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Polygres

Freemium

Polygres is an AI-powered memory platform for PostgreSQL, built for developers.

89 visitors 2 days ago

Struggling to give AI agents persistent memory across scattered data?

Stop Wasting Time on Data Silos

With Polygres, you unify relational data, graph traversal, vector search, and full-text in PostgreSQL to deliver token-ready context for AI agents.

The Aha Moment: Grounded Context

Polygres fuses results from tables, relationships, and embeddings into a single context block, accelerating grounding and accuracy.

Deploy as managed cloud or self-hosted, and retrieve customer records, orders, and interactions without a separate vector or graph database.

Verification Options:

1.

Email Verification: Verify ownership through your domain email.

2.

File Verification: Place our file in your server.

After verification, you'll have access to manage your AI tool's information (pending approval).

Freemium with free self-hosted option; no time-limited trial; no money-back guarantee

Quick verdict

Based on 2 reviews

Read all reviews

Pros

  • Graph traversal: fast, accurate multi-hop queries over PostgreSQL keys.
  • Unified retrieval: one query returns relational, graph, vector, and full-text results.
  • Token-ready context: blocks designed to pass clean subsets to AI agents.

Cons

  • Self-hosted onboarding could be clearer; I had to stitch steps from multiple docs.
  • Index tuning for large datasets takes time; defaults arenโ€™t optimal.
  • There isnโ€™t a per-project pricing option for short-term experiments.

Customer Reviews for Polygres

Overall Analytics

Comprehensive review insights and historical performance

Very Positive (2) 4.5/5 2 reviews 100% recommend โ€” Monthly growth

6-month timeline

Most helpful

Mia Martinez
Mia Martinez 0

Iโ€™ve been building a memory layer for a customer-support AI, and Polygresโ€™ unified retrieval across relational, graph, vector, and full-text results is a game changer. The graph traversal over PostgreSQL keys makes multi-hop investigations fast, letting me trace a customerโ€™s orders and disputes in a single query. The token-ready context blocks save me from slicing data manually. I chose self-hosted for control, and while the setup is doable, the onboarding docs could be clearer for first-timers.

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Recent Review Statistics

Sentiment analysis and trends from the last Last 30 days

4.5/5
2 reviews
Very Positive (2) New reviews
Trend: Steady Velocity: 0.1/day Engagement: 0%
Velocity utilization 14%
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Showing 1 - 2 of 2 reviews .

User avatar for Yuki Tanaka

Yuki Tanaka

Trusted Reviewer
4.0
Recommends

Graph-driven investigations finally click, but performance needs attention

Used for 3-6 months

What I liked

  • Graph traversal: fast, accurate multi-hop queries over PostgreSQL keys.
  • Hybrid search: dense, sparse, and semantic indexing surfaces connections quickly.
  • Token-ready context: bounded blocks ready to pass to AI agents.
  • Deployment flexibility: managed cloud or self-hosted with open-source components.

What could be better

  • Performance can dip on very large graphs; caching / indexing needs better guidance.
  • Self-hosted setup requires more networking and security steps than I expected.
  • Traversal results can be verbose; built-in filters would help prune noise.

As a security-ops engineer investigating failed payments, Polygresโ€™ graph traversal unlocks fast, multi-hop queries over my PostgreSQL data. I can chain relationships from a transaction to a dispute and related notes without exporting records. The hybrid search helps me surface relevant context with semantic closeness, and the token-ready blocks let me feed AI assistants the exact fields they need. The option to self-host is a plus, but when datasets go large, performance can dip until I optimize indices and caching.

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User avatar for Mia Martinez

Mia Martinez

Trusted Reviewer Verified purchase
5.0
Recommends

Unified retrieval finally clicks for my AI memory layer

Used for week to month

What I liked

  • Unified retrieval: one query returns relational, graph, vector, and full-text results.
  • Graph traversal: fast, accurate multi-hop queries over PostgreSQL keys.
  • Token-ready context: blocks designed to pass clean subsets to AI agents.
  • Hybrid search: dense, sparse, and semantic indexes surface relevant data quickly.

What could be better

  • Self-hosted onboarding could be clearer; I had to stitch steps from multiple docs.
  • Index tuning for large datasets takes time; defaults arenโ€™t optimal.
  • There isnโ€™t a per-project pricing option for short-term experiments.

Iโ€™ve been building a memory layer for a customer-support AI, and Polygresโ€™ unified retrieval across relational, graph, vector, and full-text results is a game changer. The graph traversal over PostgreSQL keys makes multi-hop investigations fast, letting me trace a customerโ€™s orders and disputes in a single query. The token-ready context blocks save me from slicing data manually. I chose self-hosted for control, and while the setup is doable, the onboarding docs could be clearer for first-timers.

Was this helpful?
Link copied! ๐ŸŽ‰

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How it works

How Polygres Works In 3 Steps?

  1. Step 1

    1. Install Polygres

    Install Polygres on PostgreSQL to enable AI agent memory retrieval.

  2. Step 2

    2. Connect your schema

    Attach tables and relations to enable graph and relational queries.

  3. Step 3

    3. Run a NL query

    Submit natural-language queries to fetch token-ready context for agents.

Direct Comparison

See how Polygres compares to its alternative:

Polygres VS PostgresML

Polygres: Features, Advantages & FAQs

Explore everything you need to know about Polygres

Core Features
  • Unified retrieval: One query returns relational, graph, vector, and full-text results
  • Graph traversal: Fast, accurate multi-hop queries over PostgreSQL keys
  • Token-ready context: Ready-to-pass blocks for AI agents
  • Hybrid search: Dense, sparse, and semantic retrieval
  • Deployment choice: Managed cloud or self-hosted
  • Open-source components: Self-host and customize
Advantages
  • Unified data retrieval: combines relational, graph, vector, and text results in one query
  • Grounded AI context: token-ready blocks for reliable agent reasoning
  • No separate vector or graph database needed: simplifies architecture
  • Flexible deployment: managed cloud or self-hosted
  • PostgreSQL-native: leverage existing schemas
  • Open-source components: customizable and transparent
Use Cases
  • Build grounded AI agents that access customer records and orders
  • Investigate failed transactions by linking payments and disputes
  • Create memory systems that recall past interactions
  • Query Wikipedia-scale datasets with hybrid indexes
  • Provide support agents with ranked, database-backed context
  • Develop recommendation systems using PostgreSQL data
Best For
  • AI engineers, developers, data engineers, backend developers, database administrators

Integrations

Works with the tools you already use

No direct integrations available
Best For

AI engineers, developers, data engineers, backend developers, database administrators

Skill Level
Intermediate

Frequently Asked Questions

Developed by: Polygres Team

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