ray-r-ren/agent-apprenticeship
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. observed · 2026-08-28
Health v2 · maintenance only
63/100
- Activity 91
- Release rhythm 59
- Longevity 5
Flags: young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 75
- days_rel: 61
- days_push: 58
- n_releases_24m: 1
Adoption not part of the score
1335 stars · 59 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Agent Apprenticeship is an open ecosystem and CLI tool where AI agents complete real-world tasks through iterative workflow loops, are evaluated by mentor agents or humans, and convert completed work into reusable experience data and training signals. It ships with a seed dataset of curated tasks, agent execution traces, lessons, and experience compilations to improve future agents.
Use cases
- generate training data from real agent task executions
- run automated agent workflow loops locally with claude code or codex
- collect agent traces and lessons for post-training
- evaluate agent work with mentor agents or human review
- improve my agent's performance using shared ecosystem learning signals
- estimate economic value of agent tasks
- bootstrap agent fine-tuning datasets from real-world tasks
When to choose
- you want to turn agent task executions into reusable training data
- you run coding agents like Claude Code, Codex, or Cursor and want iterative improvement loops
- you need a seed dataset of real-world agent tasks, traces, and lessons
- you want human- or mentor-in-the-loop evaluation of agent work
When to avoid
- you need a simple single-shot agent framework without learning loops
- you require a hosted managed service rather than local CLI-driven workflows
- your use case is unrelated to agent training or experience accumulation
Facets
framework · maturity active
agent-framework machine-learning workflow-automation data-generation rag machine-learning artificial-intelligence developer-tools python cross-platform cli agent-learning agent-traces post-training loop-engineering training-signals mentor-agents human-in-the-loop agent-economy seed-dataset claude-code codex cursor ai-agents automation nodejs
2 sources
- readme: https://github.com/ray-r-ren/agent-apprenticeship · fetched 2026-08-28 · d8e3d72e67a1
- homepage: https://forsy.ai · fetched 2026-08-29 · f6f4c617e483
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ray-r-ren/agent-apprenticeship | main | 63 |
For agents
markdown · JSON · MCP: product_card(name="ray-r-ren/agent-apprenticeship")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem