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

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).