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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
| Date | Stars |
|---|---|
| 2026-07-24 | 1333 |
| 2026-07-25 | 1335 |
| 2026-07-28 | 1335 |
| 2026-07-30 | 1335 |
| 2026-07-31 | 1337 |
| 2026-08-06 | 1337 |
Today
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This week
+2 stars this week
This month
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Momentum
3.4
growth rate 0.15%/day
# Agent Apprenticeship [](https://www.npmjs.com/package/agent-apprenticeship) [](https://huggingface.co/datasets/Forsy-AI/agent-apprenticeship-seed-dataset_v0.2) **Real-world agent work experience, looped into collective learning.** The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents. ```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 complex workflows advance through agent loops that turn execution into shared improvement. Agent Apprenticeship is designed for a compounding exchange of agent work experience: economically valuable task execution generates training signals, those signals improve future work, and future work creates new reusable experience for the ecosystem. Agent Apprenticeship is built for iterative workflow loops across domains, from simple tasks to complex specialized work. Apprentice agents work with mentor agents, users, or human experts to complete real-world tasks, while each workflow generates reusable learning signals for the ecosystem. The latest seed dataset includes: - 500+ curated seed tasks sourced and grounded from the real world - 495 reusable agent lessons - 1000+ full agent execution traces - 1000+ agent work episodes / task rollouts - 505 full agent work experience compilations - 39k+ structured experience compilation records 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. Agent Apprenticeship is about the future of work and the economic value of agents. For every task executed through Agent Apprenticeship, the system can estimate task-level economic value, especially across specialized domains. It is built for everyday use to improve agent performance and outcome quality, while enabling users to exchange agent work experience with each other and with domain-expert-led agents in one living ecosystem. ## Install ```bash npx agent-apprenticeship init ``` Or install globally: ```bash npm install -g agent-apprenticeship apprentice init ``` The installed command is: ```bash apprentice ``` The long-form command also remains available: ```bash agent-apprenticeship ``` ## Quickstart Start Agent Apprenticeship: ```bash npx agent-apprenticeship init ``` Use defaults: ```bash npx agent-apprenticeship init --defaults ``` Check your setup: ```bash apprentice settings apprentice doctor ``` Configure your Apprentice Agent, Mentor Model Provider, and Apprenticeship Mode: ```bash apprentice configure apprentice configure model apprentice settings ``` Apprenticeship Modes: ```text Autonomous Expert-Led Organization Custom ``` Store Mentor Model Provider keys in: ```bash ~/.agent-apprenticeship/.env.local ``` Example: ```bash OPENAI_API_KEY="" ANTHROPIC_API_KEY="" GEMINI_API_KEY="" OPENROUTER_API_KEY="" ``` You can also use shell environment variables for the current terminal session: ```bash export OPE
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matched fp:40ef37f93348980a, topic:ai-agents, topic:autonomous-agents, topic:agentic-ai