Awesome GitHub RepositoriesGraph Querying

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer

Graphiti is a backend framework and memory server designed to provide artificial intelligence agents with persistent, time-aware knowledge graph storage. It functions as a memory layer that enables agents to maintain context across long-term interactions by recording and evolving structured data over time. The system distinguishes itself through a specialized temporal graph database that tracks how entities and relationships change using validity windows. By combining semantic vector similarity, keyword matching, and graph topology traversal, the engine performs hybrid retrieval to locate rel

Dgraph is a distributed graph database designed to store and query highly connected data. It organizes information as nodes and edges to represent complex relationships between entities, providing a platform for managing and analyzing deeply linked datasets. The system functions as a horizontally scalable cluster that partitions data across multiple nodes to maintain performance and availability as information volume increases. It utilizes a specialized query language built for low-latency navigation of interconnected data points, allowing for the execution of complex queries across large-sca

Subql is a blockchain data indexing framework and TypeScript-based indexer used to extract raw blockchain events and transactions and transform them into structured, queryable data entities. It functions as a data API and a tool for building decentralized application backends, providing a query interface for type-safe access to indexed blockchain data. The project includes an AI-powered query engine that utilizes large language models to translate natural language questions into structured GraphQL queries. This system can orchestrate multi-step queries by breaking down complex requests into s

Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions. The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orche

Ent is a statically typed entity framework for Go that models database structures as a graph of nodes and edges. It functions as a code generation engine that transforms schema definitions into type-safe database clients, query builders, and migration scripts. By representing data as interconnected entities, the framework enables intuitive traversal of complex relationships and ensures that database interactions remain consistent with the application model at compile time. The framework distinguishes itself through its graph-based approach to data modeling and its reliance on compile-time cod

Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries

Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution usi

Cayley is a graph database and query engine designed to store and retrieve interconnected data. It functions as a quad store, persisting information as four-element tuples to maintain complex relationships and semantic linked data. The system features a backend-agnostic storage layer that decouples the graph API from the underlying data store. This allows for the integration of external backends through a modular adapter system, enabling the synchronization of data across different storage engines. The project provides a pattern-matching query engine for extracting specific nodes and relatio

EdgeDB is a graph-relational database that combines a PostgreSQL backend with a graph-based schema and query language. It functions as an object-relational mapper and graph query engine, allowing data to be modeled as objects and links to align storage with modern programming language structures. The system features a composable query language designed to retrieve deeply nested or interconnected data without the use of manual SQL joins. It includes an integrated AI-driven data retrieval solution with built-in support for vector embeddings. The platform provides a schema migration tool for tr

This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals

dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control. It functions as a data transformation engine that enables users to define data structures and business logic through declarative configuration files, which the system then compiles into executable code. By managing complex data dependencies through a directed acyclic graph, it ensures that transformation tasks execute in the correct order while maintaining a manifest-driven state to track lineage and execution history. The project distinguishes itself through an adapter-based d

Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

This project is a collection of programming language references and syntax cheat sheets designed for rapid developer onboarding. It serves as a library of code-based documentation that uses valid source code files to provide whirlwind tours of various language specifications. The project focuses on programming language learning by providing concise, commented code examples that explain core features and syntax in place. This approach enables developers to quickly grasp language-specific patterns, data types, and execution flow through a consistent reference format. The content covers a broad

This project is a comprehensive learning resource and reference guide for software architecture and distributed systems design. It serves as a structured curriculum for engineers to study fundamental architectural patterns, scalability strategies, and distributed computing theory, specifically tailored to prepare for technical interviews and professional engineering roles. The repository distinguishes itself by providing a curated collection of industry-standard infrastructure tools and methodologies. It covers the selection and implementation of technologies for data storage, message brokeri

Falcor is a JavaScript library that models remote data as a single virtual JSON graph, providing a path-based query engine for efficient client-side data retrieval and updates. It represents multiple remote data sources as a unified document where entities are accessed via globally unique identity paths. The system distinguishes itself by treating the remote data model as a virtual JSON resource, allowing the client to query specific paths without managing individual endpoints. It uses a reference-aware graph model to handle many-to-many relationships and prevents data duplication. Network ef

Bloodhound is an Active Directory attack path mapper and security auditor designed to visualize trust relationships and permission chains. It serves as an attack surface management tool that identifies paths to domain administrator and other high-privileged accounts. The project uses a graph database analyzer to map complex identity and access relationships. It quantifies the risk of privilege escalation by identifying misconfigured permissions and trust links within Windows domains. The system provides capabilities for Active Directory security analysis, identity and access auditing, and ne

This is a JavaScript library for parsing and serializing JSON data, with a particular focus on handling objects that contain circular references. It provides a standard JSON parser that reads text and reconstructs JavaScript values without using the eval function, guarding against code injection, alongside a standard serializer that converts objects into JSON strings for data interchange. The library distinguishes itself by offering specialized encoding and decoding for cyclical object graphs. It can serialize objects with circular references by replacing repeated object paths with JSONPath s

This project is a comprehensive educational resource and fullstack tutorial for GraphQL development. It provides instructional content and guides focused on designing schemas, implementing servers, and managing the end-to-end workflow of building production-ready applications. The material covers the conceptual differences between graph-based data structures and traditional API architectures. It includes a dedicated security course and guides for client integration, teaching users how to fetch data, manage application state, and apply protection measures to secure API endpoints. The scope of