A curated library of resources for building and evaluating AI agents, collecting papers, talks, blog posts and evaluation tools in one list.
Source: Official GitHub repository. Last checked 2026-09-18. Spot an error? Tell us.
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A Microsoft research framework for evaluating language models with a focus on prompt robustness, including adversarial prompt attacks and prompt engineering method comparisons. The repository is archived and kept for reference.
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.
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 observability and evaluation platform that turns production traces into datasets and prompt improvements, marketed for teams running self-improving agents.
An open-source workspace where engineers and subject-matter experts iterate on prompts and agent configurations side by side, with versioning, evaluation runs and observability.
A companion LangChain package focused on scoring agent trajectories, comparing the sequence of tool calls an agent made against an expected path.