LM-Kit.NET vs Agent FrameworkSelf-Contained vs. Cloud-Orchestrated
Microsoft Agent Framework is the unified successor to AutoGen and Semantic Kernel, offering enterprise-grade agent orchestration with deep Azure integration. LM-Kit.NET is a self-contained .NET SDK that ships its own inference engine, RAG, agents, and tooling in a single package. Both target .NET developers, but with very different philosophies. Here is an honest look at both.
Quick Comparison
Product Positioning
A Word Before We Compare
This comparison brings together two .NET products with fundamentally different scopes. Microsoft Agent Framework is a cloud-first orchestration layer backed by Microsoft's entire Azure ecosystem. LM-Kit.NET is a self-contained SDK that ships its own inference engine and runs everything locally. They can serve the same .NET developer, but they make very different trade-offs. We respect what Microsoft has built and want to help you choose the right tool for your project.
Microsoft Agent Framework
Microsoft Agent Framework is the unified successor to AutoGen and Semantic Kernel, combining multi-agent orchestration with enterprise Azure integration. It provides graph-based workflows, declarative agent definitions, and deep connectivity to Azure AI Foundry, Entra ID, and 1,400+ Azure Logic Apps connectors. It reached Release Candidate status in February 2026, with GA targeted for Q1 2026.
- .NET & Python SDKs (first-class)
- Graph-based workflows with checkpointing
- Azure AI Foundry managed hosting
- MCP, A2A, OpenAPI, OpenTelemetry
- MIT license (open source)
LM-Kit.NET
LM-Kit.NET is an enterprise-grade .NET SDK that bundles a local inference engine with RAG, agent orchestration, document intelligence, NLP, speech recognition, vision, structured extraction, fine-tuning, and a growing catalog of built-in tools. Everything runs on your hardware with no external API calls required. A single NuGet package replaces an entire stack.
- Built-in inference engine (no external LLM needed)
- Agent orchestration (ReAct, pipeline, supervisor)
- RAG, document processing, NLP, speech, vision
- 100% offline capable, data never leaves device
- Commercial license (free tier available)
An honest framing. Microsoft Agent Framework is like an enterprise control tower: it coordinates agents that rely on external cloud services for intelligence, with deep integration into the Azure ecosystem for security, governance, and managed hosting. LM-Kit.NET is like a self-powered field station: it carries its own compute, models, and tools, and operates independently of any cloud. The control tower gives you enterprise governance, multi-cloud LLM flexibility, and managed infrastructure. The field station gives you complete independence, data sovereignty, and zero external dependencies. The right choice depends on whether your priority is Azure ecosystem integration or self-contained local operation.
Where Agent Framework Shines
Microsoft Agent Framework combines the best of AutoGen and Semantic Kernel, backed by one of the largest engineering organizations in the world. Here is what it genuinely does well.
Deep Azure Ecosystem
Native integration with Azure AI Foundry, Entra ID, Defender, Purview, Logic Apps (1,400+ connectors), Microsoft Graph, and SharePoint. No other framework offers this depth of Azure connectivity for enterprise .NET teams.
Graph-Based Workflows
A flexible workflow engine with explicit edges, conditional routing, parallel processing, checkpointing for long-running processes, and time-travel debugging. This goes beyond basic agent orchestration into enterprise business process automation.
Multi-Agent Patterns
Inherits AutoGen's proven patterns (sequential, concurrent, group chat, handoff) combined with Semantic Kernel's plugin system. Declarative YAML/JSON definitions allow version-controlled, shareable agent workflows.
Enterprise Security
Entra Agent ID for agent identity management, Prompt Shield for real-time injection protection, PII detection, task adherence monitoring, and integration with Microsoft Defender and Purview for compliance governance.
Open Standards
First-class support for MCP (Model Context Protocol), A2A (Agent-to-Agent protocol), AG-UI, OpenAPI tool import, and OpenTelemetry observability. This standards-first approach ensures interoperability with the broader AI ecosystem.
Managed Hosting
Azure AI Foundry Agent Service provides zero-ops deployment with autoscaling, identity, observability, and governance. Over 10,000 organizations are already using it. No container images or Kubernetes knowledge required for production deployment.
Where LM-Kit.NET Has the Edge
While Microsoft Agent Framework excels at cloud-connected orchestration, LM-Kit.NET delivers the entire AI stack as a single, self-contained package with no external dependencies.
Built-in Inference Engine
Microsoft Agent Framework requires an external LLM provider (Azure OpenAI, Ollama, etc.) for every model call. LM-Kit.NET runs models directly in your process with native GPU acceleration. No API keys, no per-token costs, no network latency.
- Zero per-token API costs
- CUDA 12/13, Vulkan, Metal backends
- Multi-GPU distribution
- 60+ pre-tested model catalog
True Data Sovereignty
Agent Framework's full-featured mode relies on Azure cloud services. LM-Kit.NET processes everything on your hardware. No data leaves your device, ever. This is critical for HIPAA, GDPR, government, and air-gapped deployments where cloud connectivity is not an option.
- Air-gapped deployment support
- No cloud account required
- No third-party data processing
- Full audit trail on-premises
Speech, Vision & Document Intelligence
Agent Framework delegates speech, OCR, and document processing to separate Azure services (Voice Live API, Document Intelligence) with their own pricing. LM-Kit.NET includes all of this in the SDK: Whisper speech-to-text, VLM-powered OCR, PDF manipulation, and multi-format extraction.
- Whisper speech-to-text (tiny through large-v3)
- VLM-powered OCR with 34-language support
- PDF split, merge, unlock, and rendering
- NER, PII extraction, sentiment analysis
One Package, Zero Assembly
Agent Framework requires assembling multiple NuGet packages, connecting external LLM providers, setting up vector stores, and optionally configuring Azure services. LM-Kit.NET delivers inference, RAG, agents, tools, embeddings, and a vector database in a single NuGet package.
- Inference + RAG + agents + tools in one package
- Built-in vector database and embeddings
- No dependency management headaches
- Production-ready today (stable API)
Built-in Tool Catalog with Permissions
Agent Framework provides integration-based tools (connectors to Azure services, OpenAPI imports) but no catalog of atomic built-in tools. LM-Kit.NET ships a growing catalog across 8 categories with enterprise-grade permission policies and approval workflows.
- 8 tool categories, constantly growing
- Fine-grained ToolPermissionPolicy
- Risk levels and approval workflows
- MCP integration for external tools
Predictable, Cloud-Independent Costs
Agent Framework's production path typically involves Azure OpenAI API costs, Azure AI Foundry hosting costs, and optional Azure service costs (Document Intelligence, Voice, etc.). LM-Kit.NET has a one-time license cost with no per-token, per-call, or cloud infrastructure fees.
- No per-token API billing
- No cloud hosting fees
- Predictable budgeting
- Free tier available
Detailed Comparison
A comprehensive, side-by-side breakdown of capabilities. We aim for accuracy; if something has changed, let us know.
| Feature | LM-Kit.NET | Agent Framework |
|---|---|---|
| Core Architecture | ||
| Primary Language | C# / .NET (first-class) | .NET & Python (both first-class) |
| Built-in LLM Inference | Yes, native engine | No, requires external LLM provider |
| Deployment Model | Single NuGet package, in-process | Multiple NuGet packages + external LLM + optional Azure services |
| Offline / Air-Gapped | Full offline support | Possible with Ollama/Foundry Local; enterprise features require Azure |
| License | Commercial (free tier available) | MIT (open source) |
| Maturity | Stable, production-ready | Release Candidate (GA targeted Q1 2026) |
| LLM Provider Support | 60+ local models via built-in engine | Azure OpenAI, OpenAI, Anthropic, Ollama, any IChatClient |
| Agent Capabilities | ||
| Agent Orchestration | Pipeline, parallel, router, supervisor | Sequential, concurrent, group chat, handoff |
| Graph-Based Workflows | Not available | Edges, conditional routing, checkpointing |
| Declarative Agents (YAML/JSON) | Code-based only | YAML & JSON definitions |
| Planning Strategies | ReAct, CoT, ToT, Plan-and-Execute, Reflection | Conversation-driven; graph-based routing |
| Built-in Tool Catalog | 8 categories (Data, IO, Net, etc.) | No atomic tools; connectors & custom functions |
| Tool Permission Policies | Risk levels, approval workflows, wildcards | No built-in tool permission DSL |
| MCP Support | Native McpClient | Native MCP + A2A protocol |
| Agent Memory | RAG-based semantic memory | Foundry managed memory (preview) |
| RAG & Retrieval | ||
| Vector Database | Built-in + Qdrant connector | In-memory (prototyping); Azure AI Search, Qdrant, etc. via connectors |
| Embedding Models | Built-in local models (Qwen3-Embedding, Gemma) | No built-in; requires external providers |
| Retrieval Strategies | Semantic, hybrid, multi-query, HyDE, reranking | TextSearchProvider (auto & on-demand modes) |
| Multimodal RAG | Text + image retrieval and answering | Not built-in |
| Document Intelligence | ||
| Native OCR | Tesseract + VLM-powered OCR | Via Azure Document Intelligence (separate service) |
| PDF Manipulation | Split, merge, unlock, render | No document processing engine |
| Structured Extraction | JSON schema, NER, PII, confidence scores | Not built-in |
| Speech & Audio | ||
| Speech-to-Text | Whisper (tiny through large-v3-turbo) | Via Azure Voice Live API (separate service) |
| Model Operations | ||
| Fine-Tuning (LoRA) | Built-in LoRA training | Orchestration only |
| Quantization | Built-in model quantization | Not a training toolkit |
| GPU Acceleration | CUDA 12/13, Vulkan, Metal, AVX2 | N/A (delegates to external inference) |
| Observability & Enterprise | ||
| Tracing & Metrics | OpenTelemetry, AgentTracer, AgentMetrics | OpenTelemetry (GenAI conventions), Azure Monitor |
| Resilience Policies | Retry, circuit breaker, timeout, bulkhead | Checkpointing; Azure Durable Functions |
| Enterprise Security | Tool permission policies, local data processing | Entra Agent ID, Prompt Shield, Defender, Purview |
| Managed Cloud Hosting | Self-hosted (LM-Kit.Server available) | Azure AI Foundry (zero-ops, autoscaling) |
| Constrained Generation | JSON schema, grammar rules | Depends on LLM provider |
| Platform & Ecosystem | ||
| Azure Ecosystem | Not integrated (standalone) | Deep: Foundry, Entra, Defender, Logic Apps, Graph |
| Microsoft AI Bridges | Semantic Kernel & Extensions.AI bridges | Native (built on Extensions.AI IChatClient) |
| Python Support | Not available | First-class Python SDK |
| Cross-Platform | Windows, Linux (x64/ARM64), macOS | Anywhere .NET/Python runs |
Who Should Choose What
Both products target .NET developers but serve different deployment models. The right choice depends on your cloud strategy, data sovereignty requirements, and the breadth of AI capabilities you need.
Choose Agent Framework if...
Microsoft Agent Framework is the right choice when your priority is Azure ecosystem integration, multi-cloud LLM flexibility, and enterprise governance.
- You are invested in the Azure ecosystem (Foundry, Entra, Defender)
- You need graph-based workflows with checkpointing and time-travel
- You want to use cloud LLMs (Azure OpenAI, Anthropic, etc.) and swap freely
- You need managed cloud hosting with zero-ops deployment
- An MIT open-source license is required
- You also need Python support alongside .NET
Choose LM-Kit.NET if...
LM-Kit.NET is the right choice when you need a complete, self-contained AI platform that runs entirely on your hardware with no cloud dependencies.
- Data sovereignty is critical: HIPAA, GDPR, or air-gapped environments
- You want zero per-token costs and no cloud infrastructure to manage
- You need speech, vision, OCR, and document processing alongside agents
- You prefer a single, integrated package over assembling multiple services
- You want a production-stable SDK today (not waiting for GA)
- You want built-in fine-tuning, quantization, and a complete RAG pipeline
Ready to Build AI into Your .NET Application?
Get started with LM-Kit.NET in minutes. One NuGet package gives you inference, RAG, agents, document intelligence, speech, vision, and a growing catalog of built-in tools. No Python, no external APIs, no infrastructure to manage.