Begin adding AI memory

artifacts/plans/15-simplify-task-settings-and-more.md

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+task settings sound complicated, make them simpler

artifacts/plans/16-agent-knowledge-memory-foundation.md

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+# Feature Plan: Agent Knowledge Memory Foundation (Pre-11/12)
+
+## Goal
+Establish a scalable, queryable memory substrate so wiki and MCP features can rely on fast retrieval instead of markdown-file scans.
+
+## Why This Comes Before 11/12
+- Plan 11 (personal memory) needs performant retrieval and indexing guarantees.
+- Plan 12 (MCP wiki/tools) needs a stable backend abstraction independent of UI and tool transport.
+
+## Scope
+- Pluggable memory search backend interface.
+- Default Django backend for zero-infra operation.
+- Optional Manticore backend for scalable full-text/vector-ready indexing.
+- Reindex + query operational commands.
+- System diagnostics endpoints for backend status and query inspection.
+
+## Implementation Slice
+1. Add `core/memory/search_backend.py` abstraction and backends.
+2. Add `memory_search_reindex` and `memory_search_query` management commands.
+3. Add system APIs:
+   - backend status
+   - memory query
+4. Add lightweight Podman utility script for Manticore runtime.
+5. Add tests for diagnostics and query behavior.
+
+## Acceptance Criteria
+- Memory retrieval works with `MEMORY_SEARCH_BACKEND=django` out of the box.
+- Switching to `MEMORY_SEARCH_BACKEND=manticore` requires only env/config + container startup.
+- Operators can verify backend health and query output from system settings.
+
+## Out of Scope
+- Full wiki article model/UI.
+- Full MCP server process/tooling.
+- Embedding generation pipeline (next slice after backend foundation).

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.