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.

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.