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Last Updated: 08-21-2026 05:41 PM (UTC)

MindsDB

Backed by 39,500+ GitHub stars and over 339 releases, MindsDB delivers the open-source federated query engine that gives AI agents a single SQL interface to read, join, and aggregate across 200+ live data sources without any ETL pipelines or data movement. The Connect-Unify-Respond architecture wires up Postgres, MySQL, MongoDB, Snowflake, BigQuery, ClickHouse, Redshift, Databricks, Salesforce, Shopify, Slack, S3, GCS, Azure Blob, and dozens more through self-contained Python handler packages merged in the open from the community. Knowledge Bases fuse structured tables with vectorized unstructured data from PDFs, emails, support tickets, and documents using hybrid search combining vector similarity with keyword matching for retrieval-augmented generation. Jobs execute queries on configurable schedules refreshing Knowledge Bases nightly or syncing derived tables hourly, while Triggers fire on data changes to automatically vectorize new rows into the appropriate store. The SQL-compatible query language extends standard SQL with constructs for creating models, defining agents, managing workflows, and searching unstructured data. The built-in web editor at port 47334 provides interactive SQL authoring, while the MySQL-compatible API at port 47335 and PostgreSQL API at port 47336 connect any database client directly. An MCP Server integration exposes MindsDB to AI assistants, and the Python SDK enables programmatic access from application code. Docker deployment runs with a single command exposing all APIs immediately. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

MindsDB
MindsDB
MindsDB
MindsDB
MindsDB

Benefits

  • One SQL Dialect for Everything
  • Query Postgres, MongoDB, Snowflake, Salesforce, S3, and 200+ other sources with standard SQL joins and aggregates — no ETL pipelines, no data movement, no custom connectors to maintain.
  • Knowledge Bases for Unstructured Data
  • Ingest PDFs, emails, Slack messages, and documents into autonomous RAG systems with hybrid search combining vector similarity and keyword matching for accurate AI-powered retrieval.
  • Automated Data Workflows Built-In
  • Jobs run queries on configurable schedules while Triggers fire on data changes, enabling self-maintaining data layers that refresh Knowledge Bases and sync derived tables automatically.
  • Universal Client Compatibility
  • MySQL-compatible API on port 47335, PostgreSQL API on port 47336, HTTP editor on port 47334, MCP Server for AI assistants, and Python SDK provide access from any client or agent.

Features

  • 200+ Data Integrations
  • Self-contained Python handlers connect databases, warehouses, SaaS apps, vector stores, and file systems through a consistent SQL interface with community-contributed connectors.
  • Built-In Web Editor
  • Interactive SQL authoring environment at port 47334 with integration management, model creation, agent configuration, and query execution in a browser-based interface.
  • MCP Server Integration
  • Model Context Protocol server exposes MindsDB data and Knowledge Bases to AI assistants like Claude, ChatGPT, and Cursor for direct agent-to-data queries.
  • Hybrid RAG Search
  • Knowledge Bases combine vector similarity search with keyword matching across ingested unstructured data, providing accurate retrieval for conversational analytics and semantic search agents.
  • Docker Single-Command Deploy
  • One Docker run command starts all APIs with persistent storage, authentication support, and installable integration extras via pip inside the running container.