Polygres
PostgreSQL platform for hybrid AI agent memory and retrieval
Score
60
User Rating
3.0
Free/Trial Support
Not Supported
Starting Price
--
Last Updated
Sep 29, 2026
Overview
What is Polygres?
Polygres is an all-in-one PostgreSQL platform for AI agents that combines relational data, graph traversal, vector search, and hybrid retrieval through a unified API. It turns existing PostgreSQL databases into working memory for agents, allowing them to retrieve structured rows, connected relationships, semantic matches, full-text results, and filtered context without using a separate vector database, graph database, or synchronization pipeline. Polygres includes pgGraph for foreign-key-based graph traversal and pgContext for fused retrieval across dense HNSW vectors, sparse search, full-text search, filtering, recommendations, discovery, grouped results, and lookups. Users can deploy it as a managed cloud database or self-host its open-source components.
What is Polygres used for?
Top Features
- Unified relational, graph, vector, and full-text retrieval
- Native graph traversal over existing PostgreSQL foreign keys
- Fused hybrid search with dense HNSW, sparse, filtered, and semantic retrieval
- Token-ready context assembly for grounded AI agents
- Shortest-path, neighborhood, relationship, and multi-hop graph queries
- Managed cloud hosting and open-source self-hosting
- PostgreSQL-native data retrieval without a separate vector store or sync pipeline
How to use Polygres?
Create a Polygres managed instance or install the open-source components on an existing PostgreSQL database. Connect your schema and use the SDK or retrieval API to submit a natural-language query with options such as graph traversal depth, filters, and token limits. Polygres combines relational, graph, vector, and text results into one ranked, token-ready context block that can be passed directly to an AI agent.
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Pros & Cons
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Use Cases
- Build grounded AI agents that retrieve customer records, orders, tickets, and related history
- Investigate failed transactions by combining semantic search with order, payment, and dispute relationships
- Create user-memory systems that retrieve relevant past interactions and connected data
- Query Wikipedia-scale datasets using hybrid indexes and graph relationships
- Provide support agents with ranked, database-backed context
- Develop recommendation and discovery systems using existing PostgreSQL data
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