Begin adding AI memory

artifacts/plans/16-memory-backend-evaluation.md

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+# Memory Backend Evaluation: Manticore vs Alternatives
+
+## Decision Summary
+- **Recommended now:** Manticore for indexed text retrieval and future vector layering.
+- **Default fallback:** Django/ORM backend for zero-infra environments.
+- **Revisit later:** dedicated vector DB only if recall quality or ANN latency requires it.
+
+## Why Manticore Fits This Stage
+- Already present in adjacent infra and codebase history.
+- Runs well as a small standalone container with low operational complexity.
+- Supports SQL-like querying and fast full-text retrieval for agent memory/wiki content.
+- Lets us keep one retrieval abstraction while deferring embedding complexity.
+
+## Tradeoff Notes
+- Manticore-first gives immediate performance over markdown scans.
+- For advanced ANN/vector-only workloads, Qdrant/pgvector/Weaviate may outperform with less custom shaping.
+- A hybrid approach remains possible:
+  - Manticore for lexical + metadata filtering,
+  - optional vector store for semantic recall.
+
+## Practical Rollout
+1. Start with `MEMORY_SEARCH_BACKEND=django` and verify API/command workflows.
+2. Start Manticore container and switch to `MEMORY_SEARCH_BACKEND=manticore`.
+3. Run reindex and validate query latency/quality on real agent workflows.
+4. Add embedding pipeline only after baseline lexical retrieval is stable.