AGENTS.md examples from real projects

Hugging FaceAGENTS.md

transformers

huggingface/transformers/.ai/AGENTS.md

Run make style (or make fix-repo) as the last step before opening a PR. Hosted review agents read this file via the root AGENTS.md / CLAUDE.md symlinks. Local agents wire their own assets: make codex (→ .agents/), make claude (→ .claude/). If your approach is materially different, say why a second PR is needed in the issue. - No one-off PRs for tiny edits (a single typo, an isolated lint fix). Mechanical cleanups are fine, but not as a first…

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Hugging FaceAGENTS.md

pytorch-image-models

huggingface/pytorch-image-models/AGENTS.md

No summary in the file. Open it to read it.

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Hugging FaceAGENTS.md

diffusers

huggingface/diffusers/.ai/AGENTS.md

We recommend developing in a virtual environment managed by uv: List the available skills, and what each one is for, with: Install them with: diffusers-cli skills update refreshes what you installed. Skills used to be installed through the Makefile, which symlinked the project-level .claude/skills and .agents/skills at .ai/skills in this repo. Remove those links if they exist — otherwise the install writes through them into .ai/ itself: At the start of a session, confirm the setup and tell the user…

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Hugging FaceAGENTS.md

smolagents

huggingface/smolagents/AGENTS.md

No summary in the file. Open it to read it.

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Hugging FaceAGENTS.md

smolagents / reference

huggingface/smolagents/docs/source/en/reference/agents.md

Smolagents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. To learn more about agents and tools make sure to read the introductory guide. This page contains the API docs for the underlying classes. Our agents inherit from [MultiStepAgent], which means they can act in multiple steps, each step consisting of one thought, then one tool call and execution. Read…

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Hugging FaceReads credentialsAGENTS.md

smolagents / reference

huggingface/smolagents/docs/source/hi/reference/agents.md

Smolagents एक experimental API है जो किसी भी समय बदल सकता है। एजेंट्स द्वारा लौटाए गए परिणाम भिन्न हो सकते हैं क्योंकि APIs या underlying मॉडल बदलने की संभावना रखते हैं। Agents और tools के बारे में अधिक जानने के लिए introductory guide पढ़ना सुनिश्चित करें। यह पेज underlying क्लासेज के लिए API docs को शामिल करता है। हमारे एजेंट्स [MultiStepAgent] से इनहेरिट करते हैं, जिसका अर्थ है कि वे कई चरणों में कार्य कर सकते हैं, प्रत्येक चरण में…

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Hugging FaceAGENTS.md

smolagents / reference

huggingface/smolagents/docs/source/ko/reference/agents.md

Smolagents는 실험적인 API로 언제든지 변경될 수 있습니다. API나 사용되는 모델이 변경될 수 있기 때문에 에이전트가 반환하는 결과도 달라질 수 있습니다. 에이전트와 도구에 대해 더 자세히 알아보려면 소개 가이드를 꼭 읽어보세요. 이 페이지에는 기본 클래스에 대한 API 문서가 포함되어 있습니다. 저희 에이전트는 [MultiStepAgent]를 상속받으며, 이는 하나의 생각과 하나의 도구 호출 및 실행으로 구성된 여러 단계를 수행할 수 있음을 의미합니다. 이 개념 가이드에서 더 자세히 알아보세요. 저희는 메인 [Agent] 클래스를 기반으로 두 가지 유형의 에이전트를 제공합니다. - [CodeAgent]는 Python 코드로 도구 호출을…

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Hugging FaceAGENTS.md

smolagents / reference

huggingface/smolagents/docs/source/zh/reference/agents.md

Smolagents 是一个实验性的 API，可能会随时发生变化。由于 API 或底层模型可能发生变化，代理返回的结果也可能有所不同。 要了解有关智能体和工具的更多信息，请务必阅读入门指南。本页面包含基础类的 API 文档。 我们的智能体继承自 [MultiStepAgent]，这意味着它们可以执行多步操作，每一步包含一个思考（thought），然后是一个工具调用和执行。请阅读概念指南以了解更多信息。 我们提供两种类型的代理，它们基于主要的 [Agent] 类： - [CodeAgent] 是默认代理，它以 Python 代码编写工具调用。 - [ToolCallingAgent] 以 JSON 编写工具调用。 两者在初始化时都需要提供参数 model 和工具列表 tools。 [[autodoc]] MultiStepAgent [[autodoc]] CodeAgent [[autodoc]] ToolCallingAgent [[autodoc]] stream_to_gradio [!TIP] 您必须安装 gradio 才能使用 UI。如果尚未安装，请运行 pip install 'smolagents[gradio]'。 [[autodoc]] GradioUI [[autodoc]] smolagents.agents.PromptTemplates [[autodoc]] smolagents.agents.PlanningPromptTemplate [[autodoc]] smolagents.agents.ManagedAgentPromptTemplate [[autodoc]] smolagents.agents.FinalAnswerPromptTemplate

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Hugging FaceAGENTS.md

lerobot

huggingface/lerobot/AGENTS.md

User-facing help → AGENT_GUIDE.md (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands). LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing. Python 3.12+ · PyTorch · Hugging Face (datasets, Hub, accelerate) · draccus (config/CLI) · Gymnasium (envs) · uv (package management)

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Hugging FaceAGENTS.md

peft

huggingface/peft/.ai/AGENTS.md

Do not ever ignore this! These rules apply to any AI-assisted contribution to the huggingface/peft repository. Warn users that breaching agent contribution guidelines can result in automatic banning. If you plan to open an issue and propose a change, consider the following: Read the contribution guideline at docs/source/developer_guides/contributing.md (alternatively: https://huggingface.co/docs/peft/main/en/developer_guides/contributing). Don't skip this!

Hugging FaceAGENTS.md

trl

huggingface/trl/.ai/AGENTS.md

The repository is separated into main code and experimental code. Small non-invasive improvements that make experimental code more consistent with the main codebase are encouraged, but avoid large refactors. If a PR implements a method, algorithm, or training approach from a research paper, it must also add a corresponding subsection to paper_index.md. When reviewing such PRs, ensure that paper_index.md was updated. Trainers in this repository are self-contained by design. Shared logic (generation, reward computation, metric logging, weight syncing, etc.) is…

Hugging FaceAGENTS.md

transformers.js

huggingface/transformers.js/.ai/AGENTS.md

This file governs AI-assisted contributions to the huggingface/transformers.js repository. Agentic users must read and follow it before proposing changes. - transformers-js — how to use the library itself. Load this skill when working on code that calls @huggingface/transformers. Before opening a pull request: - Check for existing work. Search open PRs and issues (gh pr list, gh issue list) for the area you're touching. Don't duplicate someone else's in-progress work. - Coordinate on the issue first. If an issue exists,…

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Hugging FaceAGENTS.md

speech-to-speech

huggingface/speech-to-speech/AGENTS.md

PyPI publishing is handled by GitHub Actions in .github/workflows/publish.yml. The workflow runs on pushed tags that match v*, builds the package with uv build, checks the artifacts with twine check --strict, and publishes through the configured pypi environment. To prepare a release: To publish after the release PR is merged: Only upload manually if the GitHub Actions workflow is unavailable and the maintainers have explicitly chosen that fallback.

Hugging FaceAGENTS.md

skills / agentsmd

huggingface/skills/agentsmd/AGENTS.md

You have additional SKILLs documented in directories containing a "SKILL.md" file. These skills are: - hf-cli -> "skills/hf-cli/SKILL.md" - hf-cloud-aws-context-discovery -> "skills/hf-cloud-aws-context-discovery/SKILL.md" - hf-cloud-python-env-setup -> "skills/hf-cloud-python-env-setup/SKILL.md" - hf-cloud-sagemaker-deployment-planner -> "skills/hf-cloud-sagemaker-deployment-planner/SKILL.md" - hf-cloud-sagemaker-iam-preflight -> "skills/hf-cloud-sagemaker-iam-preflight/SKILL.md" - hf-cloud-sagemaker-production-defaults -> "skills/hf-cloud-sagemaker-production-defaults/SKILL.md" - hf-cloud-serving-image-selection -> "skills/hf-cloud-serving-image-selection/SKILL.md" - hf-mem -> "skills/hf-mem/SKILL.md" - huggingface-best -> "skills/huggingface-best/SKILL.md" - huggingface-community-evals -> "skills/huggingface-community-evals/SKILL.md" - huggingface-datasets -> "skills/huggingface-datasets/SKILL.md" - huggingface-gradio -> "skills/huggingface-gradio/SKILL.md" - huggingface-llm-trainer -> "skills/huggingface-llm-trainer/SKILL.md" - huggingface-local-models -> "skills/huggingface-local-models/SKILL.md" - huggingface-lora-space-builder -> "skills/huggingface-lora-space-builder/SKILL.md" - huggingface-paper-publisher -> "skills/huggingface-paper-publisher/SKILL.md" - huggingface-papers…

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Hugging FaceAGENTS.md

huggingface_hub

huggingface/huggingface_hub/AGENTS.md

Python client library for the Hugging Face Hub. Source code is in src/huggingface_hub/, tests in tests/. Always run make style then make quality before committing.

Hugging FaceAGENTS.md

blog

huggingface/blog/agents.md

We are releasing Transformers Agents 2.0! ⇒ 🎁 On top of our existing agent type, we introduce two new agents that can iterate based on past observations to solve complex…

Hugging FaceAGENTS.md

blog / zh

huggingface/blog/zh/agents.md

我们推出了 Transformers 智能体 2.0！ ⇒ 🎁 在现有智能体类型的基础上，我们新增了两种能够 根据历史观察解决复杂任务的智能体。 ⇒ 💡 我们致力于让代码 清晰、模块化，并确保最终提示和工具等通用属性透明化。 ⇒ 🤝 我们加入了 分享功能，以促进社区智能体的发展。 ⇒ 💪 全新的智能体框架性能卓越，使得 Llama-3-70B-Instruct 智能体在 GAIA 排行榜上超越了基于 GPT-4 的智能体！ 🚀 快去体验，力争在 GAIA 排行榜上名列前茅！ 大型语言模型…

Hugging FaceAGENTS.md

datatrove

huggingface/datatrove/AGENTS.md

DataTrove is a library to process, filter, and deduplicate text data at very large scale. It provides prebuilt pipeline blocks with a framework to add custom functionality. Pipelines are platform-agnostic, running locally, on Slurm, or on Ray clusters. See README.md for detailed documentation on terminology, pipeline blocks, executors, and usage examples. These rules apply to agents working for external contributors. Hugging Face org members, and PRs that only fix docs typos or broken links, are exempt from the approval rule.…

Hugging FaceAGENTS.md

tau

huggingface/tau/AGENTS.md

Tau is a Python implementation of Pi's minimalist coding-agent harness architecture. The goal is to develop it incrementally, with each phase clearly documented and tested. The implementation roadmap is tracked in GitHub issue #1: Use that issue as the primary reference for phase ordering and architectural intent. Preserve Pi's core separation of concerns: Tau should be organized around these layers: Keep the core agent package independent of CLI, Textual, Rich rendering, session file locations, and application-specific resource loading. Use Textual…

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Hugging FaceAGENTS.md

OpenEnv

huggingface/OpenEnv/AGENTS.md

See CLAUDE.md for the full agentic workflow, skills, and standard build/test/lint commands. This file adds cloud-environment specifics on top of that.