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MIT-licensed Agent Skills collection for context engineering, harness engineering, multi-agent architectures, filesystem context, memory systems, tool design, evaluation, hosted agents, and production agent operating loops for Claude Code, Cursor, Codex, and Open Plugins-compatible agent tools.

Official Hugging Face Agent Skills collection for Claude Code, Codex, Cursor, Gemini CLI, and other skills-compatible agents, covering Hub CLI workflows, datasets, model search, Spaces, Gradio, fine-tuning, evaluations, local models, papers, Trackio, ZeroGPU, transformers.js, TRL, and the Hugging Face MCP server.

MCP server for generating academic diagrams, statistical plots, figure packages, and visual evaluations from research context through PaperBanana's multi-agent illustration pipeline.

Apache-2.0 library for loading, sharing, streaming, inspecting, and preprocessing AI datasets from the Hugging Face Hub or local files.

Apache-2.0 library for loading, computing, comparing, saving, and sharing evaluation modules for machine learning models and datasets.

Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications.

Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces.

Open-source data labeling, annotation, and human-in-the-loop AI evaluation platform for text, images, audio, video, time series, and multimodal datasets.

Open-source evaluation framework for testing RAG systems, prompts, agents, workflows, and other LLM application behavior.

Open-source LLM engineering platform for tracing, prompt management, evaluation, metrics, and observability.

Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments.

Open-source suite of development tools from Microsoft for building LLM applications end to end — create executable flows that link LLMs, prompts, Python, and tools, trace and debug them, evaluate quality against datasets in CI/CD, and deploy to a serving platform.

Inspect LLM traces and spans, manage prompts, explore datasets, and review evaluation experiments from Claude — with the official Arize Phoenix MCP server, built into the open-source Phoenix AI observability platform.

Connect Claude to LangSmith — retrieve conversation threads and traces, fetch and push prompts, browse evaluation datasets and experiments, and access billing usage — with the official LangSmith Model Context Protocol server from LangChain.

Debug, evaluate, and monitor LLM applications from Claude — read traces and spans, score outputs, save prompts, run evaluation experiments, and query project metrics — with the official Opik MCP server by Comet.

Open-source ML and LLM observability framework for evaluating, testing, and monitoring data quality, drift, model behavior, and AI application outputs.

Open-source AI engineering platform for tracing, evaluating, prompt-managing, and deploying agents, LLM applications, and ML models.

Open-source evaluation and tracing framework for measuring AI agents, RAG systems, LLM apps, retrieval quality, feedback metrics, and trace-level regressions.

AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality.

Source-backed agent for designing Inspect AI benchmark tasks, datasets, solver plans, scorer rubrics, model matrices, eval logs, and release-quality prompt evaluation decisions.

Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows.

Mozilla-maintained MCP server for automating Firefox through WebDriver BiDi, with tools for page navigation, snapshots, UID-based input, screenshots, network requests, console messages, dialogs, history, viewport changes, optional JavaScript evaluation, privileged Firefox contexts, preferences, and WebExtension.

Apache-2.0 Python framework for building and sharing machine-learning demos, AI web apps, model interfaces, chatbots, API front ends, and interactive evaluation tools.

Open-source framework for building agentic LLM applications over private data with ingestion, indexes, retrieval, RAG, tools, workflows, and evaluation.

MIT-licensed command-line software-engineering agent for local coding tasks, GitHub issue fixing, trajectory inspection, and SWE-bench style evaluation.

Open-source prompt testing and red-teaming framework for LLM outputs, regressions, evaluations, and security checks.

TypeScript agent framework for building AI agents, workflows, memory, tool calling, and evaluation-backed applications.