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Kotori is a data acquisition, processing and graphing toolkit for humans

Project description

Chart recorder

Telemetry data acquisition and sensor networks for humans.

  • Status:

    CI outcome Test suite code coverage Supported Python versions Package version on PyPI Project status (alpha, beta, stable) Project license
  • Usage:

    PyPI downloads per month Docker image pulls for `kotori` (total) Docker image pulls for `kotori-standard` (total)
  • Compatibility:

    Supported Mosquitto versions Supported Grafana versions Supported InfluxDB versions Supported MongoDB versions


Kotori is a multi-channel, multi-protocol, telemetry data acquisition and graphing toolkit for time-series data processing. Free, open, simple. It supports scientific environmental monitoring projects, distributed sensor networks, and likewise scenarios.


Kotori is a data historian based on Grafana, Mosquitto, and InfluxDB, and is written in Python with Twisted. The best way to find out what you can do with Kotori, is by looking at some outlined scenarios and by reading how others are using it at the example gallery. To learn more about the technical details, have a look at the used technologies.


  • Multi-channel and multi-protocol data-acquisition and -storage. Collect and store sensor data from different kinds of devices, data sources, and protocols.

  • Built-in sensor adapters, flexible configuration capabilities, durable database storage and unattended graph visualization.

  • Based on an infrastructure toolkit assembled from different components suitable for data-acquisition, -storage, -fusion, -graphing and more.

  • Leverage the flexible data acquisition integration framework for building telemetry data acquisition and logging systems, test benches, or sensor networks for environmental monitoring systems, as well as other kinds of data-gathering and -aggregation projects.

  • It integrates well with established hardware-, software- and data acquisition workflows through flexible adapter interfaces.


Kotori can be installed in different ways. You may prefer using a Debian package, install it from the Python Package Index (PyPI), or run it within a development sandbox directly from the Git repository.

Corresponding installation instructions are bundled at


A compact example how to submit measurement data on a specific channel, using MQTT and HTTP, and export it again.

Data acquisition

First, let’s define a data acquisition channel:


and some example measurement data:

DATA='{"temperature": 42.84, "humidity": 83.1}'

Submit with MQTT:
echo "$DATA" | mosquitto_pub -h $MQTT_BROKER -t $CHANNEL/data.json -l

Submit with HTTP:

echo "$DATA" | curl --request POST --header 'Content-Type: application/json' --data @- $HTTP_URI/$CHANNEL/data

Data export

Measurement data can be exported in a variety of formats.

This is a straight-forward example for CSV data export:

http $HTTP_URI/$CHANNEL/data.csv


Thanks a stack to all the contributors who helped to co-create and conceive Kotori in one way or another. You know who you are.

Project information


Every kind of contribution, feedback, or patch, is much welcome. Create an issue or submit a patch if you think we should include a new feature, or to report or fix a bug.


In order to setup a development environment on your workstation, please head over to the development sandbox documentation. When you see the software tests succeed, you should be ready to start hacking.



The project is licensed under the terms of the GNU AGPL license, see LICENSE.

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