Metadata-Version: 2.4
Name: kestrel-feature-parametric-self
Version: 0.1.0
Summary: Owned parametric self for Kestrel Sovereign agents — a per-agent local model nightly-finetuned on the agent's own experience
License-Expression: Apache-2.0
License-File: LICENSE
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: <3.15,>=3.11
Requires-Dist: kestrel-sovereign-sdk<1,>=0.14.1
Requires-Dist: kestrel-sovereign<1,>=0.13.1
Provides-Extra: test
Requires-Dist: pytest-asyncio>=1.1.0; extra == 'test'
Requires-Dist: pytest-timeout>=2.3.1; extra == 'test'
Requires-Dist: pytest>=8.0.0; extra == 'test'
Description-Content-Type: text/markdown

# kestrel-feature-parametric-self

The agent's **owned parametric self** for Kestrel Sovereign.

A per-agent local model (target: Gemma 4 31B, 4-bit MLX) that is
nightly-finetuned during the sleep cycle on the agent's own experience, and —
once proven — consulted in the agent's reasoning loop as a disposition prior
and on-demand oracle alongside the frontier model.

Slogan: **rent intelligence, own identity.** This is the *parametric*
counterpart to reflection's *symbolic* self-model (the weights, not a trait
dict). It is **not** memory — RAG remains the factual layer.

Design: `docs/research/TWO_BRAIN_ARCHITECTURE.md` in `kestrel-sovereign`.
Build plan: epic #1.

> **Status: P0 scaffold.** The feature loads and registers; the MLX trainer,
> reflection-derived corpus, fidelity gate, and in-loop integration land in
> later phases. The Apple-Silicon trainer is imported lazily, so the package
> installs and CI-validates on any platform.

## Installation

```bash
uv pip install kestrel-feature-parametric-self
```

The package registers `ParametricSelfFeature` through the
`kestrel_sovereign.features` entry point group.

## Development

```bash
uv sync --extra test
uv run --extra test pytest
```
