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AimDB

Dataflow engine for distributed systems.
MCU to Cloud. Same API.

Stars Release Crates.io Build License

Write your data pipeline once. Run it on microcontrollers, edge gateways or Kubernetes — no code changes. AimDB's portable data contracts handle serialization, transforms and schema evolution across all runtimes.

AimDB Architecture


Getting Started

See it live — explore a running sensor mesh at aimdb.dev

AimDB Live Demo

Run locally — full MCU → edge → cloud mesh in Docker:

cd examples/weather-mesh-demo
docker compose up

Then ask VS Code Copilot: "What's the current temperature from station ...?" (MCP setup required)

AimDB MCP Live Demo

Learn more:


Why AimDB?

A real-time data runtime that adapts to your infrastructure, not the other way around.

Problem AimDB Solution
Runs Where Data Starts From $2 MCUs to Kubernetes clusters. Deploy the same code anywhere, process data at the source.
Same API Everywhere Tokio + Embassy compatible, embedded-friendly, no_std-ready. One interface across all your runtimes.
Built for Continuous Change Unified data layer with schema evolution built in. Your data pipelines adapt as fast as your business.

Connectors

Protocol Crate Status Runtimes
MQTT aimdb-mqtt-connector ✅ Ready std, no_std
KNX aimdb-knx-connector ✅ Ready std, no_std
HTTP/REST 🔨 Building std
Kafka 📋 Planned std
Modbus 📋 Planned std, no_std

Platform Support

Target Runtime Features Footprint
ARM Cortex-M (STM32H5, STM32F4) Embassy no_std, async ~50KB+
ARM Cortex-M (STM32H5, STM32F4) FreeRTOS 📋 Planned
Linux Edge Devices Tokio Full std ~10MB+
Containers/K8s Tokio Full std ~10MB+

Contributing

Found a bug or want a feature? Open a GitHub issue.

Have questions or ideas? Join the discussion on GitHub Discussions.

Want to contribute? See the contributing guide. We have good first issues to get started.


License

Apache 2.0


Write once. Deploy anywhere. Pay only where it makes sense.