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