Implements the subset of the hosted mem0 platform API that mem0ai==2.0.2
MemoryClient calls, so MemoryClient(host=..., api_key=...) works against this
server. Verified end-to-end (construct/add/search/get_all/get/history/update/delete).
- platform_compat.py: GET /v1/ping/ (returns non-empty org_id/project_id, which
the SDK's Project init requires), POST /v3/memories/{add,search}/,
POST /v3/memories/ (paginated get_all), /v1/memories/{id}/ item ops, and
GET /v1/entities/ -- all mapped onto the existing mem0_manager.
- auth.get_current_user_platform: accepts Authorization: Token (mem0 SDK),
Bearer, or X-API-Key.
- main.py: include the platform router; remove the /v1/memories* aliases added
in ea07a82 (the SDK uses /v3 and trailing-slash /v1/memories/{id}/, not those
paths); keep /v1/chat/completions and the native /memories* routes.
- docker-compose: run uvicorn with --proxy-headers --forwarded-allow-ips=* so the
proxy's https scheme is honoured. This stops trailing-slash 307 redirects from
downgrading https->http and dropping the Authorization header -- the actual
cause of the reported "POST auth broken" symptom (auth was never broken).
- test_sdk_compat.py: end-to-end MemoryClient round-trip against the server.
Backend startup needs ~30-60s (spaCy NLP models load, mem0 v2 init,
MCP session manager, 4 workers). The Dockerfile's 5s start-period was
too short, causing willfarrell/autoheal (running on the host with
AUTOHEAL_CONTAINER_LABEL=all) to kill the container before it finished
booting. Overriding the healthcheck in compose with a longer start_period
keeps failures from counting until the app is actually ready.
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.
