Existing prod storage was written by a newer Qdrant (qdrant/qdrant:latest
resolved to 1.18.1 on 2026-05-23). Pinning to 1.12.4 caused a shard-holder
deserialization panic on startup because Qdrant's storage format is
forward-compatible (newer reads older) but not backward.
Pin mem0ai[nlp]==2.0.2 and fastembed for the new hybrid-search pipeline.
Drop OSS graph memory (removed upstream in 2.0.0, PR #4805): remove Neo4j
service, env vars, volumes, and driver deps; mark /graph/relationships
deprecated. Rewrite Memory.search/get_all/chat/health call sites to use
the v2 filters={} + top_k API (entity IDs at top level now raise
ValueError). Tighten MCP remove_memory ownership check to O(1)
verify_memory_ownership so it doesn't silently truncate at the new
top_k=20 default. Downgrade base image to python:3.12-slim for spaCy.
Adds scripts/migrate_qdrant_to_v3.py (scroll+upsert with per-user count
parity check) and docs/MIGRATION_RUNBOOK.md covering snapshot, dump,
collection rebuild, cutover, and rollback procedures.
Security Enhancement:
- Remove external port exposure for PostgreSQL and Neo4j databases
- Replace 'ports' with 'expose' for internal-only database access
- Maintain full internal connectivity while eliminating external attack vectors
- Follow container security best practices
Benchmarking Framework:
- Add agent1.md: Professional Manager persona testing protocol
- Add agent2.md: Creative Researcher persona testing protocol
- Add benchmark1.md: Baseline test results and analysis
Benchmark Results Summary:
- Core engine quality: 4.9/5 average across both agent personas
- Memory intelligence: Exceptional context retention and relationship inference
- Automatic relationship generation: 50+ meaningful connections from minimal inputs
- Multi-project context management: Seamless switching with persistent context
- Cross-domain synthesis: AI-native capabilities for knowledge work enhancement
Key Findings:
- Core memory technology provides strong competitive moat
- Memory-enhanced conversations unique in market
- Ready for frontend wrapper development
- Establishes quality baseline for future model comparisons
Future Use: Framework enables systematic comparison across different
LLM endpoints, models, and configurations using identical test protocols.
