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PuppyGraph

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LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph puppygraph.com PuppyGraph Feb 19, 2026 17 facts

claimPuppyGraph supports modeling organizational networks, social introductions, fraud and cybersecurity graphs, and GraphRAG pipelines that trace knowledge provenance.

claimPuppyGraph is used by half of the top 20 cybersecurity companies, as well as enterprises including AMD and Coinbase, for use cases such as multi-hop security reasoning, asset intelligence, and deep relationship queries.

claimPuppyGraph supports standard graph query languages including openCypher and Gremlin, and integrates with visualization tools for relationship exploration.

claimPuppyGraph supports flexible and iterative modeling through metadata-driven schemas, allowing the creation of multiple graph views from the same underlying data without rebuilding pipelines.

claimPuppyGraph can be deployed via Docker, AWS AMI, GCP Marketplace, or within a VPC or data center to maintain full data control.

claimPuppyGraph eliminates data duplication by querying data in place, which maintains data consistency and leverages existing data access controls.

claimPuppyGraph utilizes a distributed compute engine with parallel processing and vectorized evaluation technology to scale with cluster size, enabling petabyte-scale workloads and 10-hop neighbor traversals in seconds.

measurementPuppyGraph can be deployed in under 10 minutes, which allows users to bypass the cost, latency, and maintenance hurdles associated with traditional graph databases.

claimPuppyGraph integrates with data lakes including Apache Iceberg, Apache Hudi, and Delta Lake, and databases including MySQL, PostgreSQL, and DuckDB, allowing users to query across multiple sources simultaneously.

claimPuppyGraph is a graph query engine that supports various databases with zero-ETL and can be integrated with LLMs to build LLM knowledge graphs.

procedureThe procedure to launch a PuppyGraph container in Docker is: 1) Ensure Docker is installed. 2) Verify installation with 'docker version'. 3) Run the command: 'docker run -p 8081:8081 -p 8182:8182 -p 7687:7687 -e PUPPYGRAPH_PASSWORD=puppygraph123 -e QUERY_TIMEOUT=5m -d --name puppy --rm --pull=always puppygraph/puppygraph:stable'. 4) Access the Web UI at http://localhost:8081 using the username 'puppygraph' and password 'puppygraph123'.

claimPuppyGraph enables real-time analysis by querying live source data, which avoids reliance on static or outdated graph snapshots.

claimPuppyGraph allows teams to use existing SQL engines for tabular workloads and PuppyGraph for relationship-heavy analysis on the same source tables, avoiding the need to force all use cases through a graph database.

claimPuppyGraph reduces total cost of ownership by eliminating the need for data pipelines, duplicated storage, parallel governance, and high-memory hardware required by traditional graph databases.

measurementPuppyGraph users report the ability to execute 6-hop queries across billions of edges in less than 3 seconds.

claimPuppyGraph is a real-time, zero-ETL graph query engine that allows data teams to query existing relational data stores as a unified graph model.

claimPuppyGraph operates as a zero-ETL query engine that runs on existing relational databases and data lakes, enabling users to query data as a graph without building pipelines.