post-graph: PostgreSQL-Backed Graph Database Library

A high-performance Python library for using PostgreSQL as a native graph database. It supports multi-tenant realms, application-level space sub-grouping (space), pgvector similarity search across main & history tables, automatic shadow audit logging, append-only history tables, and high-speed recursive graph traversals (CTEs).

🌟 Key Features

• Table-Per-Vertex & Table-Per-Edge Architecture: Maps graph elements directly to relational tables, taking advantage of PostgreSQL's foreign keys, indexes, and constraints.
• Hierarchical Multi-Tenancy & Space Sub-grouping:
  • Macro-Isolation (realm): Primary tenant partitioning at database or schema level.
  • Micro-Isolation (space): Optional application-level sub-grouping (space VARCHAR(255) DEFAULT 'default') within tables to segregate environments (e.g. production, staging, sandbox, or workspace spaces).
  • Dual Tenant Topologies:
    • Single-Schema Multi-Tenancy: Logical isolation using realm & space column partitions.
    • Schema-Per-Realm: Physical isolation creating dedicated PostgreSQL schema namespaces per tenant (CREATE SCHEMA IF NOT EXISTS "realm_name").
• pgvector Similarity Search (vector_search):
  • Native HNSW vector indexing (vector(dim)) for vertex embeddings.
  • Cosine distance (<=>), Euclidean L2 (<->), and Inner Product (<#>).
  • Multi-scope searching across main tables, associated data history tables, or both combined (search_scope="both").
• Autogenerated BIGSERIAL Primary Keys & Computed fqid:
  • Vertex FQID: {realm}/{table_name}/{id}
  • Edge FQID: {realm}/{from_table}-{to_table}/{id} (using hyphen separator)
  • Automatically populated at PostgreSQL level via GENERATED ALWAYS AS ... STORED NOT NULL.
• Append-Only History Tables ({table_name}_data):
  • Automatically created alongside vertex and edge tables.
  • Stores timestamped JSONB payload updates (data_id, realm, space, id, payload, timestamp, embedding).
  • Supports vector embeddings on data records for historical semantic matching.
  • Cascades deletion when main vertex/edge is deleted (ON DELETE CASCADE).
• Shadow Audit Logging ({table_name}_audit):
  • Operates via PostgreSQL triggers to capture all INSERT, UPDATE, and DELETE events.
  • Logs old/new row state and the initiating user_id passed via session parameters (app.current_user_id).
• Advanced Graph Traversals & Cycle Detection:
  • Recursive CTE queries for neighbor exploration, path discovery, and cycle-free shortest path calculation.
  • Optional check_cycle=True raising CyclicReferenceError during edge creation.
  • Direct object-oriented traversal APIs (vertex.to(), vertex.from_(), step.vertex(), step.add_edge_to()).
• Multiple Async Client Drivers:
  • High-speed asyncpg client (AsyncPostGraph).
  • SQLAlchemy v2.0 async client (SQLAlchemyPostGraph).

📦 Installation

Install post-graph from PyPI:

# Basic installation (includes asyncpg)
pip install post-graph

# Installation with SQLAlchemy support
pip install "post-graph[sqlalchemy]"

# Installation with all optional dependencies
pip install "post-graph[all]"

Using uv:

uv add post-graph
# or with SQLAlchemy support:
uv add "post-graph[sqlalchemy]"

PostgreSQL Setup

To enable pgvector similarity search, ensure the pgvector extension is enabled in PostgreSQL:

CREATE EXTENSION IF NOT EXISTS vector;

🏗️ Database Architecture & Multi-Tenancy Hierarchy

erDiagram
    REALM ||--o{ SPACE : contains
    SPACE ||--o{ VERTICES : contains
    SPACE ||--o{ EDGES : contains

    agent_registry {
        text realm PK
        bigserial id PK
        varchar_255 space "Indexed optional sub-grouping (e.g. production, sandbox)"
        text fqid "Generated: realm/table/id"
        uuid uuid "Unique UUID identifier"
        jsonb payload "JSON metadata"
        vector embedding "HNSW Cosine Vector"
        timestamptz created_at
        timestamptz updated_at
    }

    agent_registry_data {
        bigserial data_id PK
        text realm FK
        bigint id FK
        varchar_255 space
        jsonb payload
        vector embedding
        timestamptz timestamp
    }

    spawns {
        text realm PK, FK
        bigserial id PK
        varchar_255 space
        bigint from_id FK
        bigint to_id FK
        text relation_type
        jsonb payload
        timestamptz created_at
    }

    agent_registry ||--o{ agent_registry_data : "appends history"
    agent_registry ||--o{ spawns : "from_id / to_id"

🚀 Quick Start Guide

import asyncio
from post_graph import AsyncPostGraph

async def main():
    # 1. Initialize client
    client = AsyncPostGraph(dsn="postgresql://user:password@localhost:5432/mydb")
    await client.connect()

    realm = "proj_alpha"

    # 2. Declare Vertex & Edge tables
    await client.create_vertex_table("agents", realm=realm, vector_dim=1536)
    await client.create_edge_table("spawns", from_vertex_table="agents", to_vertex_table="agents", realm=realm)

    # 3. Upsert Vertices with Space Sub-grouping
    v_parent = await client.upsert_vertex(
        table_name="agents",
        realm=realm,
        vertex_id=1,
        space="production",
        payload={"name": "The Prime Orchestrator", "caste": "genesis"},
        embedding=[0.01] * 1536
    )

    v_progeny = await client.upsert_vertex(
        table_name="agents",
        realm=realm,
        vertex_id=2,
        space="production",
        payload={"name": "Specialized Worker", "caste": "progeny"},
        embedding=[0.02] * 1536
    )

    # 4. Create Directed Edge
    edge = await client.add_edge(
        table_name="spawns",
        realm=realm,
        from_id=v_parent.id,
        to_id=v_progeny.id,
        space="production",
        relation_type="spawned_progeny",
        payload={"reason": "Task Delegation"}
    )

    # 5. Query Vertices by Realm & Space
    prod_agents = await client.get_vertices("agents", realm=realm, space="production")
    print(f"Production agents: {[a.payload['name'] for a in prod_agents]}")

    # 6. Object-Oriented Neighbor Traversal
    neighbors = await v_parent.outgoing("spawns")
    for step in neighbors:
        print(f"Parent -> {step.vertex().payload['name']} (Edge: {step.edge.relation_type})")

    await client.close()

if __name__ == "__main__":
    asyncio.run(main())

📚 Comprehensive API Reference

Client Initialization & Management

AsyncPostGraph(dsn, schema_per_realm=False)

Initializes the high-performance asyncpg graph client.

client = AsyncPostGraph(
    dsn="postgresql://postgres:postgres@localhost:5432/postgres",
    schema_per_realm=False  # Set True for physical PostgreSQL schema isolation
)
await client.connect()

SQLAlchemyPostGraph(dsn_or_engine, schema_per_realm=False)

Initializes the SQLAlchemy v2.0 async graph client.

Schema Definition APIs

create_vertex_table(table_name, realm=None, vector_dim=None)

Creates a vertex table, associated audit table ({table_name}_audit), and append-only data history table ({table_name}_data).

await client.create_vertex_table(
    table_name="agents",
    realm="proj_alpha",
    vector_dim=1536  # Enables pgvector HNSW index
)

create_edge_table(table_name=None, from_vertex_table=..., to_vertex_table=..., cascade_delete_from=False, cascade_delete_to=False, realm=None, vector_dim=None)

Creates a directed edge table linking two vertex tables.

Pass vector_dim to give edges their own pgvector embedding column and HNSW index, enabling vector_search_edges. Optional — most workloads reach edges by traversing from a vertex rather than by similarity.

await client.create_edge_table(
    "relations",
    from_vertex_table="entities",
    to_vertex_table="entities",
    realm=realm,
    vector_dim=1536  # Optional: enables semantic search over relationships
)

Vertex Operations

add_vertex / upsert_vertex

Upserts a vertex object into a specific {realm} and optional {space}.

vertex = await client.upsert_vertex(
    table_name="agents",
    realm="proj_alpha",
    vertex_id=101,                  # Optional numeric ID or FQID
    space="production",             # Optional space sub-grouping (default: 'default')
    payload={"name": "Polymath Node"},
    embedding=[0.05] * 1536,         # Optional vector embedding
    user_id="user_admin"            # Optional for shadow audit attribution
)

get_vertex(table_name, realm, vertex_id)

Fetches a single vertex by its numeric ID, UUID, or FQID.

get_vertices(table_name, realm, space=None, limit=None)

Fetches all vertices belonging to a {realm}, with optional filtering by {space}.

# Fetch only production space vertices
prod_vertices = await client.get_vertices("agents", realm="proj_alpha", space="production")

# Fetch all vertices in realm regardless of space
all_vertices = await client.get_vertices("agents", realm="proj_alpha")

delete_vertex(table_name, realm, vertex_id, user_id=None)

Deletes a vertex and automatically cascades deletion to referencing edges and history records.

Edge Operations

add_edge

Creates a directed edge between from_id and to_id.

edge = await client.add_edge(
    table_name="spawns",
    realm="proj_alpha",
    from_id=101,
    to_id=102,
    space="production",
    relation_type="spawned_progeny",
    payload={"timestamp": "2026-07-27"},
    check_cycle=True                # Raises CyclicReferenceError if edge creates a cycle
)

get_edges(table_name, realm, space=None, limit=None)

Fetches edges in a realm, optionally filtered by {space}.

pgvector Semantic Search (vector_search)

Performs high-speed cosine, L2, or inner product vector search using HNSW indexes.

results = await client.vector_search(
    table_name="agents",
    realm="proj_alpha",
    query_vector=[0.05] * 1536,
    top_k=5,
    distance_metric="cosine",      # 'cosine', 'l2', or 'inner_product'
    search_scope="both"             # 'main', 'data', or 'both'
)

for vertex, distance in results:
    print(f"Agent: {vertex.payload['name']} | Distance: {distance:.4f}")

Edge Semantic Search (vector_search_edges)

Edges can carry embeddings too, when the edge table was created with a vector_dim. Supply the vector on add_edge / upsert_edge, then search:

await client.add_edge(
    "relations", realm=realm,
    from_id=zeus.id, to_id=hera.id,
    relation_type="married_to",
    payload={"description": "spouse"},
    embedding=[0.05] * 1536          # Optional: stored when the table has a vector column
)

results = await client.vector_search_edges(
    table_name="relations",
    realm=realm,
    query_vector=[0.05] * 1536,
    top_k=5,
    distance_metric="cosine",
    space="production",              # Optional space filter
    relation_type="married_to"       # Optional relation type filter
)

for edge, distance in results:
    print(f"{edge.relation_type} | Distance: {distance:.4f}")

If the edge table has no vector column, a supplied embedding is ignored with a warning and vector_search_edges returns [].

Append-Only Data History ({table_name}_data)

Appends timestamped data records to vertices or edges for full auditability and semantic versioning.

# Append historical data record
record = await client.add_vertex_data(
    table_name="agents",
    realm="proj_alpha",
    vertex_id=101,
    space="production",
    payload={"checkpoint": 18, "status": "active"},
    embedding=[0.02] * 1536
)

# Retrieve history records
history = await client.get_vertex_data("agents", realm="proj_alpha", vertex_id=101, limit=10)

Advanced Graph Traversals & Shortest Path (CTEs)

traverse

Executes recursive CTE traversals across edge tables.

paths = await client.traverse(
    realm="proj_alpha",
    start_table="agents",
    start_id=101,
    edge_tables=["spawns", "collaborates"],
    max_depth=4
)
for p in paths:
    print(f"Depth: {p['depth']} | Path: {' -> '.join(p['path'])}")

shortest_path

Finds the shortest cycle-free path between two vertices.

sp = await client.shortest_path(
    realm="proj_alpha",
    start_table="agents",
    start_id=101,
    target_table="agents",
    target_id=105,
    edge_tables=["spawns", "collaborates"],
    max_depth=5
)
if sp:
    print(f"Shortest path found at depth {sp['depth']}: {sp['path']}")

Multi-Tenant Realm & Space Deletion

# Delete an entire realm across all tables
deleted_count = await client.delete_realm(realm="tenant_to_remove")

📄 License

This project is licensed under the MIT License.

Developed by Chandan Rajah (chandan.rajah@gmail.com).