ADR 0001: Start with a local Python and SQLite kernel

• Status: accepted for Prototype A
• Date: 2026-07-21

Context

The repository is greenfield. The first risk is architectural validity, not distributed scale. Requiring a vector database, graph database, hosted model, or API key would make the earliest results slower to reproduce and would mix infrastructure effects with the memory design.

Decision

Build the deterministic kernel in Python 3.12 using the standard library and SQLite FTS5. Keep storage, extraction, embedding, reranking, and token-counting behind narrow interfaces.

The import package is evicortex. The distribution remains evicortex-research until the PyPI namespace and formal trademark checks are completed; no public package name is reserved.

Consequences

Benefits:

• one-file local setup and reproducible tests;
• ACID transactions, FTS5, indexes, and a mature query planner;
• no network, model, or paid-service dependency for the baseline;
• easy inspection of invariants and benchmark artifacts.

Costs:

• no dense semantic retrieval in Prototype A;
• graph traversal and rank fusion must be implemented in application code;
• write concurrency and very large deployments will require another adapter;
• SQLite benchmark results cannot be presented as distributed production results.

Exit criteria

Add or replace storage only after profiling identifies a concrete limit. A replacement must pass the same conformance suite for evidence fidelity, temporal claims, isolation, expansion, and deterministic retrieval traces.