technology-selection

Solid

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

AI & Automation 3,357 stars 247 forks Updated today MIT

Install

View on GitHub

Quality Score: 93/100

Stars 20%

100

Recency 20%

100

Frontmatter 20%

Documentation 15%

100

Issue Health 10%

License 10%

100

Description 5%

100

Skill Content

# .NET AI and Machine Learning ## Inputs | Input | Required | Description | |-------|----------|-------------| | Task description | Yes | What the AI/ML feature should accomplish (e.g., "classify support tickets", "summarize documents") | | Data description | Yes | Type and shape of input data (structured/tabular, unstructured text, images, mixed) | | Deployment constraints | No | Cloud vs. local, latency SLO, cost budget, offline requirements | | Existing project context | No | Current .csproj, existing packages, target framework | ## Workflow ### Step 1: Classify the task using the decision tree Evaluate the developer's task against this decision tree and select the appropriate technology. State which branch applies and why. | Task type | Technology | Rationale | |-----------|-----------|-----------| | Structured/tabular data: classification, regression, clustering, anomaly detection, recommendation | **ML.NET** (`Microsoft.ML`) | Reproducible (given a fixed seed and dataset), no cloud dependency, purpose-built models for these tasks | | Natural language understanding, generation, summarization, reasoning over unstructured text (single prompt → response, no tool calling) | **LLM via Microsoft.Extensions.AI** (`IChatClient`) | Requires language model capabilities beyond pattern matching; no orchestration needed | | Agentic workflows: tool/function calling, multi-step reasoning, agent loops, multi-agent collaboration | **Microsoft Agent Framework** (`Microsoft.Agents.AI...