Havuz

A light TablePlus alternative for PostgreSQL that your AI agents can use too, over MCP. macOS and Windows.

Open-source text-to-SQL engine you tune and measure on your own database

Embedded retrieval library built on Parquet. Fast, efficient, and scalable.

Declarative YAML around SQL for dbt-native dashboards: easy for agents to write, easy for humans to audit.

The memory & storage database engine for AI agents. Multi-model, edge-to-cloud, PostgreSQL-compatible. Supports graph, vector, array, timese

Local-first observability your agent reads back. TMA1 records every LLM call, then routes what it sees into the agent's next turn via hooks

Skardi is a self-improving context framework for ai agents

Agentic text-to-SQL SDK: hand the LLM one execute_sql tool and let it explore the schema, test queries, and self-correct — no RAG, no semant

Practical, tested recipes for TypeSafe's Jev decision model on OpenRouter: support triage, database indexing, file organizing, tagging, taxo

An agent-native workspace for quantitative research: an in-terminal notebook, a high-performance time-series database, and a high-throughput

A Model Context Protocol (MCP) server for CUBRID database — enabling LLMs to inspect schemas and execute read-only queries via pycubrid.

The multi-paradigm database expert for Claude Code — design a schema, audit it on two never-blended scores (Design & Integrity / Performance

Let your users ask your database anything, in their own words — safely. The model fills in a validated Query AST, never a query string, and

Your data, explored locally — and your AI agents kept on a leash. A federated data explorer with governed agentic access.

An AI-native multi-model database unifying SQL, vector, full-text, graph, and sandboxed Python — for transactional, analytical, and agent wo

Token-efficient MCP server for tabular data retrieval. Index CSV/Excel files, query rows, aggregate — 99%+ token savings vs raw file reads.

PostgreSQL-compatible SQL, graph, and vector database built from scratch in Rust.

Methodology + templates for a trustworthy, traceable, self-growing AI data-analyst agent: metrics-first, independent read-only validation, a

Multi-driver TUI database client with a built-in MCP server. Six databases (postgres, mysql, sqlite, duckdb, clickhouse, mssql), vim editing

The database agent for Postgres and MySQL - Point it at PostgreSQL or MySQL and ask questions in plain English — schema exploration, query g

RoR engine that enables natural language queries over your database using RAG and LLMs.

Database MCP server for MySQL, MariaDB, PostgreSQL, and SQLite - with builtin PII redaction and write-prevention

Local-first dashboards your AI agent builds and keeps maintaining, over data that never leaves your machine.

Dashboards from plain DuckDB SQL: tag result columns with ::ROLE casts and each SELECT becomes a chart, KPI, table, map or input, drawn as S

Postgres MCP Server

🐀 Terminal-native AI data agent — ask your warehouse a question in plain English, watch dialect-correct SQL stream into your terminal, get g

Towards AI-Generated and Formally Verified Database Systems

Governed, provenance-sealed creative rendering for Databricks — a Streamlit App + Unity Catalog ai_render() function that turns any governed