The Best VeritasGraph Alternatives in 2026

Omnigraph provides a graph engine for lakehouse data with Git-style workflows.

Spatial AI and procedural knowledge research: a GPU-native reference implementation for concepts proposed to PM-KR, exploring shared knowledge for human and AI clients.

agent-learning-kit provides an evaluation and simulation environment for AI-related workflows.

Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.

PyTorch Geometric provides graph neural network tools and libraries for PyTorch projects.

Spektral provides a Python framework for creating graph neural networks with Keras and TensorFlow.

Little Ball of Fur provides graph sampling extensions for NetworKit and NetworkX.

Hex combines data notebooks, applications, and AI tools so teams can ask questions and build with organizational data.

Sourcetable combines spreadsheets and data analysis tools for creating financial models and working with datasets.

This repository catalogs AI models across providers with pricing, context, modality, and capability data.

BambooAI analyzes data with a language model in a persistent Python kernel and supports web and Docker workflows.

Kadoa builds and maintains web data pipelines that produce verified datasets for finance.

Zerve supports research and analytics workflows, with AI agents for data discovery, analysis, reports and production deployment.

Flexor prepares and retrieves unstructured data as context for AI agents, workflows, and enterprise decisions.

Open-source DMS (data management system) for powering data hubs and data portals.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media.

pytorch tabular provides a unified framework for deep learning models on tabular data.

PyTorch Frame provides a library for applying deep learning models to tabular data.

tsfresh automatically extracts relevant features from time series data.

Snorkel helps generate training data programmatically through weak supervision.

Apache Spark is a unified engine for large-scale data processing and analytics.

Disco’s discovery engine finds statistically validated patterns in tabular data, including feature interactions and subgroup effects.

ADAM analyzes genomics data with specialized file formats built on Apache Spark, Avro, and Parquet.