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This repository was archived by the owner on Sep 12, 2026. It is now read-only.

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learning-ai

Historical learning utilities consolidated into Loom, Knowledge, Evaluation, and Strategy.

The original package combined feedback detection, experience recording, learning extraction, and Thompson Sampling reward tracking. Those concerns now have clearer architectural homes.

Migration map

Former capability Canonical destination
User feedback / correction detection evaluation (evaluation_ai/feedback.py)
Experience/task recording Loom evidence and checkpoint/run history + storage
Learning extraction knowledge and Loom Knowledge APIs
Strategy rewards strategy
Thompson Sampling state strategy

The important distinction is that Knowledge is durable understanding, not a raw conversation or experience log. Interaction observations, evidence, and evaluation are inputs from which Knowledge may be derived.

Status

Retirement candidate. The reusable conversation-feedback matcher has been extracted to evaluation. The remaining historical classes intentionally have not been ported because their responsibilities are already owned by canonical Loom capabilities.

No new architectural capabilities should be added here. Preserve this repository only as historical source and archive it when convenient.

Loom's interaction loop is:

human interaction
      ↓
observation
      ↓
Intent / execution / evaluation
      ↓
Knowledge derivation
      ↓
future interpretation and decisions

License

MIT

About

FlossWare learning-ai - zero-dependency Python package for AI/LLM orchestration

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