# 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.