MindsDB in 2025: From SQL to the Universal AI Data Hub

In 2025, the challenge of connecting enterprise data to AI shifted from "if" to "how fast." For the MindsDB open-source community, this year was a journey of evolution—moving beyond predictive models to building the essential infrastructure for the Agentic Web.

We began the year with a focus on Agentic AI, believing it would fundamentally reshape how we build and interact with software. Over the last 12 months, we’ve worked alongside our contributors to make that vision accessible to everyone. By sticking to our core philosophy—Connect, Unify, Respond—we have grown into a platform where developers can confidently build self-reasoning agents on top of any data source.

Here is the scorecard for 2025 and a look at how we got here.

2025 By The Numbers

It was a record-breaking year for activity in our repository.

• 11 Major Monthly Releases (v25.1 to v25.11).

• 1,500+ Pull Requests merged to main.

• 37,400+ GitHub Stars (and counting).

• 800+ Active Contributors driving the ecosystem forward.

• 500k+ Docker pulls

• 850k+ pip installs (no mirrors)

1. CONNECT: The Universal Adapter for Agents

The Goal: Make every data source "AI-Ready" instantly.

The biggest shift in 2025 was moving beyond simple database connections to becoming the universal language for AI Agents.

• One Interface to Rule Them All: By abstracting 200+ data sources behind a universal SQL-like interface, we removed the need for agents to learn hundreds of different APIs. Whether it's a Postgres database, a Slack channel, or a Salesforce CRM, your agent logic stays simple, portable, and decoupled from the underlying data complexity.

• The Big Pivot (MCP Support): In Q1, we re-architected our API layer to support the Model Context Protocol (MCP). This effectively turned MindsDB into a "universal adapter," allowing agents (like Claude Desktop) to plug into any backend data source without custom code.

• Google MCP Toolbox (Nov): We collaborated with Google to supercharge their MCP Toolbox, bringing unstructured data support to the broader developer ecosystem.

• Deep Integrations: We didn't just add more handlers; we made them smarter. We rolled out verified, agent-ready integrations for major platforms like Snowflake, BigQuery, Salesforce, Oracle, Databricks, PostgreSQL, MySQL, SQL Server, and Gong, enhancing them with advanced metadata extraction so AI agents can autonomously understand and navigate your data schemas. (Plus, we have Jira and Elasticsearch coming soon!)

2. UNIFY: Democratizing RAG

The Goal: Treat unstructured data (PDFs, Docs) just like a SQL table.

In Spring, we solved the "Last Mile" problem of AI: retrieval. We believed you shouldn't need a PhD in vector databases to build a knowledge bot.

• Knowledge Bases (April): We officially launched Knowledge Bases, allowing users to ingest documents and query them with SQL-like syntax. This was enabled by a massive Q1 refactor of our storage engine to handle embeddings natively.

• Hybrid Search (August): We upgraded the engine to support Hybrid Search, blending keyword accuracy (BM25) with semantic understanding (Vector).

  • Result: AI answers that are both factually accurate and contextually rich.

• Virtual Metadata Tables (June): We introduced META_HANDLER_INFO and META_COLUMNS, allowing agents to introspect the database structure themselves—a critical step for self-healing agent workflows.

See how this allows you to search unstructured data with SQL precision here.

3. RESPOND: Talking to Your Data

The Goal: A conversational interface for your data.

We  wanted to empower agent builders—from expert engineers to "vibe coders"—to interact with data intuitively. Getting answers from your data is easier than ever; you can now use natural language instead of writing complex SQL yourself.

• Chat with Your Data (May): We released the Open Source Chat Interface. This major UX overhaul unified structured SQL querying and unstructured document chat into one seamless window, allowing developers to prototype agent interactions instantly.

  • Technical Win: This required merging our websocket handling and state management into a unified "Agent" backend.

• Agent Skills: Throughout Q3, we refined how Agents handle SQL generation. They can now autonomously decide when to query a database vs. when to search a Knowledge Base, effectively giving your applications a "Natural Language API" for any connected data source.

4 EXPERIENCE: A GUI Built for Builders

The Goal: A seamless, intuitive environment for AI development.

We didn't just upgrade the engine; we overhauled the dashboard. In 2025, the MindsDB GUI evolved into a full-fledged IDE for AI.

• Refined Workflow: We introduced a richer tab experience with drag-and-drop organization, session persistence, and per-tab storage, ensuring you never lose context when switching between tasks.

• Visual Overhaul: From the highly requested Dark Mode to a revamped full-width sidebar, the interface is now cleaner and easier to navigate.

• Integrated Resources: We embedded documentation directly into the GUI, so you can find answers without leaving your workspace.

• Seamless Onboarding: A new onboarding process makes getting started with MindsDB faster and more intuitive for new users.

• Model Management: Enhanced model management screens provide deeper visibility and control, making it easier to oversee your AI models as you scale. You can now easily configure and switch between top-tier providers including OpenAI, Anthropic, Google Gemini,NVIDIA NIM, Ollama, and AWS Bedrock, giving you the flexibility to choose the right model for the job.

• Authentication & Access: We added standalone local login support with a fresh brand identity and streamlined OAuth support for tools like Microsoft Teams and Google Calendar.

The Foundation: Stability, Performance & Security

While features grab headlines, reliability runs production. This year saw the most significant engineering overhauls in our history.

Runtime and Operational Stability (September)

In v25.9, we made significant changes to improve runtime stability and operational reliability.

Impact:

• Eliminated orphaned background processes in containerized environments

• Reduced memory usage across typical deployments

• Simplified setup and operational troubleshooting

Performance: Faster Inference

• Smarter SQL: We overhauled planning, pruning, and pushdown; optimized handlers; reduced cold-start penalties; and validated everything with large-scale benchmarks. The result: faster federated SQL, smarter agents, and a cleaner architecture ready for the next step.

• Faster Query Execution: Previously, MindsDB retrieved handler information directly from the underlying database – a process that added overhead to every query. We’ve now re-engineered this flow so that handler data is read as lightweight, in-code metadata. This architectural shift eliminates unnecessary I/O and streamlines execution. See results below.

• Improved Startup and Shutdown Times: Historically, each new process spent 5+ seconds of CPU time initializing the MindsDB SQL parser, because a set of complex parsing tables was being regenerated on every startup. We have mitigated this by adopting and integrating smart caching capabilities.

• Enhanced Logging and Configurability: We improved the clarity, structure, and consistency of log output, making it easier for users to diagnose issues and monitor system behavior. In addition, logging levels are now fully configurable via the MindsDB configuration file.

Security: Enterprise-Grade Trust

Trust is paramount. Throughout 2025, we hardened the platform to meet the needs of our growing enterprise user base.

• SOC2 Certification: We officially achieved SOC2 compliance, validating our commitment to the highest standards of data security and operational governance. You can verify our security posture in real-time at our new Trust Center.

• Proactive Patching: We stayed ahead of the curve, patching critical CVEs (e.g., CVE-2024-45853) and updating dependencies like Werkzeug and Ray before they became risks.

• Secret Management: We overhauled how environment secrets and SSL verifications are handled, ensuring that your connection to enterprise data sources remains hermetically sealed.

• Proactive Patching: We stayed ahead of the curve, patching critical CVEs (e.g., CVE-2024-45853) and updating dependencies like Werkzeug and Ray before they became risks.

• Secret Management: We overhauled how environment secrets and SSL verifications are handled, ensuring that your connection to enterprise data sources remains hermetically sealed.

Ready for 2026

2025 was the year MindsDB matured from a tool into a platform. Whether you’re building customer support agents, financial analysis bots, or just querying your database in natural language, the foundation is stronger than ever.

Thank you to our community, our contributors, and our users for building with us. See you in 2026!