Metadata-Version: 2.4
Name: engrammic-primitives
Version: 0.1.1
Summary: EAG schema primitives for epistemic context management
Author-email: Aliasgar Khimani <aliasgar.khimani@engrammic.ai>
License: Apache-2.0
License-File: LICENSE
Keywords: ai-agents,context-management,graphrag,knowledge-graph
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.13
Requires-Dist: pydantic>=2.0
Description-Content-Type: text/markdown

# Engrammic Primitives

EAG schema primitives for building epistemic context systems.

## Install

```bash
pip install engrammic-primitives
```

## Usage

```python
from primitives.schema import PersistenceLayer, MemoryLabel, KnowledgeLabel
from primitives.eag import combined_confidence, should_promote_r1

# Check persistence layer for a label
layer = PersistenceLayer.MEMORY
assert MemoryLabel.UTTERANCE.layer == layer

# Combine confidence scores
conf = combined_confidence(base=0.7, corroboration=0.85)

# Check if a claim should promote to Fact
decision = should_promote_r1(confidence=0.8, corroboration_count=3)
```

## When to Use

Building your own EAG-compatible system or extending Engrammic.

For using Engrammic directly, see:
- [engrammic-mcp](https://github.com/engrammic-ai/mcp) - hosted service
- [engrammic-engine](https://github.com/engrammic-ai/engine) - local engine

## Learn More

- [EAG Paradigm](docs/README.md) - the four-layer cognitive architecture

## Modules

| Module | Purpose |
|--------|---------|
| `primitives.schema` | Node and edge type definitions |
| `primitives.eag` | EAG-specific implementations |
| `primitives.protocols` | Storage and lifecycle interfaces |
| `primitives.scoring` | Decay and freshness formulas |

## License

Apache 2.0
