LM-Kit.NET is the entire vehicle, ready to drive, with the engine, transmission, navigation, and safety systems already integrated. If you only need the engine, LLamaSharp is an excellent choice. If you need the whole vehicle, LM-Kit.NET saves you from assembling it yourself.
Where LLamaSharp Shines
Credit where it is due. LLamaSharp is a mature, respected project with genuine strengths that make it the right choice for specific use cases.
Fully Open Source (MIT)
No licensing fees, no restrictions. You can fork it, modify it, and embed it in any project, commercial or otherwise. This matters when your organization requires full source code transparency.
Lean and Focused
If all you need is local llama.cpp inference in .NET, LLamaSharp does exactly that with minimal overhead. No unnecessary abstractions or features you will not use.
Active Community
With over 3,000 GitHub stars and frequent releases, LLamaSharp has a healthy open-source community. You can expect ongoing maintenance, issues resolved publicly, and contributions from the .NET ecosystem.
Composable Architecture
LLamaSharp separates model loading (LLamaWeights), context (LLamaContext), and execution into distinct components. This gives experienced developers fine-grained control over memory and session management.
Semantic Kernel Integration
LLamaSharp has a dedicated Semantic Kernel connector package (LLamaSharp.semantic-kernel), letting you use it as a local model provider inside Microsoft's orchestration framework.
Low Entry Barrier
Getting started takes just a NuGet install, a GGUF model file, and a few lines of code. The learning curve is shallow, making it accessible for experimentation and prototyping.
Where LM-Kit.NET Goes Further
LM-Kit.NET includes its own optimized inference engine and then adds layers of capability that LLamaSharp was never designed to provide. These are not criticisms of LLamaSharp; they are simply outside its scope.
Agent Orchestration
Build multi-step, tool-using AI agents with four orchestration patterns. Let the LLM reason, plan, and call tools autonomously to complete complex tasks.
- ReAct (reasoning + acting) planning
- Pipeline, parallel, and supervisor patterns
- Built-in tool catalog across 8 categories
- Enterprise permission policies per tool
Retrieval-Augmented Generation
Index documents, chunk text, generate embeddings, and query a knowledge base, all from a single SDK. No need to assemble a RAG pipeline from separate libraries.
- Built-in vector indexing and search
- Conversational RAG with source citations
- Reranking and hybrid search
- Qdrant connector for external vector DBs
Document Intelligence
Extract text from PDFs, Word documents, spreadsheets, and emails. Run OCR on scanned images. Convert documents to Markdown. Detect layout and tables. All built in.
- PDF, DOCX, XLSX, PPTX, EML, HTML extraction
- Tesseract OCR (34 languages)
- Layout analysis and table extraction
- PDF split, merge, and image rendering
NLP & Structured Extraction
Go beyond raw text generation with purpose-built NLP capabilities. Extract entities, detect sentiment and emotions, classify text, and pull structured data from unstructured content.
- NER, PII detection, sentiment, emotion
- Zero-shot classification (single and multi-label)
- Grammar-constrained JSON extraction
- Schema discovery from sample documents
Speech & Vision
Transcribe audio with Whisper models, analyze images with vision language models, and extract text from scanned documents, all from the same SDK instance.
- OpenAI Whisper (tiny through large-v3-turbo)
- Multi-turn visual conversations (VLMs)
- Vision-based OCR with bounding boxes
- Multimodal RAG (text + image embeddings)
Enterprise Production Tooling
Ship to production with confidence. LM-Kit.NET includes resilience patterns, observability, middleware pipelines, and permission policies that production workloads demand.
- Retry, circuit breaker, rate limit, bulkhead
- Prompt, completion, and tool filter pipelines
- Token-level telemetry and generation metrics
- Fine-tuning (LoRA) and model quantization
Detailed Comparison Table
A comprehensive, honest breakdown of capabilities. Green means native, built-in support. Amber means partial or community-supported. Gray means not available.
| Feature | LM-Kit.NET | LLamaSharp |
|---|---|---|
| Core Inference | ||
| Local LLM inference | Optimized native engine | llama.cpp binding |
| Multi-turn conversation | MultiTurnConversation API | ChatSession + InteractiveExecutor |
| Streaming output | Event-based streaming | IAsyncEnumerable streaming |
| Text embeddings | Text + image embeddings | LLamaEmbedder (text only) |
| Model quantization | Built-in Quantizer | LLamaQuantizer |
| Grammar-constrained decoding | JSON, regex, schema | GBNF grammar support |
| Validated model catalog | 60+ pre-validated models with URIs | Manual GGUF model sourcing |
| Batched / parallel inference | Thread-safe concurrent requests | BatchedExecutor |
| GPU & Hardware Acceleration | ||
| CUDA (NVIDIA) | CUDA 12 + 13 | CUDA 11 + 12 |
| Vulkan (cross-platform GPU) | ||
| Metal (macOS) | Native Metal via GGML | |
| AVX / AVX2 CPU optimization | ||
| Automatic backend selection | CUDA → Vulkan → CPU fallback | Manual backend package selection |
| Agents & Tools | ||
| Agent orchestration | ReAct, pipeline, parallel, supervisor | Not available |
| Function / tool calling | ITool interface + built-in catalog | Not available natively |
| Built-in tool library | Data, IO, Net, Document, Text, Numeric, Security, Utility | Not available |
| Tool permission policies | Allow / deny / require approval per tool | Not available |
| MCP (Model Context Protocol) | Native MCP client | Not available |
| RAG & Knowledge Management | ||
| Built-in RAG engine | RagEngine with indexing, chunking, search | Not available (Kernel Memory integration possible) |
| Conversational RAG | RAGChat / PdfChat with citations | Not available natively |
| Vector database connectors | Qdrant integration | Not available natively |
| Agent memory (persistent) | Semantic, episodic, procedural memory | Not available |
| Document Processing & NLP | ||
| Document text extraction | PDF, DOCX, XLSX, PPTX, EML, HTML | Not available |
| OCR | Tesseract (34 languages) + Vision OCR | Not available |
| Sentiment / emotion analysis | Purpose-built APIs | Not available (manual prompting needed) |
| Named entity recognition | Person, location, org, date, number | Not available |
| Text classification | Zero-shot, single / multi-label | Not available |
| Structured data extraction | Schema-driven with confidence scores | Not available |
| Translation | 100+ language pairs | Not available (manual prompting needed) |
| Speech & Vision | ||
| Speech-to-text | Whisper models (tiny to large-v3-turbo) | Not available |
| Vision language models | Qwen 3-VL, Gemma 3-VL, and more | LLaVA support |
| Image embeddings | Unified text + image vector space | Text embeddings only |
| Enterprise & Production | ||
| Resilience patterns | Retry, circuit breaker, bulkhead, rate limit | Not available |
| Observability / telemetry | Token metrics, generation speed, latency | Minimal logging only |
| Filter / middleware pipeline | Prompt, completion, tool filters | Not available |
| Fine-tuning (LoRA) | Built-in LoRA fine-tuning | Not available (inference only) |
| REST API server | LM-Kit.Server (ASP.NET Core) | Not available natively |
| Microsoft ecosystem integration | Semantic Kernel + Extensions.AI | Semantic Kernel connector |
| Platform & Licensing | ||
| Windows | Windows 7+ | |
| macOS | Universal (Intel + Apple Silicon) | |
| Linux | x64 & ARM64 | |
| .NET Standard 2.0 support | ||
| License | Commercial (free tier available) | MIT (fully open source) |
Which One Is Right for You?
Both libraries serve .NET developers, but they target different needs. The right choice depends on how much AI infrastructure you want to build yourself versus getting out of the box.
Choose LLamaSharp if you...
LLamaSharp is an excellent choice when you need a lightweight, open-source inference layer and are comfortable building everything else around it.
- Only need local LLM inference and embeddings in your .NET project
- Want full source code access with no licensing restrictions (MIT)
- Prefer to assemble your own AI stack from individual libraries
- Are prototyping or building a research project with GGUF models
- Want fine-grained control over llama.cpp internals (weights, context, executors)
- Value community-driven development and open governance
Choose LM-Kit.NET if you...
LM-Kit.NET is the right choice when you are building a real application and need more than inference, without stitching together a dozen libraries.
- Are building production .NET applications with AI capabilities
- Need agent orchestration, RAG, or document intelligence built in
- Want a single SDK that covers inference, NLP, speech, vision, and tools
- Require enterprise features: resilience, observability, tool permissions
- Need NLP capabilities like NER, PII detection, or sentiment analysis
- Want speech-to-text, fine-tuning, or model quantization in one package
- Need a validated model catalog with tested URIs and VRAM requirements
Ready to Build Something Ambitious?
LM-Kit.NET gives you local inference, agent orchestration, RAG, document intelligence, and enterprise tooling in a single .NET package. Start building today.