# Show HN: Instant GraphRAG over any Postgres database

> Source: <https://polygres.com>
> Published: 2026-07-07 21:28:59+00:00

[Evokoa](https://evokoa.com)

# Postgres for the Agent Era

Introducing Polygres

Structured records. Connected relationships. Semantic meaning. Polygres combines PostgreSQL hosting with native graph traversal, HNSW vector search, and hybrid retrieval APIs. Integrate using [Evokoa/polygres-sdk](https://github.com/Evokoa/polygres-sdk).

## Query everything from one place.

Combine SQL, graph relationships, and vector similarity search inside a single query.

``` python
from polygres import Polygres

# Connect to the project Runtime API
client = Polygres(
    api_key="<POLYGRES_API_KEY>",
    runtime_url="<POLYGRES_RUNTIME_URL>",
)
project = client.project()

# Hybrid Search: Combine graph relationships and vector embeddings
page = project.hybrid.joint(
    [0.12, 0.34, 0.56],
    {"schema": "public", "table": "users", "id": "<USER_ID>"},
    graph_weight=0.40,
    vector_weight=0.60,
    limit=2
)

for doc in page.results:
    print(f"[{doc.score:.4f}] {doc.properties.get('title')}")
{
  "results": [
    {
      "schema": "public",
      "table": "documents",
      "id": "doc_architecture",
      "score": 0.9634,
      "graph_score": 0.92,
      "vector_score": 0.99,
      "properties": {
        "title": "Polygres Distributed Spec"
      }
    },
    {
      "schema": "public",
      "table": "documents",
      "id": "doc_pitch_deck",
      "score": 0.8415,
      "graph_score": 0.69,
      "vector_score": 0.94,
      "properties": {
        "title": "Evokoa V2 Pitch Deck"
      }
    }
  ]
}
```

## Everything your agent needs. Wired together.

All core components are open source. Self-host with pgGraph and pgVector, or use our managed cloud to skip the setup.

### Postgres

A full PostgreSQL 17 instance. Use the ORMs, clients, and tools you already know. ACID-compliant, battle-tested, and exactly what you'd expect.

[View on GitHub](https://github.com/postgres/postgres)

### pgGraph

Model relationships between your data natively. Traverse connections, discover neighbors, and map entity graphs without wrestling with recursive SQL joins.

[View on GitHub](https://www.github.com/Evokoa/pgGraph)

### pgVector

Store and search embeddings directly in your database. HNSW indexing for fast similarity lookups at scale, with scalar filtering to narrow results precisely.

[View on GitHub](https://github.com/pgvector/pgvector)

### Hybrid Search & Cloud

Hybrid search and Polygres scalable cloud combine Postgres, pgVector, and pgGraph together. Built with native Kubernetes and Docker scaling support, plus personalized support for pgGraph.

[Create Account](https://app.polygres.com)

## Your stack. Your choice.

Connect with any Postgres-compatible client, use our Python SDK [Evokoa/polygres-sdk](https://github.com/Evokoa/polygres-sdk) for agent workflows, or talk directly to the REST API. Get running in minutes, not hours.

``` python
from polygres import Polygres

# Initialize client using live retrieval key
client = Polygres(
    api_key="<POLYGRES_API_KEY>",
    runtime_url="<POLYGRES_RUNTIME_URL>",
)
project = client.project()

# Run hybrid joint retrieval matching graph proximity & vector embeddings
page = project.hybrid.joint(
    [0.12, 0.34, 0.56],  # Replace with your embedding vector
    {"schema": "public", "table": "users", "id": "<USER_ID>"},
    graph_weight=0.40,
    vector_weight=0.60,
    limit=5
)

for item in page.results:
    print(f"[{item.score:.4f}] {item.properties.get('title')}")
```

## Postgres for the Agent era.

Deploy your managed database in seconds, or integrate with your existing schema using the open-source SDK.

### Start Building with Polygres

Get full access to relational table definitions, pgGraph traversals, and pgVector similarity search instantly.