LM-Kit.NET vs AutoGenFull Stack vs. Multi-Agent Hub
Microsoft AutoGen pioneered multi-agent conversation patterns with its actor-based framework. LM-Kit.NET is a self-contained .NET SDK that ships its own inference engine alongside agents, RAG, and document intelligence. Both target .NET developers, but from very different angles. Here is an honest look at both.
Quick Comparison
Product Positioning
A Word Before We Compare
This comparison covers two products that share a .NET presence but occupy very different positions. AutoGen is a multi-agent orchestration framework from Microsoft Research, focused on composable agent conversations. LM-Kit.NET is a self-contained .NET SDK that ships its own inference engine and a full AI stack. We respect the research behind AutoGen and want to help you choose the right tool for your needs.
Microsoft AutoGen
AutoGen is an open-source framework from Microsoft Research that pioneered composable multi-agent conversations. Its actor-based architecture enables complex patterns like group chat, nested conversations, and hierarchical agent teams. It supports Python (primary) and .NET, with deep Azure integration. Note: AutoGen is now in maintenance mode as Microsoft transitions to the unified Agent Framework.
- Python (primary) & .NET SDKs
- Composable multi-agent conversations
- Docker-based code execution sandbox
- AutoGen Studio (low-code prototype UI)
- 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. AutoGen is like a conference room with a facilitator: it orchestrates conversations between specialized agents, each of which calls out to external LLM services for intelligence. LM-Kit.NET is like a self-contained workstation: it has its own compute, its own intelligence, and its own tools built in. The conference room gives you sophisticated conversation patterns and the ability to connect to any cloud LLM. The workstation gives you everything in one box, running entirely on your hardware. The right choice depends on whether you need multi-agent conversation orchestration across cloud providers, or a complete, self-sufficient AI platform for .NET.
Where AutoGen Shines
AutoGen was a pioneering multi-agent framework from Microsoft Research with 54K+ GitHub stars. It introduced patterns that influenced the entire AI agent ecosystem. Here is what it genuinely does well.
Multi-Agent Conversation Pioneer
AutoGen pioneered the concept of composable multi-agent conversations. Its group chat, nested conversations, and speaker selection mechanisms enable sophisticated agent collaboration patterns that influenced the entire industry.
Code Execution Sandbox
Agents can generate Python code and execute it in local or Docker-sandboxed environments, with results feeding back into the conversation. This makes AutoGen well suited for data analysis, research, and coding tasks where agents iterate on code.
Flexible Conversation Patterns
Two-agent chat, sequential chains, round-robin group chat, selector-based group chat, nested conversations, and FSM-based transitions. These composable patterns let you model complex multi-step workflows where agents take turns contributing their specialization.
Microsoft Research Pedigree
Born from Microsoft Research, AutoGen benefits from academic rigor and production insights. The Magentic-One benchmark agent achieved competitive results on GAIA, AssistantBench, and WebArena, demonstrating the potential of its orchestration patterns.
Distributed Runtime
AutoGen 0.4's actor-based Core API supports a distributed runtime powered by Microsoft Orleans, enabling cross-language agent communication between Python and .NET via gRPC. This is designed for large-scale, resilient multi-agent deployments.
AutoGen Studio
A low-code web interface for visually composing, testing, and debugging multi-agent workflows. Teams are defined as JSON, can be exported to Python applications, or deployed as API endpoints. Useful for rapid prototyping (though marked as a research prototype, not for production).
Where LM-Kit.NET Has the Edge
While AutoGen focuses on orchestrating conversations between agents that call external services, LM-Kit.NET delivers the entire AI stack as one integrated, self-contained platform.
Built-in Inference Engine
AutoGen requires an external LLM provider for every model call. LM-Kit.NET runs models directly in your process with native GPU acceleration (CUDA, Vulkan, Metal). No API keys, no per-token billing, no network latency on inference.
- Zero per-token API costs
- CUDA 12/13, Vulkan, Metal backends
- Multi-GPU distribution
- 60+ pre-tested model catalog
Complete Data Sovereignty
AutoGen's default workflow sends your data to external LLM APIs (OpenAI, Azure). LM-Kit.NET processes everything locally. No data leaves your hardware, making it inherently suitable for HIPAA, GDPR, and air-gapped environments.
- Air-gapped deployment support
- HIPAA and GDPR compliant by design
- No third-party data processing
- Full audit trail on-premises
First-Class .NET SDK
AutoGen's .NET SDK has historically lagged behind its Python counterpart in features and documentation. LM-Kit.NET is built from the ground up for C#, with full async/await, strong typing, IntelliSense, and native integration with ASP.NET, MAUI, and Blazor.
- .NET Standard 2.0 through .NET 10
- Semantic Kernel & Extensions.AI bridges
- AOT compilation support
- Single NuGet package, no Python dependency
Speech, Vision & Document Intelligence
AutoGen has no built-in speech, OCR, or document processing. LM-Kit.NET includes Whisper-based speech-to-text, VLM-powered OCR with 34-language support, PDF manipulation, and multi-format document extraction, all running locally.
- Whisper speech-to-text (tiny through large-v3)
- VLM-powered OCR with bounding boxes
- PDF split, merge, unlock, and rendering
- NER, PII extraction, sentiment analysis
Integrated RAG Pipeline
AutoGen's RAG support is integration-based, requiring external embedding providers and vector databases. LM-Kit.NET includes a complete RAG pipeline with built-in embeddings, a built-in vector store, and advanced retrieval strategies like HyDE, reranking, and multimodal RAG.
- Built-in vector database
- Local embedding models (Qwen3, Gemma)
- HyDE, reranking, multi-query retrieval
- Multimodal RAG (text + image)
Built-in Tool Catalog with Permissions
AutoGen ships minimal built-in tools (code execution, HTTP) and expects you to wrap Python functions. LM-Kit.NET provides a growing catalog of atomic built-in tools across 8 categories with enterprise-grade permission policies, risk levels, and approval workflows.
- 8 tool categories, constantly growing
- Fine-grained ToolPermissionPolicy
- Risk levels and approval workflows
- MCP integration for external tools
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 | AutoGen |
|---|---|---|
| Core Architecture | ||
| Primary Language | C# / .NET (first-class) | Python (primary), .NET (secondary) |
| Built-in LLM Inference | Yes, native engine | No, requires external LLM provider |
| Deployment Model | Single NuGet package, in-process | pip/NuGet + external LLM + vector store + extras |
| Offline / Air-Gapped | Full offline support | Requires external LLM API (cloud or local server) |
| License | Commercial (free tier available) | MIT (open source) |
| Active Development | Actively developed | Maintenance mode (bug fixes only); succeeded by Microsoft Agent Framework |
| Agent Capabilities | ||
| Agent Orchestration | Pipeline, parallel, router, supervisor | Group chat, sequential, nested, selector |
| Multi-Agent Conversations | SupervisorOrchestrator, DelegateTool | Core strength: group chat, nested, FSM transitions |
| Planning Strategies | ReAct, CoT, ToT, Plan-and-Execute, Reflection | Conversation-driven; no dedicated planning API |
| Human-in-the-Loop | Tool approval workflows | UserProxyAgent for human input |
| Code Execution Sandbox | Not built-in | Local and Docker-based execution |
| Built-in Tool Catalog | 8 categories, growing (Data, IO, Net, etc.) | Minimal (code exec, HTTP, GraphRAG search) |
| Tool Permission Policies | Risk levels, approval workflows, wildcards | No built-in permission system |
| MCP Support | Native McpClient | mcp_server_tools extension |
| Agent Memory | RAG-based semantic memory | Mem0Memory, RedisMemory |
| RAG & Retrieval | ||
| Vector Database | Built-in + Qdrant connector | Integration-based (ChromaDB, PGVector, Qdrant) |
| Embedding Models | Built-in local models (Qwen3-Embedding, Gemma) | No built-in; requires external providers |
| Retrieval Strategies | Semantic, hybrid, multi-query, HyDE, reranking | RetrieveChat with self-correcting retrieval |
| Multimodal RAG | Text + image retrieval and answering | Not built-in |
| Document Intelligence | ||
| Native OCR | Tesseract + VLM-powered OCR | No built-in; external tools only |
| PDF Manipulation | Split, merge, unlock, render to image | No document processing engine |
| Structured Extraction | JSON schema, NER, PII, confidence scores | Not built-in |
| Format Conversion | PDF, DOCX, HTML, Markdown, EML | No conversion engine |
| NLP & Text Analysis | ||
| Sentiment / Emotion Analysis | Built-in sentiment, emotion, sarcasm | Not built-in; prompt the LLM |
| Classification | Custom categories, batch classification | Not built-in |
| Translation | Built-in multilingual translation | Not built-in |
| Speech & Audio | ||
| Speech-to-Text | Whisper (tiny through large-v3-turbo) | No built-in; via external services only |
| Voice Activity Detection | Built-in VAD | No audio processing |
| Model Operations | ||
| Fine-Tuning (LoRA) | Built-in LoRA training | Orchestration only, no training |
| Quantization | Built-in model quantization | Not a training toolkit |
| GPU Acceleration | CUDA 12/13, Vulkan, Metal, AVX2 | N/A (delegates to external inference server) |
| Observability & Enterprise | ||
| Tracing & Metrics | OpenTelemetry, AgentTracer, AgentMetrics | OpenTelemetry, Jaeger, Azure Monitor |
| Resilience Policies | Retry, circuit breaker, timeout, bulkhead | Distributed runtime persistence (Orleans) |
| Low-Code UI | Not available | AutoGen Studio (research prototype) |
| Constrained Generation | JSON schema, grammar rules, templates | Depends on LLM provider capabilities |
| REST API Server | LM-Kit.Server (ASP.NET Core) | AutoGen Studio CLI export; Azure AI Foundry |
| Platform & Ecosystem | ||
| .NET SDK Maturity | Primary platform, full feature parity | Secondary to Python; docs recommend reading Python first |
| Python Support | Not available | Primary, most mature |
| Microsoft AI Ecosystem | Semantic Kernel & Extensions.AI bridges | Semantic Kernel adapter; Azure AI Foundry |
| Cross-Language Agents | Not available | Python & .NET agents via gRPC + CloudEvents |
| Cross-Platform | Windows, Linux (x64/ARM64), macOS | Anywhere Python/.NET runs |
Who Should Choose What
Both products serve different needs. The right choice depends on your architecture requirements, deployment model, and what capabilities you need beyond agent orchestration.
Choose AutoGen if...
AutoGen is the right choice when your primary need is sophisticated multi-agent conversation orchestration with cloud LLM providers.
- You need complex multi-agent conversation patterns (group chat, nested, FSM)
- Code generation and execution in sandboxed environments is a core use case
- You work primarily in Python and connect to cloud LLMs
- You need cross-language agents (Python + .NET interop via gRPC)
- An MIT open-source license is required
- You want a low-code prototyping UI (AutoGen Studio)
Choose LM-Kit.NET if...
LM-Kit.NET is the right choice when you need a complete, self-contained AI platform for .NET with full data sovereignty and no external dependencies.
- Your application is built on .NET and you want a first-class C# experience
- Data sovereignty is critical: HIPAA, GDPR, or air-gapped environments
- You want zero per-token costs and no dependency on cloud LLM APIs
- You need speech, vision, OCR, and document processing alongside agents
- You prefer a single, actively developed package over assembling components
- 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.