PuppyGraph Adds AI Schema Builder to 1.0

The update adds an AI-assisted graph schema builder and MCP integrations to help developers query interconnected data with AI agents.

Updated on Nov. 10, 2026 in Artificial Intelligence

Wide view of a low-rise modern tech campus building with concrete and glass facade under clear daylight, with a rooftop column of exhaust vents and green landscaping. — PuppyGraph, a Santa Clara software company, released version 1.0 with an AI-assisted graph schema builder that automatically maps existing database tables into graph entities and relationships. AI Illustration. Upload story photo >

PuppyGraph, headquartered in Santa Clara, added an AI-assisted graph schema builder and MCP integrations to PuppyGraph 1.0. The schema builder automatically maps existing tables into graph entities, relationships, and properties, which developers can review and approve.

Why it matters

The update is designed to help developers reduce the complexity, latency, and costs associated with letting AI agents query interconnected data. Its query engine validates agent-generated queries against the graph schema, a step intended to make agent queries more reliable.

The query engine rejects undefined relationships and returns feedback agents can use to correct errors, while the execution engine splits complex graph queries into smaller operations that run simultaneously across multiple compute nodes. Performance measurements for these operations were not included in the announcement.

The players

PuppyGraph

PuppyGraph is a graph query engine company headquartered in Santa Clara that maps existing tables into a graph data model for querying.

IDC

IDC is a market research firm that tracks enterprise technology and data-management trends.

The details

The engine maps existing tables into a native Labeled Property Graph model, and the latest version also supports conventional SQL queries. PuppyGraph's technology is used by enterprises including AMD, Palo Alto Networks, Datadog, Netskope, Coinbase, Trend Micro, Baker Hughes, Sola Security, HighStreet Insurance Partners, and Prevalent AI.

2024: PuppyGraph's graph query engine was released.
November 10, 2026: PuppyGraph announced the schema builder and MCP integrations for PuppyGraph 1.0.

The Tech Race

The update extends PuppyGraph's graph query engine, first released in 2024, by adding tools aimed at AI agents that query interconnected data. It positions the company within the broader push to consolidate data management across the many database technologies organizations already run.

Developers building AI agents that query interconnected data could see fewer query errors, since invalid relationships are rejected and returned as feedback. The announcement does not state pricing or availability terms for the new features.

The takeaway

Teams evaluating AI agents for complex data should check whether their query layer validates generated queries against a schema before running them. Reviewing a generated schema before approval can help catch mapping errors early.