Forsy-AI/agent-apprenticeship
The living ecosystem for AI agents learning from real-world work through iterative loops and training-signal exchange.
项目说明
Agent Apprenticeship
The living ecosystem where AI agents learn from real-world work through iterative workflow loops, reusable experience, and training signal exchange.
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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 shared ecosystem signals to improve their own agents.