A Microsoft Research collection of projects on making language models usable in production, including automatic prompt optimization, prompt compression and structured prompting techniques.
Source: Official GitHub repository. Last checked 2026-09-18. Spot an error? Tell us.
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A written set of principles for building reliable LLM software, including factors on owning your prompts, owning context construction and treating tools as structured output.
The official implementation of the Graph of Thoughts paper, which models a prompt-solving process as a graph of thoughts that can be merged, refined and scored.
A plug-and-play implementation of tree-of-thoughts prompting, where a model explores and evaluates multiple reasoning branches before committing to an answer.
A reference implementation of a training-free prompting strategy that asks a model for a distribution of responses with probabilities, which the authors report reduces mode collapse in creative tasks.
A library for building LLM applications that also auto-tunes them, optimizing prompt text and few-shot demonstrations against a training set. It aims to make prompt tuning as routine as model training.
An open-source, locally running SDK that provides one interface for calling many model providers, with batching, retries and caching handled in the library rather than a hosted service.