Contents

• pgmnemo
  • Benchmarks (v0.3.0, retrieval-only)
  • Why this exists
  • 30-second quickstart
  • Features
  • Documentation
  • License
  • Contributing
  • Citing

pgmnemo

Multi-agent memory substrate for PostgreSQL — provenance-gated, vector-hybrid recall.

v0.3.0 (2026-05-10): MAGMA edge taxonomy — edge_kind ENUM, per-kind indexes, recall_lessons() BFS fix.

Benchmarks (v0.3.0, retrieval-only)

Real numbers vs published academic benchmarks. Canonical protocol: benchmarks/PROTOCOL.md (v1.0.0, frozen 2026-05-10) — release notes citing recall improvements must reference it. Full reproduction commands in docs/BENCHMARKS.md. Methodology change log in benchmarks/HISTORY.md.

Benchmark	Embedder	Metric	pgmnemo	Comparison
LoCoMo (Maharana ACL 2024)	DRAGON (paper canonical)	recall@10 / MRR	0.795 / 0.548	paper DRAGON range 0.55–0.65 (session-level)
LongMemEval (Wu ICLR 2025)	bge-m3 (subst. for Stella V5)¹	recall@10 / MRR	0.933 / 0.855	BM25 baseline² 0.982

¹ Stella V5 paper canonical incompatible with transformers 5.8 — substituted bge-m3 (1024d, MTEB-strong). Addendum. ² Pure-Python BM25 baseline included for reference: run_nollm.py.

Competitor status: Mem0, Zep, MemGPT, and MAGMA have not published recall@10 on LoCoMo or LongMemEval as of 2026-05-10. pgmnemo is among the first memory substrates to publish reproducible retrieval benchmarks on these academic datasets.

Reproduce in 3 commands: see docs/BENCHMARKS.md#reproducibility.

Honest caveats: BM25 outperforms pgmnemo vector retrieval on LongMemEval (keyword-friendly task). Hybrid retrieval (vector + BM25 RRF) is on the v0.2.2 roadmap.

Why this exists

• One differentiator none of Pinecone, Letta, Mem0, or Zep have: a write-time provenance gate. Every ingest() call must carry a commit_sha or artifact_hash; rows without provenance are blocked (or warned) by default. Hallucinated agent memories cannot silently accumulate.
• No new service. CREATE EXTENSION pgmnemo; in your existing PostgreSQL — no separate API server, no SaaS endpoint, no vendor lock-in.
• Hybrid recall in-database. Cosine similarity (HNSW) + BM25 full-text + recency decay + importance weighting, scored in one SQL call.
• Role isolation built in. First-class role + project_id composite scoping; no hand-rolled RLS.

Aspect	pgmnemo	Generic Vector DB	Cloud Memory API
Provenance enforcement	✅ Mandatory	❌	❌
Zero data egress	✅ In-database	❌	❌
Install model	CREATE EXTENSION	External service	SaaS API
Self-hosted price	Free (Apache 2.0)	$$$$	$$$$$

30-second quickstart

PGXN install (if pgxnclient is available):

pgxn install pgmnemo

From source (Docker):

# 1. Start PG 17 + pgvector
docker run --name pgmnemo-dev -e POSTGRES_PASSWORD=pass -p 5432:5432 -d pgvector/pgvector:pg17

# 2. Build and install the extension (requires make, gcc, pg_config on PATH)
git clone https://github.com/pgmnemo/pgmnemo.git
docker exec pgmnemo-dev bash -c "apt-get install -y postgresql-server-dev-17 make gcc 2>/dev/null; true"
docker cp pgmnemo/extension pgmnemo-dev:/tmp/pgmnemo
docker exec pgmnemo-dev bash -c "cd /tmp/pgmnemo && make && make install"

-- psql -h localhost -U postgres

CREATE EXTENSION pgmnemo CASCADE;

SELECT pgmnemo.ingest(
    p_role        := 'developer',
    p_project_id  := 1,
    p_topic       := 'auth',
    p_lesson_text := 'Rotate JWT secrets after any key-compromise incident.',
    p_commit_sha  := 'abc1234'
);

SELECT lesson_text, score
FROM pgmnemo.recall_lessons(
    query_embedding := array_fill(0, ARRAY[1024])::vector(1024),
    query_text      := 'JWT secret rotation',
    role_filter     := 'developer'
);

For a native install (no Docker), see INSTALL.md.

Features

• HNSW vector search — fast approximate nearest-neighbour recall via pgvector HNSW indexes
• Provenance gate — enforce / warn / off modes; controlled by pgmnemo.gate_strict GUC
• Recency-weighted scoring — 0.5×cosine + 0.2×importance + γ×recency(90d) + 0.1×prov_strength; γ tunable via pgmnemo.recency_weight
• Role scoping — role + project_id composite isolation; role_filter=NULL pools across roles
• Graph traversal — traverse_causal_chain() and traverse_temporal_window() walk typed mem_edge relationships between lessons
• MAGMA edge taxonomy (v0.3.0, EXPERIMENTAL) — edge_kind ENUM (semantic | temporal | causal | entity) with per-kind partial indexes; recall_lessons() BFS graph-proximity now correctly uses edge_kind instead of the broken v0.2.x relation_type string matching. MAGMA §4 (adaptive traversal policy) and §5 (dual-stream consolidation) are not yet implemented.

Documentation

• INSTALL.md — build, install, configure, upgrade
• docs/USAGE.md — API reference and tuning guide
• CHANGELOG.md — version history
• docs/MIGRATION.md — upgrade path and migration notes
• docs/PRODUCTION_READINESS.md — production deployment checklist
• examples/ — annotated runnable examples (init, ingestion, recall)

License

Apache License 2.0 — see LICENSE.

Contributing

See CONTRIBUTING.md. Contributions accepted under the DCO sign-off model.

Citing

@misc{gaydabura2026pgmnemo,
  author = {Gaydabura, Alex and pgmnemo contributors},
  title  = {pgmnemo: A Provenance-Gated Multi-Agent Memory Substrate for PostgreSQL},
  year   = {2026},
  note   = {ICSE-SEIP submission in preparation}
}