microsoft/LMOps

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4465 stars · 377 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Microsoft research initiative collecting code and papers on fundamental techniques for building AI products with large language models, covering prompt optimization, longer context, LLM alignment, and inference acceleration. It bundles research implementations like Promptist, structured prompting, and lossless LLM acceleration.

Use cases

• automatically optimize prompts for text-to-image generation
• speed up LLM inference without quality loss
• scale in-context learning to many examples
• retrieve useful in-context demonstrations for a prompt
• adapt an LLM to a new domain
• learn how GPT-style models do in-context learning

When to choose

• you are doing research on prompting or LLM efficiency techniques
• you want reference implementations of prompt optimization methods like Promptist
• you need lossless LLM inference acceleration code
• you want to explore in-context learning at scale

When to avoid

• you need a production-ready LLM ops platform with monitoring and deployment
• you want a polished end-user tool rather than research code
• you need a maintained framework with stable APIs - components are research artifacts

Facets

library · maturity active

prompt-engineering llm-inference machine-learning rag large-language-models artificial-intelligence python prompt-optimization in-context-learning llm-acceleration research-papers microsoft natural-language-processing research

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/LMOps")