PuppyGraph on Tuesday said it was adding an AI-assisted graph schema builder and MCP integrations to PuppyGraph 1.0, the new version of its distributed graph query engine for…
PuppyGraph on Tuesday said it was adding an AI-assisted graph schema builder and MCP integrations to PuppyGraph 1.0, the new version of its distributed graph query engine for enterprise data. The update is designed to help developers reduce the complexity, latency, and costs associated with letting AI agents query interconnected data across thousands of database tables and multiple platforms.
The AI-assisted schema builder automatically maps existing tables into graph entities, relationships, and properties, allowing developers to review and approve the resulting schema rather than manually defining how disparate datasets connect, the Santa Clara-headquartered startup said in a statement.
These schemas can be used by agents deployed to query enterprise data, giving them a predefined map of how information across different tables and systems is connected, rather than requiring them to identify relevant tables and construct complex SQL joins dynamically, in turn simplifying query generation and reducing errors and retries, it added. To improve query accuracy, PuppyGraph’s query engine also validates agent-generated queries against the graph schema, rejecting undefined relationships and returning feedback that agents can use to correct errors.
Further, the execution engine splits complex graph queries into smaller operations that run simultaneously across multiple compute nodes, rather than sequentially on a single machine, helping accelerate queries that traverse multiple relationships across large datasets, the company said. These additions build on PuppyGraph’s existing graph query engine, released in 2024, that lets enterprises query relational databases, data warehouses and lakehouses as a unified graph without moving or duplicating data into a separate graph database.
The engine maps existing tables into a native Labeled Property Graph (LPG) model, representing data entities as nodes, their relationships as edges, and their attributes as properties, before executing graph queries across underlying data sources through its own distributed compute cluster. The latest version of the engine, which also supports conventional SQL queries, adds MCP integrations that allow enterprise teams to connect PuppyGraph to AI agents and tools that support the protocol, the company said.
This is a source-linked briefing. Read the original report from Info World .