# microsoft/LMOps

General technology for enabling AI capabilities w/ LLMs and MLLMs

Repository: https://github.com/microsoft/LMOps
Canonical: https://ross.abutalabs.com/products/lmops
Homepage: https://aka.ms/GeneralAI
Language: Python
License: MIT
License Family: permissive
Topics: nlp, agi, gpt, llm, lm, pretraining, prompt, lmops, promptist, x-prompt, language-model
Last push: 2026-07-25T06:15:21+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 35, longevity 97
- inputs: {"age_days": 1359, "days_push": 39, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4465, forks 377 (observed 2026-08-28T04:08:50.851979+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: prompt-engineering, llm-inference, machine-learning, rag
- domain: large-language-models, artificial-intelligence
- platform: python
- tags: prompt-optimization, in-context-learning, llm-acceleration, research-papers, microsoft, natural-language-processing, research

## Member repositories
- microsoft/LMOps (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:50.851979+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:39.255311+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/LMOps (fetched 2026-08-28T04:08:50.851979+00:00, sha 69af5795daa5)
- Data as of 2026-08-30T08:39:29.467469+00:00.