Knowledge Graph Kernel for Agent Reasoning

AI systems need durable, trustworthy knowledge. Plain fact storage throws away how something was known, whether it's still valid, or what to do when two sources disagree — most memory layers let you silently overwrite or delete history.

KGK supplies application-neutral laws for durable knowledge: append-only events, immutable statements, provenance, and soft retraction that invalidates without erasing. It also tracks bi-temporal views (real-world validity time versus system observation time), epistemic status, conflict handling, entity consolidation, and hybrid retrieval. Applications decide domain meaning and authority tiers; the kernel just guarantees how assertions entered and changed state.

It's a Python kernel exposed via a Python API, CLI, and FastAPI, with exchangeable in-memory and SQLite backends behind narrow interfaces. Retrieval combines lexical and vector fusion with graph traversal. It's verified with pytest and ruff, and demonstrable end to end: ingest two conflicting assertions, inspect the conflict view, retract or supersede one, then replay the event ledger to confirm it reproduces the same projection.