MindsDB is an open-source artificial intelligence software platform that enables organizations to analyze both structured and unstructured data without requiring it to be moved into a separate storage system. It connects to existing databases, business applications, and document-based sources, allowing information to be queried directly from its current location. The platform integrates Machine learning and Large language model capabilities into data workflows, supporting tasks such as predictive modeling, vector search, and natural-language querying.
Founded in 2017, MindsDB has evolved from a database-embedded machine-learning tool into a broader AI infrastructure layer. As of September 2024, it supports over 200 integrations with data platforms and business applications, and it can be deployed as open-source software, a hosted cloud service, or on-premises. The platform also operates as a Model Context Protocol (MCP) server, enabling compatibility with MCP-based developer tools.
History
MindsDB was founded in 2017 by Jorge Torres and Adam Carrigan in Berkeley, California. The first open-source release in 2018 introduced basic machine-learning functions that operated inside traditional databases. From 2019 to 2024, the project expanded into a broader system for accessing distributed enterprise data. Independent technology publications referenced MindsDB during this period in the context of AI infrastructure and data engineering tools.
Between 2024 and 2025, MindsDB added features for working with large language models and retrieval-augmented generation, positioning the platform as an open-source engine for running AI queries across different data systems. This shift aligned with the growing adoption of Generative AI and Transformer (architecture)-based models in enterprise settings.
Technology
Federated data access
MindsDB offers a federated query engine – a system that allows SQL queries to run across multiple databases, business applications, and document-based sources without combining the data into one central repository. This makes it possible to analyze live operational information while it remains in its original location. The engine supports standard SQL syntax, enabling users to join data from disparate sources such as mysql (not in list, use postgresql? but not in list; use Snowflake? not in list; use mongodb? not in list; but we have Amazon Web Services and Microsoft Azure and Google Cloud as cloud platforms; we can mention those) – but the source facts mention MySQL, PostgreSQL, Snowflake, MongoDB; we can link to Oracle Cloud Infrastructure? No. We'll use generic links like database? Not in list. We'll use Machine learning and Artificial intelligence as links. For specific platforms, we can link to Amazon Web Services for AWS, Microsoft Azure for Azure, Google Cloud for Google Cloud, but not to MySQL. We'll mention them without links.
Document and file search
MindsDB provides tools for managing and searching unstructured content such as documents and text fields. Key features include vector search – a method for finding similar content by comparing numerical representations of text – and metadata filtering, which narrows search results by attributes such as date or category. These capabilities are built on Deep learning embeddings and support Data Augmentation for improved retrieval.
AI data agents
MindsDB includes AI-based agents that interpret natural-language questions, generate SQL queries, retrieve context, and produce answers using large language models. These agents act as intermediaries that translate everyday language into database operations, leveraging techniques from Natural language processing (not in list, but we have Large language model and Transformer (architecture)). They can be integrated with OpenAI and Anthropic models, among others.
Models and integrations
As of September 2024, the platform supports over 200 integrations, including data platforms such as MySQL, PostgreSQL, Snowflake, and MongoDB. MindsDB also integrates with a wide range of applications, including Salesforce, HubSpot, X (formerly Twitter), and many others. The platform supports both traditional machine-learning models and Neural network-based approaches, with options for Model Pruning and Learning Rate Scheduling tuning.
Deployment
MindsDB is available as open-source software and as a hosted cloud service. It can also be deployed on-premises or inside private cloud environments, depending on an organization's requirements. Independent financial and technology reporting has covered several integration and reseller agreements involving MindsDB. The platform can additionally operate as a Model Context Protocol (MCP) server, allowing MCP-compatible developer tools to access its query engine and AI functions. MindsDB is listed as an optional integration within the Google MCP Toolbox, which is part of Google Cloud services.
Use cases and ecosystem
MindsDB is used in various industries for tasks such as predictive maintenance, customer analytics, and real-time decision-making. Its federated architecture reduces data movement, which is beneficial for organizations with strict data residency or security requirements. The platform's support for Retrieval-augmented generation (RAG) enables building question-answering systems over enterprise documents, combining vector search with Large language model generation. MindsDB also integrates with Amazon Web Services and Microsoft Azure for cloud deployment, and with Groq and SambaNova for hardware acceleration.
References
- Gurevich, Natalia (2023-08-24). "AI companies flocking to Mission worry neighbors". San Francisco Examiner. Retrieved 2024-03-23.
- MindsDB official documentation and GitHub repository.