Apache AGE is a PostgreSQL extension that adds graph database capabilities, implementing the openCypher query language. It allows users to create, query, and manipulate property graphs (vertices and edges) directly within PostgreSQL, leveraging the database's transactional guarantees, security, and indexing. The extension provides a custom data type (agtype) for graph entities, a Cypher-to-SQL parser, and custom scan executors for efficient graph operations. It includes a comprehensive test suite, multi-language client drivers (Go, Java, Node.js, Python), and integration with NetworkX for graph analysis. The architecture is tightly integrated with PostgreSQL internals through hooks, custom nodes, and memory management, making it a production-grade graph database solution for PostgreSQL users.
Authorizer is a self-hosted, production-grade authentication and authorization server written in Go. It implements OAuth 2.0 / OIDC, SAML IdP/SP, SCIM 2.0, WebAuthn/passkeys, TOTP, email/SMS OTP, and fine-grained authorization via an embedded OpenFGA engine. The architecture is transport-agnostic: a shared service layer (internal/service) is called by GraphQL resolvers, gRPC handlers, and HTTP handlers (Gin). It supports multiple storage backends (SQL, MongoDB, Cassandra, DynamoDB, Couchbase, ArangoDB) via a unified storage.Provider interface, plus Redis and in-memory stores for sessions and caching. The system includes a React admin dashboard, an end-user login SPA, and an MCP (Model Context Protocol) server for AI tool exposure. Security is a primary focus, with extensive regression tests for CVEs, SSRF protection, constant-time comparisons, and audit logging.
This is a full-stack application (FastAPI backend, React frontend) that builds knowledge graphs from unstructured data using LLMs. It ingests documents from local files, S3, GCS, web pages, YouTube, and Wikipedia, chunks them, extracts entities and relationships via LangChain's LLMGraphTransformer, and stores the graph in Neo4j. The system provides a chatbot interface with multiple retrieval modes (vector, graph, hybrid, fulltext, community search), post-processing capabilities (community detection, duplicate merging, vector indexes), and RAGAS-based evaluation metrics. The architecture is a monolithic backend with a single FastAPI app and a React SPA frontend, orchestrated via Docker Compose. External dependencies include Neo4j, multiple LLM providers (OpenAI, Gemini, Anthropic, etc.), and optional cloud storage integrations. The codebase is actively maintained with recent commits and issue triage, and shows low technical drift despite some aspirational model names and minor duplication.
This is the TigerGraph ecosystem repository, a large collection of tools, connectors, demos, benchmarks, and tutorials centered around the TigerGraph graph database platform. It is not a single application but an aggregation of community and in-house developed components. The dominant character is application: it includes production-grade ETL connectors (Spark DataSource V2, JDBC driver, Kafka Connect, Hadoop MapReduce), a multi-version GSQL CLI client, a Go-based REST API for remote GSQL management, a Kubernetes operator, and numerous demos showcasing graph analytics (PageRank, community detection, geospatial search, fraud detection, GraphRAG). There are also benchmarking suites (LDBC SNB) and sample data. The codebase exhibits high craftsmanship in the connector and client libraries, with robust retry logic, token management, streaming JSON parsing, and schema inference. However, it suffers from massive code duplication across GSQL client versions, incomplete JDBC driver implementations, hardcoded paths and credentials in demos, and outdated vendored libraries (Leaflet 0.7.7, moment-timezone 2016). Overall, it is a functional but uneven collection; the core connectors are reusable and well-engineered, while many demo scripts are fragile and require manual configuration.