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mindsdb

starFeaturedMCP Server

AI Data Vault - A query engine for AI Agents to securely query data from any datasource

Copy the install, test the workflow, then decide if it earns a permanent slot.

39,079
Why nowMoving now

Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.

DecisionHigh-conviction move

Copy the install, test the workflow, then decide if it earns a permanent slot.

Trial costMedium lift

Testable in one sitting, but you will likely touch real infra or local setup before you know if it sticks.

Risk21/100

GitHub health 100/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.

What You Are Adopting

AI Agent

Universal

Model

Multiple

Build Time

Days

Test This In Your Stack

One command inClean rollbackLow commitment
settingsRegistryAdds a named entry to Claude config. One command to remove.

Fastest way to find out if mindsdb belongs in your setup.

Copy the install command, run a real test, and back it out cleanly if it slows you down.

Try now
claude mcp add mindsdb -- npx mindsdb

Run this first. You will know quickly if the workflow earns a permanent slot.

Back out
claude mcp remove mindsdb

No messy cleanup loop. If it misses, remove it and keep moving.

Install Location

~/  └─ .claude.json    └─ mcp_servers/      └─ mindsdb ← registers here

About

AI Data Vault - A query engine for AI Agents to securely query data from any datasource. An open-source mcp server for the AI coding ecosystem.

README

Query engine for AI analytics, powering agents to answer questions across all your live data

MindsDB Release Python supported Docker pulls

Website · Docs · Contact us for a demo · Community Slack


MindsDB is a popular open-source query engine for AI analytics, powering AI agents that need to answer questions directly from databases, data warehouses, and applications, with no ETL required.

MindsDB demo - answer questions in plain English from live enterprise data

What you can build with MindsDB Query Engine

CONVERSATIONAL ANALYTICS AGENTS SEMANTIC SEARCH AGENTS
Get precise, data-driven answers using natural language.

Unify and query data across sources (MySQL, Salesforce, Shopify, etc.), without ETL.

Watch video
Ground LLM responses in your most relevant internal knowledge.

Search across unstructured sources like documents, support tickets, Google Drive, and more.

Watch video

How MindsDB works

MindsDB follows a simple workflow: Connect → Unify → Respond. At the center is an SQL-compatible data language with additional constructs for searching unstructured data, managing workflows (jobs/triggers), and building agents.

Connect Universal data access: Give your agents federated access to 200+ live data sources (Postgres, MongoDB, Slack, files, and more).
Unify Dynamic context engine: Fuse structured tables with vectorized data (text, PDFs, HTML) inside a Knowledge Base.
Respond Autonomous reasoning: Deploy agents that blend and retrieve data points across your stack to produce grounded answers.

Setup

Users can install MindsDB via Docker, Docker Extension, or PyPI.

Here is how to pull and run MindsDB via Docker:

docker run --name mindsdb_container \
-e MINDSDB_APIS=http,mysql \
-p 47334:47334 -p 47335:47335 \
mindsdb/mindsdb:latest

Usage

Follow the quickstart guide to get started with MindsDB using our demo data.

Retrieve and analyze data from over 200 data sources in one SQL dialect. For AI agents, this means faster response time, better accuracy, and lower token consumption.

--use SQL to aggregate pipeline data from Salesforce 
SELECT SUM(ExpectedRevenue) AS open_pipeline
FROM salesforce.opportunities
WHERE close_date >= CURDATE()

--use the same dialect to retrieve even from a non-SQL database, like MondoDB
SELECT COUNT(*) AS negative_emails_last_30_days
FROM mongodb.support_tickets
WHERE sentiment = 'negative'
  AND created_at >= CURRENT_DATE - INTERVAL '30 days';

Create views and join data even from different types of data systems.

--join MongoDB and Salesforce data
CREATE VIEW risky_renewals AS (
SELECT *
FROM mongodb.support_tickets AS reviews
JOIN salesforce.opportunities AS deals
  ON reviews.customer_domain = deals.customer_domain
WHERE deals.type = "renewal"
  AND reviews.sentiment = "negative"
);

Join vectorized and structured data inside a knowledge base. Combine semantic search with precise metadata criteria in a single SQL query.

--create a knowledge base for customer issues 
CREATE KNOWLEDGE_BASE customers_issues
USING
  storage = my_vector.db,
  content_columns = ['ticket_description'];
  metadata_columns = ['customer_name', 'segment', 'revenue', 'is_pending_renewal'];

--find large customers who submitted ticket related to data security topics  
SELECT * FROM customers_issues
WHERE content = 'data security'
AND
  is_pending_renewal = 'true'.
  revenue > 1000000;

Use MindsDB pre-packaged data agents and connect them with your own. See how to use MindsDB via API or MCP.

CREATE AGENT my_agent
USING
    model = {
        "provider": "openai",
        "model_name" : "gpt-xx",
        "api_key": "sk-..."
    },
    data = {
         "knowledge_bases": ["mindsdb.customer_issues"],
         "tables": ["salesforce.opportunities", "postgres.sales", "mongodb.support_tickets"]
    },
    prompt_template = 'my prompt template and agent guidance';

See MindsDB’s recommended usage of agents here and how to automate workflows with jobs.

📃 Tutorials

  • Enterprise Knowledge Search (example)
  • Advanced Semantic Search (example)
  • Customer Support Automation (example1, example2)
  • Intelligent Content Discovery (example)
  • Financial Analysis Agents (example)
  • Real-time AI-powered analytics (example)
  • Conversational Data Assistants (example)
  • CRM Intelligence (example)
  • Compliance & Customer Intelligence (example)
  • Conversation Intelligence (example) Subscribe to our (blog) for more

🫴 Help and support

Stuck on a query? Found a bug? We’re here to help.

Ask a question Join our Slack Community.
Report a bug Open a GitHub Issue. Please include reproduction steps!
Get commercial support Contact the MindsDB Team for enterprise SLAs and custom solutions.

Security Note: If you find a security vulnerability, please do not open a public issue. Refer to our security policy for reporting instructions.

🤝 Contribute to MindsDB

MindsDB is open source and contributions are welcome! You can submit code changes through pull requests or by opening issues to report bugs, suggest new features, or enhancements.

Ways you can help:

  • Develop a database integration
  • Develop an app integration
  • Identify and fix bugs

How to contribute

  • Read the contribution guide to get set up.
  • Browse open issues.
  • Join the #contributors channel in Slack.
  • Explore community rewards and programs.

Our top 100 contributors

Made with contrib.rocks

📚 Resources

  • Documentation
  • Blog
  • Events
  • Community Slack
  • Brand guidelines
  • Contact form

Tech Stack

PythonDockerGoMySQLMongoDBOpenAIGPTLLM

Installation

Follow the quickstart guide to get started with MindsDB using our demo data. Retrieve and analyze data from over 200 data sources in one SQL dialect. For AI agents, this means faster response time, better accuracy, and lower token consumption. -- use SQL to aggregate pipeline data from Salesforce SELECT SUM (ExpectedRevenue) AS open_pipeline FROM salesforce . opportunities WHERE close_date >= CURDATE() -- use the same dialect to retrieve even from a non-SQL database, like MondoDB SELECT COUNT (

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ActiveLast commit today
bug_report74open issues
Submitted August 2, 2018

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