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A harness is the system around a model that enables it to act as an agent: prompts, tools, memory, skills, orchestration, and more. Harnessing is the process of optimizing that system.

Independent Auditing of AI Agents. Run by human or the agent itself, to answer the most crucial question in the AI Agent Economy. Is the agent doing what is supposed to do?

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. cpp and MLX.

[NeurIPS 2026] Safety failures in coding agents and frontier LLMs. Reproducible workspaces for studying when AI agents generate unsafe data during task completion.

Fully automatic censorship removal for language models

Praxist is an autonomous research system for measurable, computer-executable research. It coordinates parallel research peers, task-owned evaluation, durable evidence, and generation-to-generation synthesis.

Personal AI agents are exploding in popularity, but nearly all of them still route intelligence through cloud APIs. Your "personal" AI continues to depend on someone else's server. 3× from 2023 to 2025.

Red-teaming and evaluation framework for AI agents, built around an OWASP-inspired ASI01–ASI10 taxonomy

Build your own AI SRE agents. The open source toolkit for the AI era.

As part of model development, we evaluate our models on open research problems. We expanded these evaluations after performance on our existing mathematical evaluations saturated. Some outputs build upon earlier results produced by the models.

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

is a CLI and library for evaluating and red-teaming LLM apps. Stop the trial-and-error approach - start shipping secure, reliable AI apps.

Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own

A practical guide to building enterprise AI agents across their full lifecycle.

A Codex jailbreak prompt and test pack for gpt. 针对 gpt 系列的 Codex 破甲提示词与测试包。

One workspace: what ran, what broke, what is already pinned as a test.

🤖 Agent 应用层 - ✅ Agent / Workflow - LangGraph、OpenAI Agents SDK - AutoGen、CrewAI、Pydantic AI - Dify、n8n、Flowise - ✅ 任务形态 - Research / Coding / Web Agent - Multi-Agent / Supervisor / Handoff - Computer Use / Browser Automation

nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, and inference.

pth） - 支持跨不同 GPU 数量恢复（自动调整 step） - 支持 wandb 训练记录连续性（自动恢复同一个 run）

Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. Maintained by Orchestra Research.

A curated AI Agent learning roadmap for people who want to build useful, reliable agents instead of collecting random links.

skill —— 一个让你的Skill无限进化的系统：评估→改进→测试→保留或回滚 | Autoresearch-inspired autonomous skill optimization for Claude Code. Evaluate, improve, test, keep or revert.

Every notebook can be opened and run on Google Colab directly from the links below. Just click the Open In Colab badge next to the notebook you want to run.

Reef is the first open-source infrastructure for continual self-improving agents. It connects agent inference, feedback, learning, and versioned delivery. Use it to train model weights with Slime and SGLang, or improve an agent's harness, including its prompts, rules, and skills.

If you like the course, don't hesitate to ⭐ star this repository. This helps us to make the course more visible 🤗.

Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.

Companion materials for our survey of how execution experience becomes persistent harness state under a fixed base model. The survey covers update mechanisms, evolutionary search, continual PAC learning, evaluation, applications, and societal impact.