4.8 out of 5 stars
(29 Ratings)
LM-Kit.NET is a complete local AI runtime for .NET that lets engineering teams ship AI-powered features without cloud dependencies, per-token costs, or data leaving the network.
Most .NET AI integrations stop at inference. LM-Kit.NET covers the full range of capabilities production applications actually need: agentic workflows with tool calling, planning, and memory; document intelligence with OCR and structured extraction; retrieval-augmented generation with built-in vector storage; multilingual speech-to-text; vision and multimodal understanding; text analysis with classification, NER, PII extraction, and sentiment; and text generation with translation, summarization, and constrained output.
Ships in one NuGet package, runs in-process with no sidecar services, and works across all major hardware acceleration backends. Drop-in replacement for Semantic Kernel through its Microsoft.Extensions.AI compatibility layer.
Learn more about LM-Kit.NET
4.3 out of 5 stars
(1178 Ratings)
Gemini platform is an enterprise AI development and machine learning platform from Google Cloud that enables organizations to build, customize, deploy, and manage AI agents and intelligent applications. Formerly known as Vertex AI, the platform provides access to more than 200 foundation models from Google, third-party providers, and open-source communities. Its Agent Studio and Agent Development Kit allow developers to create autonomous agents, design multistep workflows, and integrate artificial intelligence with enterprise data and external tools. The platform also supports custom model training, fine-tuning, machine learning pipelines, model evaluation, and scalable inference infrastructure. Enterprise capabilities include agent identity management, centralized governance, security policies, persistent memory, and monitoring tools for evaluating agent performance. Gemini platform integrates with BigQuery, Google Antigravity, Google Cloud infrastructure, and developer frameworks to support generative AI applications, predictive analytics, and automated business workflows.
Learn more about Gemini platform
oMLX
oMLX is a macOS-native MLX server designed to make local AI faster and more practical on Apple Silicon. Built for the way coding agents actually work, it uses paged SSD KV caching to persist cache blocks to disk, allowing previously seen prefixes to be restored across requests and server restarts instead of being recomputed from scratch. This can reduce time to first token on long contexts from 30–90 seconds to under five seconds after the first turn. Continuous batching handles concurrent requests through mlx-lm’s BatchGenerator, improving generation throughput without forcing requests to wait behind a single job. oMLX can serve LLMs, vision-language models, embedding models, and rerankers simultaneously, using LRU eviction when memory runs low. It supports any MLX-format model from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can reuse models already stored in the standard Hugging Face cache, LM Studio folders, or custom directories.
Learn more about oMLX
