# kontextual-engine

Headless knowledge operations engine for turning heterogeneous information
assets into persistent, contextual, governed, retrievable, transformable, and
agent-operable knowledge.

Start here:

- `INTENT.md`
- `wiki/ProductRequirementsDocument.md`
- `wiki/FunctionalRequirementsSpecification.md`
- `wiki/kontextual-engine_scope_research_md_bundle/`
- `SCOPE.md`
- `docs/knowledge-operations-roadmap.md`
- `docs/architecture-blueprint.md`
- `docs/architecture-core-implementation.md`
- `docs/asset-registry-implementation.md`
- `docs/stack-decision.md`
- `docs/markitect-main-scope-assessment.md`
- `docs/markitect-tool-reuse-boundary.md`
- `docs/markitect-tool-integration-usecases.md`
- `docs/markitect-tool-capacity-risks.md`
- `examples/markitect-tool-contract/`
- `docs/test-performance-monitoring.md`
- `docs/phase-memory-boundary.md`
- `docs/system-layer-extraction-inventory.md`
- `docs/system-layer-migration-backlog.md`
- `docs/service-api-boundary.md`
- `workplans/`

## Development

This repo uses Python 3.12+, setuptools, a `src/` package layout, and pytest.

```bash
python3 -m pytest
```

Pytest records a compact rolling performance history under
`.pytest_cache/kontextual/performance-history.json`; see
`docs/test-performance-monitoring.md`.

The first runtime slice implements artifacts, collections, relationships,
in-memory storage, ingestion adapters, query, workflow run manifests, and
agent-facing context packages. The current roadmap re-scopes the next work
around the V0.2 knowledge operations vision: governed asset identity,
multi-format ingestion, retrieval, traceable transformations, workflows,
service APIs, agent-safe operation, observability, and export.