For developers and teams who want to explore high-performance graph analytics with full transparency and zero cost.
Experiment with real-time traversal performance, versioning semantics, and memory-efficient graph storage in an open-source, self-managed environment. Install locally, benchmark on your own hardware, and validate graph-driven workloads before moving to production.
Install locally or in your own sandbox environment using pip and Docker
Evaluate sub-millisecond to millisecond traversal speeds against your own datasets
Test column-oriented graph storage vs traditional adjacency-list engines
Explore Git-like commits and auditability for time-travel analytics
Direct access to source code for debugging, extension, and experimentation
Provide feedback and contribute ideas to influence feature direction
Install locally or in your own sandbox environment using pip and Docker
Evaluate sub-millisecond to millisecond traversal speeds against your own datasets
Test how column-oriented graph storage reduces memory footprint versus traditional engines
Explore Git-like commits and auditability for time-travel analytics and reproducibility
Assess historical states, data lineage, and change-tracking to reduce operational risk
Direct access to source code for debugging, extension, and experimentation
Provide feedback and contribute ideas to influence feature direction
Use for prototypes, benchmarking, R&D, academic research, and early-stage experimentation
Source code, documentation, benchmarks, and support through community channels
Teams validating whether high-performance graph analytics fits their workloads, researchers testing versioned graph modeling, and engineering groups building proofs-of-concept prior to enterprise deployment.