Introducing Crow: Open-Source AI Infrastructure for Independent Research

What is Crow

Crow is an open-source AI platform built on the Model Context Protocol (MCP). It gives your AI assistant persistent memory, a full research pipeline with APA citations, and encrypted peer-to-peer sharing. It works with Claude, ChatGPT, Gemini, Grok, Cursor, Windsurf, Cline, and Claude Code.

The problem Crow solves is context fragmentation. Every time you start a new AI conversation, you start from scratch. Your assistant does not remember what you worked on yesterday. It cannot cite the sources you found last week. And it has no way to share its findings with a collaborator. Crow fixes all of that.

Why Maestro Press Built This

Maestro Press is building a statewide education data platform covering every Texas school district and campus. That database includes TEA funding data, accountability ratings, demographic breakdowns, public information request responses, and legislative tracking. The data exists to support independent research on educational equity and school finance.

But a database is only useful if people can access it. We needed a way for researchers, parent advocates, and policy analysts to connect to the data from whatever AI tools they already use. Not everyone can write SQL queries or build their own data pipelines. Crow is the bridge.

The Vision: Open Research Infrastructure

Crow is not just a memory tool. It is research infrastructure. Here is how the pieces connect:

1. Access the database. Connect to the Maestro Press education data platform through Crow's MCP protocol. Query districts, compare funding patterns, explore demographic trends. All from your preferred AI assistant. No pipelines to build, no accounts to create.

2. Perform independent research. Crow's research pipeline handles persistent memory, auto-citations, and source management. A parent advocate in Austin or a policy researcher in El Paso can ask questions of the data, build bibliographies, and develop findings with full APA citation support.

3. Share results peer-to-peer. Crow's encrypted P2P sharing (built on Hypercore and Nostr) lets researchers send findings, memories, and data directly to each other. No central server, no accounts, no metadata leaks. The analysis stays between the people who need it.

4. Contribute back. Users can contribute their own research, cleaned datasets, and findings back to the Maestro Press database, enriching the shared resource for the entire community. The open protocol means contributions flow both ways.

This is what differentiates Crow from other AI tools. It is not a productivity app. It is infrastructure for democratizing access to education data.

How It Works

Crow runs as an MCP gateway server. Your AI client connects to it over HTTP (cloud) or stdio (local), and Crow exposes tools for memory, research, and sharing. The server handles authentication (OAuth 2.1), data persistence (SQLite/Turso), and proxies to 15+ external services (GitHub, Slack, Notion, Gmail, and more).

P2P sharing uses NaCl encryption with invite codes and safety numbers. Shares propagate through peer relays for async delivery. No central server ever sees your data.

You can deploy Crow in the cloud (one-click Render deploy), on your desktop (Claude Desktop config), or as a developer tool (Claude Code auto-detects it).

Get Involved

Crow is MIT licensed and open to contributions. The developer program covers MCP integrations, behavioral skills, core tools, and self-hosted deployment bundles.

If you work in education policy, advocacy, or research and want to explore what this could look like for your work, reach out at kevin.hopper@maestro.press.