# Review by Andrej Karpathy (Round 3)

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VERDICT: approve

FINDINGS:
- [src/colonyos/memory.py]: Keyword extraction in `load_memory_for_injection` is naive — splits on whitespace, takes words ≥3 chars. Passes stopwords and prompt boilerplate as "keywords", diluting FTS5 relevance. Acceptable for MVP; the recency fallback is sound. Single most impactful v2 improvement.
- [src/colonyos/memory.py]: FTS5 query wraps all keywords in a single double-quoted phrase, requiring exact phrase match. "pytest PYTHONPATH" won't match a memory mentioning both in different sentences. Switching to individual terms joined by OR would improve recall.
- [src/colonyos/memory.py]: Pruning is global FIFO, not per-category FIFO as PRD specifies. Documented deviation with clear rationale — acceptable at 500-entry cap.
- [src/colonyos/orchestrator.py]: SQLite connection stays open for entire pipeline duration (potentially hours). Not a leak (single connection), but open/close per phase would be marginally cleaner.
- [src/colonyos/orchestrator.py]: `_capture_phase_memory` silently returns empty when `artifacts["result"]` is missing — no debug log for observability. Some phases may never produce memories without anyone noticing.
- [src/colonyos/orchestrator.py]: Memory injection log reports newline count as "memories injected" — off by header lines. Minor debugging confusion.
- [src/colonyos/memory.py]: Token estimation via `chars / 4` systematically undercounts for code-heavy memories. Not a blocker — budget is a soft cap.
- [tests/test_memory.py]: FTS5 escaping tests assert `len(results) >= 0` (always true) — they verify "no crash" but not "correct results". Stronger assertions would catch operator-leakthrough regressions.

SYNTHESIS:
This is a well-executed MVP of a memory system. The architecture makes the right fundamental choices: writes only from the orchestrator (never agent sessions), sanitization before persistence, configurable token budget, and graceful degradation everywhere — a corrupt DB never crashes a pipeline run. The 39-test suite covers the important paths including FTS5 injection safety. Zero new dependencies.

From an LLM-application perspective, the main gap is retrieval quality. The current system is essentially "recent memories from relevant categories, maybe filtered by a naive keyword phrase match." This works for the first 50–100 memories but the signal-to-noise ratio in the injected `## Memory Context` block will degrade as the store grows. The good news: the architecture cleanly supports upgrading retrieval without touching storage or injection points — `load_memory_for_injection` is the single function to improve in v2 (stopword filtering, per-term matching, or even lightweight TF-IDF).

The system will already deliver value by surfacing recent failure context and codebase facts, directly addressing the "cross-run amnesia" problem. I approve for merge.
