Amu Pgvector MCP Server

amu-pgvector

Reference implementation of Lineage-Aware Memory Governance (Sangaraju & Vissa, IEEE Access, 10.1109/ACCESS.2026.3730363) on PostgreSQL + pgvector.

The problem

Say Finance computes avg(income) for a customer segment and caches the result so the next agent that asks the same question doesn't have to recompute it. Later, a Marketing agent asks a semantically similar question -- "what's the average customer income by segment?" -- and a naive shared memory, gated only on the metric's name or content tags, serves back Finance's cached result. Marketing was never permitted to see income. The result itself doesn't look like income -- it's a number -- but it was derived from a column Marketing isn't permitted to touch, and nothing about matching on metric name or content catches that. That's the leak this project closes: gating reuse on how a result was derived, not on what it looks like.

60-second quickstart

git clone https://github.com/sangaraju1988/amu-pgvector.git
cd amu-pgvector
docker compose up -d
psql -h localhost -p 5433 -U amu_owner -d amu_dev -f sql/amu_pgvector.sql
# password: amu_owner_password

uv sync --all-packages --all-extras   # or: pip install amu-pgvector

from amu_pgvector import AMUStore
from amu_pgvector.embeddings import fake_embedder

DSN = "postgresql://amu_owner:amu_owner_password@localhost:5433/amu_dev"
embed = fake_embedder(dim=1536)  # swap for a real embedding model in production

admin = AMUStore(DSN)
admin.register_sensitive_column("income")
admin.grant_department_permission("Finance", "income")
admin.create_agent_role("finance_agent", "Finance", "finance_pw")
admin.create_agent_role("marketing_agent", "Marketing", "marketing_pw")

admin.record(
    "SELECT avg(income) FROM customers",
    {"avg": 82000},
    metric_name="avg_income",
    description="average customer income",
    owner_department="Finance",
    embed_fn=embed,
)

finance_dsn = "postgresql://finance_agent:finance_pw@localhost:5433/amu_dev"
marketing_dsn = "postgresql://marketing_agent:marketing_pw@localhost:5433/amu_dev"

AMUStore(finance_dsn).search("average customer income", k=5, embed_fn=embed)
# -> [SearchResult(metric_name='avg_income', ...)]

AMUStore(marketing_dsn).search("average customer income", k=5, embed_fn=embed)
# -> [] -- Marketing was never granted `income`, so Postgres itself
#          never returns the row, regardless of how the query is asked.

Every line above was run against this exact repo before being written here (see docs/design.md for what a from-scratch install actually looks like). amu_owner/amu_owner_password is the docker-compose default -- change it for anything beyond local development.

How the gate works

amu.memory_units is a normal Postgres table with FORCE ROW LEVEL SECURITY enabled. Every write goes through a trigger that extracts the lineage of the SQL that actually produced the cached result (via amu-governance's sql_lineage module -- not self-reported by the agent), closes it over any registered materialized-view/derived-table edges, and computes S(a): the set of sensitive columns that lineage actually touches. A SELECT policy enforces S(a) ⊆ P(d) -- the row is visible only if every column in S(a) is in the requesting role's permitted set P(d) -- for every query against the table, from every client: raw SQL, amu.search(), the Python client, the LangChain integration, and the MCP server all go through the same policy, because it's the table's policy, not any one client's filter.

sequenceDiagram
    participant Writer as amu_writer role<br/>(trusted interception layer)
    participant PG as Postgres: amu.memory_units<br/>(FORCE ROW LEVEL SECURITY)
    participant Trigger as BEFORE INSERT trigger
    participant Finance as finance_agent role
    participant Marketing as marketing_agent role

    Writer->>PG: INSERT ... lineage = {"steps":[{"table":"customers","columns_used":["income"]}]}
    PG->>Trigger: compute lineage_columns, S(a), lineage_status
    Trigger-->>PG: sensitive_columns = {income}, resolved

    Finance->>PG: SELECT ... (session_user = finance_agent)
    PG->>PG: S(a)={income} subset P(Finance)={income,...}? yes
    PG-->>Finance: row returned

    Marketing->>PG: SELECT ... (session_user = marketing_agent)
    PG->>PG: S(a)={income} subset P(Marketing)={...}? no
    PG-->>Marketing: row not returned

See docs/design.md for how every paper concept maps to a specific SQL object, and docs/sql-reference.md for an exhaustive reference of every table, function, role and policy.

Security model and limits

Protected: cross-department leakage of a cached result's derivation columns (the core guarantee, enforced by RLS); silent metric-definition conflicts (amu.check_conflict()); a column reclassified as sensitive after AMUs already exist (a trigger recomputes affected rows, no rewrite needed); lineage closure that silently truncates a long or cyclic derivation chain (fails closed -- marks the AMU unresolved and invisible, rather than serving a partially-traced result).

Explicitly out of scope: a writer that lies about lineage (the SQL gate trusts amu_writer's input by design); inference by combining several individually-permitted AMUs; Postgres superusers, who always bypass RLS; timing side channels; a "convenience mode" identity option (off by default) that's only as safe as the boundary between trusted application code and agent-influenced code on a pooled connection.

Full writeup: docs/threat-model.md.

Benchmarks

Every number below is quoted directly from results/SUMMARY.md, generated by benchmarks/generate_summary.py from committed results/<name>/<timestamp>/ runs (git SHA, Postgres/pgvector versions, hardware, seed and parameters in each run's manifest.json). Re-run the benchmark scripts yourself to reproduce or challenge these; nothing here is estimated.

Leak rate (2000 requests over 600 synthetic AMUs, Finance/Marketing/Support):

Latency (p50/p95 at k=10; RLS and hnsw.iterative_scan each on/off, same role/queries/data per row):

Read honestly: hnsw.iterative_scan=relaxed_order (what amu.search() uses by default, so that filtered results still hit k even when most rows are hidden) costs real latency at scale -- roughly 16x slower than iterative-off at 100k rows in this run. That's the price of the recall guarantee below; if your visible fraction is consistently high, you may prefer to query without it. 1M-row scale was not attempted: the 100k HNSW index build alone took about 7 minutes on the machine this was run on (Docker Desktop on macOS, sharing the host with unrelated workloads) -- recorded honestly rather than extrapolated.

Filtered recall (recall@10 of gated HNSW search vs. exact brute-force over the same role's actually-visible rows, n=20,000):

Integrations

• Plain SQL -- sql/amu_pgvector.sql is the artifact that matters most: idempotent, installs on a managed Postgres as a normal database owner (no superuser), no C extensions beyond vector. Works identically on Supabase and Neon (see submissions/supabase-neon-guide.md for a from-scratch tutorial covering both).
• LangChain -- langchain-amu provides AMUVectorStore and AMURetriever. Every read runs under the store's own Postgres role, so RLS gates LangChain the same way it gates raw SQL. See packages/langchain-amu/.
• MCP -- pip install amu-pgvector[mcp] adds an amu-pgvector-mcp console script exposing amu_search, amu_record and amu_check_conflict as tools, gated the same way. See packages/amu-pgvector/src/amu_pgvector/mcp_server.py.

Citation

@article{sangaraju2026lineage,
  title   = {Lineage-Aware Memory Governance: A Derivation-Gated Framework
             for Privacy-Preserving Column-Level Access Control in
             Enterprise AI Agents},
  author  = {Sangaraju, Venkata and Vissa, Sudhir},
  journal = {IEEE Access},
  year    = {2026},
  doi     = {10.1109/ACCESS.2026.3730363}
}

See CITATION.cff to also cite this software directly.

License

MIT. See LICENSE.

Related

• Paper: Sangaraju & Vissa, IEEE Access, DOI 10.1109/ACCESS.2026.3730363
• Reference library: amu-governance (PyPI)
• docs/design.md -- paper concepts mapped to SQL objects, plus every real gotcha found while building this
• docs/threat-model.md -- what's protected, what's not
• docs/sql-reference.md -- every table, function, role and policy