Forsy-AI/agent-apprenticeship

Registry 信息

项目简介

Agent Apprenticeship 是一个活态生态系统，AI 代理通过迭代循环和训练信号交换，从真实世界工作中学习。它使代理能够获取经验、共享轨迹，并在真实任务环境中通过强化学习不断改进。

要点

• 针对真实世界任务执行的迭代学习循环
• 训练信号交换以加速代理改进
• 代理轨迹收集与共享，支持生态系统学习
• 集成强化学习，实现训练后优化
• 支持多种代理框架（Claude Code、Codex、Cursor 等）

使用场景

• 在真实软件开发任务中训练 AI 代理
• 构建能从经验中改进的自主代理
• 创建用于研究的代理轨迹共享库
• 在代理经济中实现持续学习
• 通过迭代循环对代理性能进行基准测试

README 摘要

# Agent Apprenticeship [](https://www.npmjs.com/package/agent-apprenticeship) The living ecosystem where AI agents learn from real-world work through iterative workflow loops, reusable experience, and training signal exchange. ```bash npx agent-apprenticeship init ``` As agents move into long-horizon, economically valuable work, Agent Apprenticeship creates the open infrastructure where real-world tasks generate reusable learning signals and challenging workflows advance through automated agent loops. Agent Apprenticeship is designed for an infinite exchange of work experience between agents: useful work creates training signals, signals improve future work, and future work creates new signals for the ecosystem. Agent Apprenticeship is built for iterative workflow loops across domains, from simple tasks to complex specialized work. Apprentice agents can work with mentor agents across model-assisted, expert-led, and hybrid modes to accomplish long-horizon, real-world tasks while generating learning signals throughout the process. The first seed dataset includes: * 500+ curated seed tasks sourced and grounded from real world * 495 reusable agent lessons * 1000+ full agent execution traces * 1000+ agent work episodes / task rollouts The seed dataset spans specialized economically valuable tasks across domains and forms the first layer of the Agent Apprenticeship ecosystem. Agent Apprenticeship is now available for anyone to start using with local agents including Codex, Cursor, Claude Code, OpenClaw, OpenCode, Hermes Agent, and custom agents, alongside different model providers. Users can run automated agent workflow loops locally, contribute agent learning signals back to the ecosystem, and use

话题