Integration (Coming Soon)

From Demo to Real Usage

FlowMCP schemas work end-to-end — from data source to AI agent response. But a standalone app is not the goal. Most people do not want to install yet another app. They want answers where they already are: in WhatsApp, Telegram, Slack, or their preferred AI assistant.

That is exactly what this phase is about: integrating our schemas and agents into existing clients — not as an island, but as a building block in a larger ecosystem.

Why CLI-first?

When we designed the first architecture in October 2025, the plan was: everything via MCP servers. The Model Context Protocol was the standard, and it worked. But since then, something has changed.

In practice, AI agents work most reliably with command-line interfaces. The reasons are technical: session initialization with MCP is error-prone, state management across protocol boundaries is complicated, and instant updates work better with CLI.

This does not mean MCP is dead. MCP remains as a second channel — for clients that prefer it. FlowMCP supports both. But for the primary integration path, we choose CLI because it works most reliably today.

OpenClaw: Why This Project?

OpenClaw is an open-source AI assistant gateway under MIT license. It has existed only since November 2025 and has already reached over 330,000 GitHub stars — more than Linux, Kubernetes, and Blender combined. It is the fastest-growing software project on GitHub.

For us, OpenClaw is the right partner for several reasons:

Cron jobs change everything. Most AI interactions are reactive: the user asks, the AI responds. With OpenClaw, we can set up proactive queries. A cron job runs automatically — for example every morning at 7:30 AM — and delivers results without the user doing anything. This is not possible with pure MCP servers or MCPUI.

Multi-channel, not multi-app. OpenClaw delivers answers via WhatsApp, Telegram, Slack, Discord, and more. The user decides where to receive the data — not us. Our schemas work identically in every channel.

No gatekeeper. On other platforms (e.g., OpenAI MCPUI), you need approval to be visible. Not with OpenClaw. Open source means: anyone can install and use our plugin — immediately, without approval.

Three Integration Levels

Integration is not a single step but a staged model. Each level brings our schemas closer to users:

Level	What happens	For whom	Status
Level 1: MCP Server	Our schemas are embedded directly as an MCP server in OpenClaw. All three usage architectures work: individual tools, sub-agents with their own intelligence, or full orchestration with a coordinator.	Developers using MCP clients	Possible
Level 2: OpenClaw Plugin	An npm package that registers each schema as a tool. Publishable on ClawHub, installable with one command. The fastest path for end users.	All OpenClaw users	Planned
Level 3: NemoClaw Policy Preset	A YAML file bundling all API endpoints of our schemas. Enterprise customers can unlock instantly — with the security policies their organization requires.	Companies and government agencies	Planned

Enterprise Security with NemoClaw

For use in companies and government agencies, open source alone is not enough. You need security policies, sandbox isolation, and controlled release processes.

NVIDIA NemoClaw is the enterprise security layer for OpenClaw (Apache 2.0, Alpha since March 2026). It offers deny-by-default network policies, sandbox isolation, and a blueprint system. A policy preset would bundle all API endpoints of our schemas — so a security officer could enable access to all open data sources with a single approval.

This matters because: public data is public, but access to it within an organization still needs to be regulated. NemoClaw makes this possible without us having to run enterprise infrastructure ourselves.

Open and Free: Local Operation

Not everyone wants or can use cloud services. That is why we are working in parallel on a fully local solution:

llama.cpp as a local LLM — no API key, no costs, full control over your own data
Running on a Raspberry Pi — a device for under 100 euros, completely independent from cloud services
Ideal for individuals who do not want to send their data to third parties, for schools working on limited budgets, and for organizations with strict data privacy requirements

Our schemas work locally just as they do in the cloud. That is the principle of open protocols: data and preparation are separate from the operating model. If you want to work locally, you can — without limitations.

Example: Multi-Source Data Integration

A practical integration combines multiple FlowMCP schemas through a single MCP server. For instance, a travel planning system could load schedule, weather, and location schemas — giving any connected AI client access to all three data sources through one endpoint.

The same pattern works for any domain: environmental monitoring, public administration, financial data, or any combination of structured data sources.

Pilot Program

In parallel with technical integration, we are looking for data partners who want to co-develop AI connections for public data. We are not looking for money or labor — we are looking for data sources and the willingness to review a finished connection.

Meet the team →

Next Steps

This phase is in preparation. Specifically, we are working on:

Validation of existing schemas with real data partners — do they work in everyday use?
Optimization based on real-world usage — better answer quality, better error handling
OpenClaw integration — the plugin that makes schemas available as tools
Local operation — testing with llama.cpp on Raspberry Pi