drawio-skill is a comprehensive AI agent skill for generating, editing, synchronizing, inspecting, testing, and publishing editable draw.io diagrams from natural language, codebases, infrastructure-as-code, and data schemas. It provides a unified CLI (`diagramctl.py`) orchestrating a pipeline of 39+ specialized scripts covering importers (Python, JS/TS, Go, Rust, Terraform, K8s, Docker, SQL DDL, OpenAPI, CI pipelines), auto-layout (Graphviz, ELK, deterministic sequence/C4/tube-map), shape search (10k+ official icons), AI/LLM brand logos (321 brands), style presets, diagram diff, time-lapse, heat maps, and export (PNG, SVG, PPTX, Mermaid, HTML viewer). The architecture is centered on a versioned Diagram IR (JSON Schema) that separates meaning, provenance, and geometry, enabling incremental sync preserving manual layout, multi-view projection, architecture contract testing (Diagram-as-Test), what-if failure simulation, and accessible Story Mode. It integrates with agent platforms via a SKILL.md and an optional MCP server (`diagramctl_mcp.py`) exposing nine tools over stdio, and supports CI integration through GitHub Actions for PR diff and architecture test gates. The project is actively maintained with semantic versioning, 174 unit tests, and a changelog tracking releases through v3.2.0.
drawDB is a free, open-source, client-side web application for designing relational database schemas and generating SQL. It provides an interactive ER diagram editor with support for multiple database dialects (MySQL, PostgreSQL, SQLite, MariaDB, MSSQL, Oracle), import/export in SQL, DBML, JSON, Mermaid, and Markdown, and features like auto-arrange, auto-connect foreign keys, schema validation, migration generation, and versioning via GitHub Gists. The architecture is a React single-page application (Vite + Tailwind CSS + Semi UI) with state managed through custom React Contexts and hooks, persistence via IndexedDB (Dexie), and an optional cloud backend through an extension system. It is primarily an application, with no data research or ML components.
DBeaver is a universal database management tool and SQL client, built as a desktop application on the Eclipse RCP (OSGi plugin) framework. It provides a rich graphical interface for connecting to over 100 database systems via JDBC/ODBC drivers, including relational databases (MySQL, PostgreSQL, Oracle, SQL Server, SQLite, etc.), cloud databases (BigQuery, Athena, Snowflake), and NoSQL systems (MongoDB, Cassandra) in commercial editions. The application offers a full suite of database development features: a multi-tab SQL editor with syntax highlighting, code completion, and execution plan visualization; a data editor with grid, form, and GIS geometry views; ER diagram designer; data export/import wizards supporting CSV, JSON, XML, SQL, Markdown, and Excel; dashboard widgets for query-based monitoring; and an integrated AI assistant (OpenAI, Copilot) for natural language query generation and schema analysis. The architecture follows a strict model/UI separation enforced by OSGi plugin boundaries. The core model plugin (`org.jkiss.dbeaver.model`) defines interfaces for database objects, data values, execution contexts, and security, which are implemented by database-specific extension plugins (`org.jkiss.dbeaver.ext.*`). UI plugins (`org.jkiss.dbeaver.ui.*`) provide editors, navigators, and dialogs, while data transfer, debugging, and AI features are isolated in their own plugins. This design allows the model layer to be reused in the web-based CloudBeaver product. Key entry points include the `DBeaverApplication` class for startup, the `SQLEditor` for query editing, and the `DataTransferWizard` for import/export. External dependencies include the Eclipse RCP framework, JDBC drivers, ANTLR4 for SQL grammar parsing, JSQLParser for SQL analysis, and various third-party libraries for SSH tunneling, AWS SDK, and AI API communication. Data flows center around database connections: users configure data sources via connection wizards, execute SQL queries through the editor, and view results in data grids or export pipelines. The data transfer subsystem implements a producer/consumer pattern with pluggable transformers and serializers, supporting streaming for large datasets. The AI integration uses a provider abstraction to interface with different LLM APIs, generating SQL from natural language and providing schema-aware context. The repository also includes a CLI tool (`dbvr`) for CI/CD automation and a headless test platform. While the community edition is fully open-source, commercial versions add NoSQL support, additional AI providers, and premium features, with some code paths guarded by feature flags.
Liam is a full-stack web application and CLI toolchain for database schema design and visualization. It parses schemas from multiple formats (PostgreSQL, Prisma, Drizzle, tbls, schema.rb), renders interactive Entity-Relationship Diagrams (ERDs) using React Flow, and provides an AI-assisted workflow powered by LangGraph agents. Users can create design sessions, chat with a team of AI agents (PM, DB, QA) that analyze requirements, generate schema changes, create migration SQL, and validate via pgTAP tests. The frontend is built with Next.js and Supabase for authentication, real-time updates, and data persistence. A CLI tool allows local ERD generation from schema files. The monorepo includes numerous internal packages for schema parsing, UI components, error handling (neverthrow), and in-browser PostgreSQL execution (PGlite).